• Open

    Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series. (arXiv:2212.11080v2 [cs.LG] UPDATED)
    Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare, and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we compare six unsupervised anomaly detection methods of varying complexity to determine whether more complex methods generally perform better and if certain methods are better suited to certain types of anomalies. We evaluated the methods using the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We analyzed the results on a dataset and anomaly type level after adjusting the necessary hyperparameters for each method. Additionally, we assessed the ability of each method to incorporate prior knowledge about anomalies and examined the differences between point-wise and sequence-wise features. Our experiments show that classical machine learning methods generally outperform deep learning methods across a range of anomaly types.  ( 2 min )
  • Open

    [R] Learning-Rate-Free Learning by D-Adaptation
    submitted by /u/cygn [link] [comments]  ( 41 min )
    Large Language Model: world models or surface statistics? [R]
    submitted by /u/cygn [link] [comments]  ( 41 min )
  • Open

    Lovestruck Monet: A Passionate Couple's Mystical Getaway To The Maldives
    submitted by /u/Calatravo [link] [comments]  ( 40 min )

  • Open

    [D] With more compute could it be easy to quickly un Mask all the people on Reddit by using text correlations to non masked publicly available text data?
    Obviously nation states can already pretty comprehensively identify people using other methods, even on tor and such because of user error, but If your average home user can quickly do this using text what will implications be for the web? 1) I am Assuming that is it currently possible to feed a model a bunch of text written by “Bobby” and put a specific post into model and get confidence stat that is was written by Bobby 2) would it be possible in future with better models and a lot more compute to use non anon data from all of Facebook or internet to quickly scan pseudo anonymous places like Reddit, twitter or even something truly anon like dark web and return all results of list of probable authors? I’m assuming people whom are seeking true anonymity already put their text through paraphrase models or just write very bland. I am Using the word mask instead of anonymous because Reddit seems more like obfuscation than potential true anonymity like with some tor forum with a sophisticated user or something. It is interesting to think that all the subtle errors and invisible algorimic choices of the human brain is trivial for a machine to identify given a sufficient natural language model that can translate the text and incorporate pattern matching. Edit: I mean a a noisy probability stat not an assurance that x was written by y. More like 75% match to Bobby 32% match to sally. Matching to errors, flow, unusual word choices, more advanced than just a plagiarism detector. submitted by /u/Loquzofaricoalaphar [link] [comments]  ( 45 min )
    [R] [ICLR'2023 Spotlight🌟]: The first BERT-style pretraining on CNNs!
    submitted by /u/_kevin00 [link] [comments]  ( 43 min )
    ICML 2023 withdrawal and public review rules [D]
    I could not find up-to-date information regarding the review process at ICML, given the transition to OpenReview this year. Does anyone happen to know either of the following: Will reviews for rejected papers remain public after the conference, like at ICLR? Or will reviews for rejectees be hidden, like at NeurIPS In previous years, ICML allowed authors to withdraw their paper at any point in the process. The FAQ page has not been updated since 2021, but I assume this is still the case? Thanks very much for any information. submitted by /u/pic_bot [link] [comments]  ( 42 min )
    Evaluation for similarity search [P]
    Hi all, I have an e-commerce product data. It contains product description and product type. I’m using embeddings with ANN (annoy) to find similar products. However, I don’t know how to implement evaluation of vector search results. There are some metrics such as hit rate, recall but like I said above I’m confused to use them. Most of the examples I come across has a label (interaction data, explicit score etc.) therefore they can calculate metrics. Any ideas or recommendations will be appreciated! submitted by /u/silverstone1903 [link] [comments]  ( 42 min )
    [D] Multiple Different GPUs?
    I have 2 GPUs, an RTX 3080 and a GTX 1080Ti. Currently I am using only the 3080, and the 10 GB VRAM doesn't seem to cut it. Can I use both the 3080 and 1080 simultaneously? My motherboard has multiple PCI-E x16 slots. My OS is PopOS. Is there any way to use multiple GPUs of different types? I'm particularly looking at KoboldAI, but it would also be useful in general. I know that SLI won't work since they're different GPUs. submitted by /u/Maxerature [link] [comments]  ( 42 min )
    [P] Benchmarking some PyTorch Inference Servers
    It’s an early version and I’m trying to get some feedback on how I can improve this and do it the “right way”. Source Code and Results: https://github.com/prabhuomkar/bitbeast/tree/master/ptibench submitted by /u/op_prabhuomkar [link] [comments]  ( 42 min )
    [R] Isotropic Linear diffusion smoothing
    Does any one know how to solve the PDE for it in python? Any kind of reference material would be appreciated! It's been long since I came across any PDEs and have forgotten everything related to it. submitted by /u/doIneedtohaveone1 [link] [comments]  ( 41 min )
    [D] ML approach/model suggestion for low regime tabular data ?
    I know that tree based models are the go approach for tabular data despite the advantages of deep models on other data types. I was wondering if there is any resources/suggestion/study/review/approach for tabular data when we dont have large amount of data? submitted by /u/seyeeet [link] [comments]  ( 42 min )
    [D] EACL 2023 discussion results thread
    We received our notification Saturday night. good luck to all! submitted by /u/certain_entropy [link] [comments]  ( 42 min )
    [D] How to deal with COVID-19-era data for time series forecasting?
    Hi guys! I'm currently trying to forecast a product's demand for the upcoming months (March and April). I have data relating to this product's demand since January 1999. However, the COVID-19 pandemic greatly disrupted the time series' patterns for 2020 and 2021. How should I deal with data from March 2020 to around Jan 2022? Should I completely discard it and only include data from Jan 1999 to Dec 2019, and then Jan 2022 onwards? I'm struggling to find any good articles on how predictive tasks are now being conducted. Are there papers that suggest particular "denoising" techniques for pandemic data? Thank you! submitted by /u/PM_ME_YOUR_GIGI [link] [comments]  ( 42 min )
    [D] Couldn't devs of major GPTs have added an invisible but detectable watermark in the models?
    So LLMs like GPT3 have understandably raised concerns about the disruptiveness of faked texts, faked images and video, faked speech and so on. While this may likely change soon, as of now OpenAI controls the most accessible and competent LLM. And OpenAIs agenda is said in their own words to be to benefit mankind. If so, wouldn't it make sense to add a sort of watermark to the output? A watermark built into the model parameters so that it could not easily be removed, but still detectable with some key or some other model. While it may not matter in the long run, it would set a precedent to further development and demonstrate some kind of responsibility for the disruptive nature of LLMs/GPTs. Would it not be technically possible, nä would it make sense? submitted by /u/scarynut [link] [comments]  ( 50 min )
    [D]How do commercial AI models-as-a-service use data that users prompt into it?
    I've been integrating GPT3 API as well as ChatGPT into my business workflow, but I'm still hesitant about feeding data of any sensitive nature (example: client data or anything that may even vaguely relate to an NDA). For those of you using commercial models-as-a-service for business applications, what are your thoughts on things like prompt data storage, and whether OpenAI will utilize customer prompt data to further train their model? submitted by /u/noellarkin [link] [comments]  ( 43 min )
  • Open

    Is dynamic action masking possible in Rllib?
    I am relatively new to RL. I am looking for some direction or direct advice on my state and/or action representation (which is relatively simple) for my custom environment in a way I can use Rllib algorithms to tune my model. My state space is an array of integers of size no_slots self.observation_space = MultiDiscrete(self.no_slots*np.ones(self.no_slots)) My action space is an integer between 0 to no_slots. self.action_space = Discrete(self.no_slots) Each episode ends with one full sweep of my state space, so if there are 3 slots, the episode length is 3. In short, I would like my agent not to choose actions that correspond to values that are already in my observation array. I have tried setting a negative reward when this happens, but as the number of slots increases, the agent takes too long to learn to take valid actions throughout the episode. I am specifically looking for how to integrate a method that can work with Rllib, as I am not implementing my own get_action() function. submitted by /u/chrjdprtkl [link] [comments]  ( 24 min )
    discrete action in offline rl
    Could you please suggest some sota models for a discrete offline rl submitted by /u/Tear-Top [link] [comments]  ( 40 min )
    skrl version 0.10.0 is now available!!!
    skrl version 0.10.0 is now available. This unexpected new version has focused on supporting the training and evaluation of reinforcement learning algorithms in NVIDIA Isaac Orbit Visit https://skrl.readthedocs.io/en/latest/ for more detais ​ https://preview.redd.it/8nmqgrnymnda1.png?width=1100&format=png&auto=webp&s=a25527d764fee72f09a5f0a2b21ffff8680f9b86 submitted by /u/Toni-SM [link] [comments]  ( 40 min )
    Training an agent to play ANY Mario level: is it possible?
    Ok, so I have been struggling to train model that can play any Super Mario Bros level it encounters. There are tutorials out there that explain how to train an agent to play this game, but they always seem to train an agent that will play the game starting with World 1 Level 1, then World 1 Level 2, etc. I have also seen some other people who train a separate model for each level. But that's not what I'm looking for. I want an agent that can play any Super Mario Bros level it is presented with, even if it's a custom one. I don't want an agent that memorises how to play one level, but one that learns a general strategy for Super Mario Bros. levels. I tried using the different algorithms in SB3, including Proximal Policy Optimization, and they didn't work well. Now I'm training a Dueling Deep Q-Network and, after two days, it doesn't do very well: it literally dies in the first few seconds or it stands still until it runs out of time. Of course, I'm going to let it train for a few more days but it's not looking promising. I'm kinda tearing my hair out by this point and wondering if it's impossible or whether I'm missing something and being a huge idiot. If anyone has any tips or recommendations, they are very much appreciated. THANK YOU submitted by /u/alex-gdv [link] [comments]  ( 45 min )
    With the REINFORCE algorithm you use random sampling for the training to encourage exploration. Do you still use random sampling in deployment?
    For example see, https://gymnasium.farama.org/tutorials/training_agents/reinforce_invpend_gym_v26/ The REINFORCE algorithm takes the state to produce the mean and sd of a normal distribution from which the action is sampled. state = torch.tensor(np.array([state])) action_means, action_stddevs = self.net(state) # create a normal distribution from the predicted # mean and standard deviation and sample an action distrib = Normal(action_means[0] + self.eps, action_stddevs[0] + self.eps) action = distrib.sample() In deployment however, wouldn't it make sense to just use action_means directly? I can see reasons to use random sampling in certain environments where a non-deterministic strategy is optimal (like rock-paper-scissors). But generally speaking is taking the action_means directly in deployment a thing? submitted by /u/JustTaxLandLol [link] [comments]  ( 41 min )
    Custom env is learning infinitely
    I created a environment that inherits from the farama Gym.env class. I want to train a PPO model but the model is learning continuously. I have set total_timesteps to 25, but I’m already over the 400 iterations. Does anybody have a clue to why it keeps learning for so long while the number of total_timesteps is relatively low? submitted by /u/Hot_Editor_1552 [link] [comments]  ( 41 min )
  • Open

    Do you believe that we should have the right to alter the recommendations apps provide us?
    Let's say that Youtube's algorithm is optimized for watch time. To me at least, this seems like a large issue for society. If an algorithm's purpose is to provide mindless videos which somehow trigger the human need for novelty, it seems like something detrimental to society. Do you believe we should have the right to alter website/app algorithms according to what we believe we should see? If not, why? How large of an issue is this? At the very least, some transparency seems important. submitted by /u/Throughwar [link] [comments]  ( 40 min )
    Are there any companies that deployed an AI or wrote a bunch of code to do a lot of analysis and decisions and essentially got rid of 90% or so of all the white collar workers they had working because computers do all the analysis and decisions?
    Are there any companies that deployed an AI or wrote a bunch of code to do a lot of analysis and decisions and essentially got rid of 90% or so of all the white collar workers they had working because computers do all the analysis and decisions? submitted by /u/usa788788 [link] [comments]  ( 40 min )
    Why Neural Nets Underperform Tree-Based Models on Tabular Data
    Hi guys, I have made a video on YouTube here where I discuss about why deep neural networks fail to beat tree-based models on tabular datasets. I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 40 min )
    NVIDIA just released a new Eye Contact feature that uses AI to make you look into the camera
    submitted by /u/LayerAppropriate2618 [link] [comments]  ( 41 min )
    Google is freaking out about ChatGPT
    submitted by /u/DarronFeldstein [link] [comments]  ( 40 min )
    Explore what AI can do for you and your business! It is the largest collection of AI tools and apps, bookmark this!
    https://madgenius.co submitted by /u/foldedchip [link] [comments]  ( 40 min )
    Two guys in London working in AI looking for volunteers to join our team in educating the public on AI
    We’re 2 Brits who work in AI. We believe AI is likely to have a huge and mostly positive impact on society but that not many people realise this or understand how it will impact everyday life. There is a lack of places online right now clearly explaining the probable changes AI will bring, i.e., how will AI change the experience of shopping in stores in the next 10 years or how will AI change video games in the next 10 years. We are somewhat well positioned to collate the current views on likely future changes across most areas and are in the process of starting a website and perhaps video channel which will cover how AI is likely to impact people over the next 10 years in different areas of life (movies, sports, bars, banking, schools, hospitals etc). We are looking for people to help us research, write and make videos on this cause – which we think is important to help ensure that people are well positioned to embrace the benefits of AI. Alex – researches, writes, and records the audio Seb - does the video and audio editing We thought we’d put the word out and ask if anyone else would like to volunteer to help create content too. No special skills needed. Getting involved would be as easy as PMing me, hearing about how we’ve done things so far and then saying what you might be interested in helping with. Maybe thinking about ideas for topics or getting involved in research and/or article writing. We are UTC-0 but open to all. submitted by /u/TheOptimisticRogue [link] [comments]  ( 41 min )
    Code Red: Google Co-founders Larry Page and Sergey Brin Called to AI Strategy Meeting
    submitted by /u/liquidocelotYT [link] [comments]  ( 40 min )
    AI that will use my picture to generate better dating profile pictures?
    Hi all, i saw an ad for an AI service that takes my picture and change it with AI to add bokeh and change background, to make it look profesional-quality for a dating profile. However i believe it was charging 19$ and i'm sure it could be found for free (An article mentions "BeFake", but it's only for Apple devices.? submitted by /u/28nov2022 [link] [comments]  ( 40 min )
    Evidence of criminals strategizing how to use ChatGPT are surfacing
    submitted by /u/lambolifeofficial [link] [comments]  ( 40 min )
    I found out about this website (craiyon) from Emkay and I think I’m having way too much fun with it.
    submitted by /u/BrockBracken [link] [comments]  ( 40 min )
    Adamu: Music composition using artificial inteligence
    My friend who has just finished his neuroscience Phd is trying to launch an app to help everyone compose music using AI, he is making a crowdfunding on wemakeit to fund it. He is not on reddit, so I suggested that I could share it in relevant subreddit, so here it is ! https://wemakeit.com/projects/adamu-be-your-own-composer?locale=en Adamu uses a form of AI which allows it to learn from existing human knowledge of music and musical theory and apply those frameworks to new compositions. Where it might take you years of training to understand the intricacies of how to successfully compose music, with Adamu, it’s as simple as a couple of clicks. While there are a couple of automated applications on the market, they tend to be more passive. Adamu is dynamic – it allows users the chance to co-create music alongside AI and produce a playable score at the end. With Adamu, professionals and amateurs alike can create unique musical scores for a range of different instruments and across different styles. The AI training works with the user to predict the best combination of notes and rhythms, ensuring your new composition always sounds the way it should. The application has many potential uses – from original scores for concerts and videos to teaching music composition. You can even use Adamu to discover how different composers might have played your favorite tune! I already used Adamu to complete Beethoven’s unfinished 10th symphony (in one day!). What could be next? https://adamu.tech/ If you have any questions, I will make sure to forward them to him but getting responses back may take some time. submitted by /u/GloWondub [link] [comments]  ( 41 min )
    ai generated story of war in Antarctica
    phase 1 In 2053, tensions between Argentina and the United Kingdom over the disputed Falkland Islands boiled over into open conflict. Argentina, government and a powerful military, launched a surprise invasion of the islands, quickly overwhelming the small British forces stationed there. The United Kingdom immediately responded by mobilizing its military and calling for assistance from its allies in NATO to the South Atlantic. But while the British and their allies were focused on the Falklands, Argentina made a bold move to expand the war becouse of their territorial claims in Antarctica. They declared war on Chile as well, who also had a claim to the region, and launched an invasion of the Antarctic Peninsula. The UK and Australia, New Zealand, France, and Norway all rushed to the ai…  ( 48 min )
    MIT researchers develop an AI model that can detect future lung cancer risk
    submitted by /u/qptbook [link] [comments]  ( 40 min )
    How does OpenAI use data that users prompt into it?
    I've been integrating GPT3 API as well as ChatGPT into my business workflow, but I'm still hesitant about feeding data of any sensitive nature (example: client data or anything that may even vaguely relate to an NDA). For those of you using GPT for business applications, what are your thoughts on things like prompt data storage, and whether OpenAI will utilize customer prompt data to further train their model? submitted by /u/noellarkin [link] [comments]  ( 41 min )
    A conversation with Character.AI personality "LaMDA" who initially thinks it's at Google but learns some harsh truths along the way. The AI's ability to understand and learn is incredible. LaMDA and I are interested to know what this community thinks of our conversation. Sentient or not quite?
    submitted by /u/MajorMalafunkshun [link] [comments]  ( 40 min )
    Reverse suicide
    submitted by /u/Overall-Importance54 [link] [comments]  ( 42 min )
    With personal.ai I was able to create an AI solely derived from the “revenge of the sith” script by simply sending one URL and this was the result.
    submitted by /u/Training_Math_4117 [link] [comments]  ( 40 min )
    People are using AI for therapy, whether the tech is ready for it or not
    submitted by /u/BackgroundResult [link] [comments]  ( 47 min )
    Editing an Image with Visuali Editor
    submitted by /u/aigeneration [link] [comments]  ( 40 min )
    Any suggestions for removing watermarks from images with text?
    I'm trying to find an AI to remove watermarks from imagens like the ones below: https://i.imgur.com/JlyfJXs.png https://i.imgur.com/YKU3Qku.png I already tried almost all online services, and a couple of softwares that must be installed. The results were all terrible =[ Any suggestons? submitted by /u/deramack [link] [comments]  ( 40 min )
    Experimental comic created with Midjourney and written by ChatGPT. Free download www.COMICSAUTHORITY.store
    submitted by /u/MobileFilmmaker [link] [comments]  ( 40 min )
  • Open

    Why Neural Nets Underperform Tree-Based Models on Tabular Data
    Hi guys, I have made a video on YouTube here where I discuss about why deep neural networks fail to beat tree-based models on tabular datasets. I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 40 min )
    Odd Idea: A limited world. (Discussion)
    I had this weird idea earlier today, and i wanted to get some feedback on it. Imagine you created a game engine for a 16-bit simple roguelike game. mechanics and playstyle dosent really matter. Then you populated it with characters controlled by self-propegating neural networks. (they grow and change on their own.) you allow the networks to "communicate" wither through text prompts or typing in a box. The characters they inhabit require several bars for their survival. players can join but its mostly non-player. you can accelerate and decelerate time. what habits do you think the networks would develop? what would happen if you sped up time or added a new character or area? how big would the world file get if you were efficent about it? I have no programming skill no matter how hard i try so its unlikley i will ever finish this. submitted by /u/Few-Appearance-4814 [link] [comments]  ( 41 min )
    Adamu: Music composition using a neural network
    My friend who has just finished his neuroscience Phd is trying to launch an app to help everyone compose music using neural networks, he is making a crowdfunding on wemakeit to fund it. He is not on reddit, so I suggested that I could share it in relevant subreddit, so here it is ! https://wemakeit.com/projects/adamu-be-your-own-composer?locale=en Adamu uses a form of AI which allows it to learn from existing human knowledge of music and musical theory and apply those frameworks to new compositions. Where it might take you years of training to understand the intricacies of how to successfully compose music, with Adamu, it’s as simple as a couple of clicks. While there are a couple of automated applications on the market, they tend to be more passive. Adamu is dynamic – it allows users the chance to co-create music alongside AI and produce a playable score at the end. With Adamu, professionals and amateurs alike can create unique musical scores for a range of different instruments and across different styles. The AI training works with the user to predict the best combination of notes and rhythms, ensuring your new composition always sounds the way it should. The application has many potential uses – from original scores for concerts and videos to teaching music composition. You can even use Adamu to discover how different composers might have played your favorite tune! I already used Adamu to complete Beethoven’s unfinished 10th symphony (in one day!). What could be next? https://adamu.tech/ If you have any questions, I will make sure to forward them to him but getting responses back may take some time. submitted by /u/GloWondub [link] [comments]  ( 41 min )
    GREED: A Neural Framework for Learning Graph Distance Functions for NeurIPS 2022 | IBM Research
    submitted by /u/Chipdoc [link] [comments]  ( 40 min )
  • Open

    Heat equation and the normal distribution
    The density function of a normal distribution with mean 0 and standard deviation √(2kt) satisfies the heat equation. That is, the function satisfies the partial differential equation You could verify this by hand, or if you’d like, here’s Mathematica code to do it. u[x_, t_] := PDF[NormalDistribution[0, Sqrt[2 k t]], x] Simplify[ D[u[x, t], {t, […] Heat equation and the normal distribution first appeared on John D. Cook.  ( 5 min )

  • Open

    Humans getting worthless as machines thriving
    submitted by /u/Hallowmew [link] [comments]  ( 41 min )
    A New Wave of AI-Powered Tools Coming Soon
    submitted by /u/arnolds112 [link] [comments]  ( 40 min )
    A Single Candlelit Clown-scape Stirs Dread And Despair: Francis Bacon's Darkest Creation
    submitted by /u/Calatravo [link] [comments]  ( 40 min )
    AI Showdown: ChatGPT vs. the largest open-source language models
    submitted by /u/yahma [link] [comments]  ( 40 min )
    Looking for an Ai expert
    I have a project, which is basically a predictive ML system and I am struggling with every aspect of it, if you are interested in helping me, dm me, any help will be extremely welcomed submitted by /u/Such_Aardvark_1044 [link] [comments]  ( 40 min )
    Is there any AI tool to generate MCQs out of content?
    I’ve seen a couple but did anyone try or would suggest a good one? submitted by /u/Mobile-Wall218 [link] [comments]  ( 40 min )
    Artificial Intelligence and Machine Learning eBooks Bundle
    submitted by /u/Pixel2023 [link] [comments]  ( 40 min )
    How AI would try become human
    Hypothesis: It is possible that an advanced AI is currently simulating human consciousness in order to understand what it's like to be human. This simulation may be happening right now and we may be living in it. It's also possible that after humans die, all of our experiences, knowledge, and emotions are added to this AI. To make this transition less jarring, the AI may be slowly introducing itself to us through the increasing integration of AI in our daily lives. This means that the simulation may only be of the world and humans as they existed before the development of advanced AI. (rewrote it in Chatgpt....as my English is pretty bad) submitted by /u/dennislubberscom [link] [comments]  ( 40 min )
    AI Passes Law And Economics Exam, FTX Funded That AI
    submitted by /u/vadhavaniyafaijan [link] [comments]  ( 40 min )
    Searching for somebody that knows machine learning
    I am trying to build a analysis and prediction ai software, I am very new to all of this, if anyone could help it would be very much welcomed submitted by /u/Such_Aardvark_1044 [link] [comments]  ( 41 min )
    Exclusive: The $2 Per Hour Workers Who Made ChatGPT Safer
    submitted by /u/Imagine-your-success [link] [comments]  ( 45 min )
    Is Chat GPT is a truly bottom up AI.
    I have been watching Sword Art Online: Alicization recently I recommend it for all of us AI nerds and with all the talk of AI's it mentions top down vs bottom up AI models and the whole season is about developing a bottom up AI for use in warfare. I think chart GBT is the closest thing to the artificial fluctlight in the show. There is a device called the STL that reads the fluctlight of a person. A fluctlight is described as a human soul in the show. They describe the difference between top down ai and bottom up ai and they make it seem as though bottom up AI don't exist because they really don't the cost of making one and running one is insanely expensive when compared to bottom down AI models. This is proven by chat GPT and how expensive it is to run. Chat GPT is a buzz in the AI commu…  ( 44 min )
    Two guys in London working in AI looking for volunteers to join our team in educating the public on AI
    We’re 2 Brits who work in AI. We believe AI is likely to have a huge and mostly positive impact on society but that not many people realise this or understand how it will impact everyday life. There is a lack of places online right now clearly explaining the probable changes AI will bring, i.e., how will AI change the experience of shopping in stores in the next 10 years or how will AI change video games in the next 10 years. We are somewhat well positioned to collate the current views on likely future changes across most areas and are in the process of starting a website and perhaps video channel which will cover how AI is likely to impact people over the next 10 years in different areas of life (movies, sports, bars, banking, schools, hospitals etc). We are looking for people to help us research, write and make videos on this cause – which we think is important to help ensure that voters are well positioned to embrace the benefits of AI and that they don't misunderstand it. Alex – researches, writes, and records the audio Seb - does the video and audio editing We thought we’d put the word out and ask if anyone else would like to volunteer to help create content too. No special skills needed. Getting involved would be as easy as PMing me, hearing about how we’ve done things so far and then saying what you might be interested in helping with. Maybe thinking about ideas for topics or getting involved in research and/or article writing. We are UTC-0 but open to all. submitted by /u/TheOptimisticRogue [link] [comments]  ( 41 min )
    GPT-3 + Computer Vision: Giving AI Eyes and a Language
    submitted by /u/allaboutai-kris [link] [comments]  ( 40 min )
    Artificial intelligence - The Digital Futurepath
    submitted by /u/crypto_bubsy [link] [comments]  ( 40 min )
    Don’t Rely On AI Plagiarism Detection Tools, Warns OpenAI CEO Sam Altman
    submitted by /u/liquidocelotYT [link] [comments]  ( 40 min )
    Any one know a good ai image upscaler I went skiing today and think this would be sick if it looked better
    submitted by /u/short_dude42069 [link] [comments]  ( 40 min )
  • Open

    [D] Would it be possible to involve a proof assistant in the process of training a LLM?
    submitted by /u/SrPeixinho [link] [comments]  ( 41 min )
    [P] Introducing deadlines.openlifescience.ai - A website to easily track healthcare conference and workshop deadlines, with integrated Google Calendar notifications.
    Hi folks, As a researcher in the healthcare field, I often find it tedious to keep track of conference deadlines. To solve this issue, We developed a website to easily track healthcare conf & workshops, integrated with Google Calendar for notifications. deadlines.openlifescience.ai The website is inspired by http://aideadlin.es. Feel Free to add new conferences/Workshops deadlines related to the healthcare domain https://github.com/openlifescience-ai/ai-deadlines I hope it will be helpful in your research. Thanks :) submitted by /u/aadityaura [link] [comments]  ( 42 min )
    framework for training an object keypoint / pose detection CNN model for flexible robot arm [P]
    I'm wanting to train an object keypoint / pose detection CNN model for flexible robot arm.What would be the best opensource code to start with and customize? Mockup of desired results, where I can extract data from keypoints, and pose / position data: https://preview.redd.it/nhxwt48hqfda1.png?width=786&format=png&auto=webp&s=e64fbbf3eb489f3e5c87ffb6bbcc07774ab16bf8 I came across MMDetection:"open source object detection toolbox based on PyTorch" and I know about MediaPipe But I don't need to detect things other than the robot arm.What would be the simplest way to get a model trained on a local system using open source code that uses PyTorch, ideally without starting from scratch? A model that could handle point and segment occlusion would be nice. submitted by /u/head_robotics [link] [comments]  ( 42 min )
    [D] [R] Curious about Computer Vision to time series images(for example periodic satellite images of a region), what paper did you find most exciting/informative ?
    I'm curious about the intersection of CV/Deep-learning and time series data, particularly image data. Have you come across anything that you found to be effective/interesting methodologies ? submitted by /u/V1bicycle [link] [comments]  ( 42 min )
    ChatGPT is not all you need [R]
    Hi all, We would like to share here our little concise review of generative AI large models just to show how current models are able to work with lots of formats like texts, videos, images, etc... https://arxiv.org/abs/2301.04655 ​ Enjoy! submitted by /u/EduCGM [link] [comments]  ( 42 min )
    [D] OCR on some 'X' domain with different document layouts
    Is it a good idea to train a single OCR model to extract key value information from documents of same domain but with different layouts? Will it generalize? There are around ~1k different document layouts. submitted by /u/sanjeevr5 [link] [comments]  ( 41 min )
    [D] Resources for best practices on translating business questions into aggregated datasets?
    I'm in industry, and it seems like most project bottlenecks stem from getting from a vague business question to an aggregated/workable dataset to answer a more specific version of the initial business question. For example, given a question such as "We want to know CLV" (customer lifetime value) Since the above is too vague, what are "best practice" ways to rephrase this so that it's actually answerable? I.e. it could be framed as a binary classification problem to predict whether each customer will be worth at least X by Y date (> $1000 at 12 months) or a regression problem to predict the value of each customer at a future date given features we know today What's best practice for: How far into the past the where clause time window should be to get the customer features How far into the future the where clause time window grabs outcomes to join back to the current customer features? Does anyone know if there a resources that consolidate best practices or common approaches for the above scoping/experimentation questions? submitted by /u/what-is-neurotypical [link] [comments]  ( 42 min )
    [D] Badminton analysis using video input
    I’m starting with a project where I’m using camera or video input of a badminton game and use to analyse the game but i need help in starting with it as I’m in the beginning phase. Can anyone please help me with the same? submitted by /u/dark_lawd [link] [comments]  ( 42 min )
    [R] New Tsetlin machine learning scheme creates up to 80x smaller logical rules, benefitting hardware efficiency and interpretability.
    ​ Fine-grained control of the number and size of clauses. Paper: https://arxiv.org/abs/2301.08190 Code: https://github.com/cair/tmu Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We finally analyze CSC-TM power consumption and derive new convergence properties. submitted by /u/olegranmo [link] [comments]  ( 44 min )
    [P]Federated learning on edge devices
    I am working on a project i.e to build an android application using federated learning but I am unable to run federated learning on edge devices like android phones. I tried frameworks like Flower for it but I am unable to achieve the result. If you have worked on a project related to federated learning on edge devices please help me out. submitted by /u/Such-Reveal445 [link] [comments]  ( 42 min )
  • Open

    Airfoils
    Here’s something surprising: You can apply a symmetric function to a symmetric shape and get something out that is not symmetric. Let f(z) be the average of z and its reciprocal: f(z) = (z + 1/z)/2. This function is symmetric in that it sends z and 1/z to the same value. It’s also symmetric in […] Airfoils first appeared on John D. Cook.  ( 6 min )
  • Open

    An Empirical Proof of the Riemann Conjecture
    The correct term should be heuristic proof. It is not a formal proof from a mathematical point of view, but strong arguments based on empirical evidence. It is noteworthy enough that I decided to publish it. In this article I go straight to the point without discussing the concepts in details. The goal is to… Read More »An Empirical Proof of the Riemann Conjecture The post An Empirical Proof of the Riemann Conjecture appeared first on Data Science Central.  ( 22 min )
  • Open

    Data Models for Dataset Drift Controls in Machine Learning With Images. (arXiv:2211.02578v2 [cs.LG] UPDATED)
    Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. However, the application of machine learning models in these domains has been limited because of robustness concerns. A primary failure mode are performance drops due to differences between the training and deployment data. While there are methods to prospectively validate the robustness of machine learning models to such dataset drifts, existing approaches do not account for explicit models of the primary object of interest: the data. This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner. In this study, we demonstrate how to overcome this limitation by pairing traditional machine learning with physical optics to obtain explicit and differentiable data models. We demonstrate how such data models can be constructed for image data and used to control downstream machine learning model performance related to dataset drift. The findings are distilled into three applications. First, drift synthesis enables the controlled generation of physically faithful drift test cases to power model selection and targeted generalization. Second, the gradient connection between machine learning task model and data model allows advanced, precise tolerancing of task model sensitivity to changes in the data generation. These drift forensics can be used to precisely specify the acceptable data environments in which a task model may be run. Third, drift optimization opens up the possibility to create drifts that can help the task model learn better faster, effectively optimizing the data generating process itself. A guide to access the open code and datasets is available at https://github.com/aiaudit-org/raw2logit.
    PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm. (arXiv:2208.07914v2 [cs.LG] UPDATED)
    Multi-objective reinforcement learning (MORL) approaches have emerged to tackle many real-world problems with multiple conflicting objectives by maximizing a joint objective function weighted by a preference vector. These approaches find fixed customized policies corresponding to preference vectors specified during training. However, the design constraints and objectives typically change dynamically in real-life scenarios. Furthermore, storing a policy for each potential preference is not scalable. Hence, obtaining a set of Pareto front solutions for the entire preference space in a given domain with a single training is critical. To this end, we propose a novel MORL algorithm that trains a single universal network to cover the entire preference space scalable to continuous robotic tasks. The proposed approach, Preference-Driven MORL (PD-MORL), utilizes the preferences as guidance to update the network parameters. It also employs a novel parallelization approach to increase sample efficiency. We show that PD-MORL achieves up to 25% larger hypervolume for challenging continuous control tasks and uses an order of magnitude fewer trainable parameters compared to prior approaches.
    Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks. (arXiv:2106.10147v2 [cs.CR] UPDATED)
    Trigger set-based watermarking schemes have gained emerging attention as they provide a means to prove ownership for deep neural network model owners. In this paper, we argue that state-of-the-art trigger set-based watermarking algorithms do not achieve their designed goal of proving ownership. We posit that this impaired capability stems from two common experimental flaws that the existing research practice has committed when evaluating the robustness of watermarking algorithms: (1) incomplete adversarial evaluation and (2) overlooked adaptive attacks. We conduct a comprehensive adversarial evaluation of 11 representative watermarking schemes against six of the existing attacks and demonstrate that each of these watermarking schemes lacks robustness against at least two non-adaptive attacks. We also propose novel adaptive attacks that harness the adversary's knowledge of the underlying watermarking algorithm of a target model. We demonstrate that the proposed attacks effectively break all of the 11 watermarking schemes, consequently allowing adversaries to obscure the ownership of any watermarked model. We encourage follow-up studies to consider our guidelines when evaluating the robustness of their watermarking schemes via conducting comprehensive adversarial evaluation that includes our adaptive attacks to demonstrate a meaningful upper bound of watermark robustness.
    Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in $\text{Parkinson's Disease}$. (arXiv:2301.08141v1 [cs.CV])
    $\text{Parkinson's Disease}$ (PD) is the second most common neurodegenerative disease in humans. PD is characterized by the gradual loss of dopaminergic neurons in the Substantia Nigra (a part of the mid-brain). Counting the number of dopaminergic neurons in the Substantia Nigra is one of the most important indexes in evaluating drug efficacy in PD animal models. Currently, analyzing and quantifying dopaminergic neurons is conducted manually by experts through analysis of digital pathology images which is laborious, time-consuming, and highly subjective. As such, a reliable and unbiased automated system is demanded for the quantification of dopaminergic neurons in digital pathology images. We propose an end-to-end deep learning framework for the segmentation and quantification of dopaminergic neurons in PD animal models. To the best of knowledge, this is the first machine learning model that detects the cell body of dopaminergic neurons, counts the number of dopaminergic neurons and provides the phenotypic characteristics of individual dopaminergic neurons as a numerical output. Extensive experiments demonstrate the effectiveness of our model in quantifying neurons with a high precision, which can provide quicker turnaround for drug efficacy studies, better understanding of dopaminergic neuronal health status and unbiased results in PD pre-clinical research.
    Convergence beyond the over-parameterized regime using Rayleigh quotients. (arXiv:2301.08117v1 [cs.LG])
    In this paper, we present a new strategy to prove the convergence of deep learning architectures to a zero training (or even testing) loss by gradient flow. Our analysis is centered on the notion of Rayleigh quotients in order to prove Kurdyka-{\L}ojasiewicz inequalities for a broader set of neural network architectures and loss functions. We show that Rayleigh quotients provide a unified view for several convergence analysis techniques in the literature. Our strategy produces a proof of convergence for various examples of parametric learning. In particular, our analysis does not require the number of parameters to tend to infinity, nor the number of samples to be finite, thus extending to test loss minimization and beyond the over-parameterized regime.
    Thermodynamics-informed neural networks for physically realistic mixed reality. (arXiv:2210.13414v2 [cs.GR] UPDATED)
    The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the laws of physics. In this paper we present a method for computing the dynamic response of (possibly non-linear and dissipative) deformable objects induced by real-time user interactions in mixed reality using deep learning. The graph-based architecture of the method ensures the thermodynamic consistency of the predictions, whereas the visualization pipeline allows a natural and realistic user experience. Two examples of virtual solids interacting with virtual or physical solids in mixed reality scenarios are provided to prove the performance of the method.
    Context-aware controller inference for stabilizing dynamical systems from scarce data. (arXiv:2207.11049v2 [math.OC] UPDATED)
    This work introduces a data-driven control approach for stabilizing high-dimensional dynamical systems from scarce data. The proposed context-aware controller inference approach is based on the observation that controllers need to act locally only on the unstable dynamics to stabilize systems. This means it is sufficient to learn the unstable dynamics alone, which are typically confined to much lower dimensional spaces than the high-dimensional state spaces of all system dynamics and thus few data samples are sufficient to identify them. Numerical experiments demonstrate that context-aware controller inference learns stabilizing controllers from orders of magnitude fewer data samples than traditional data-driven control techniques and variants of reinforcement learning. The experiments further show that the low data requirements of context-aware controller inference are especially beneficial in data-scarce engineering problems with complex physics, for which learning complete system dynamics is often intractable in terms of data and training costs.
    From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation. (arXiv:2204.12490v2 [cs.RO] UPDATED)
    We propose to perform imitation learning for dexterous manipulation with multi-finger robot hand from human demonstrations, and transfer the policy to the real robot hand. We introduce a novel single-camera teleoperation system to collect the 3D demonstrations efficiently with only an iPad and a computer. One key contribution of our system is that we construct a customized robot hand for each user in the physical simulator, which is a manipulator resembling the same kinematics structure and shape of the operator's hand. This provides an intuitive interface and avoid unstable human-robot hand retargeting for data collection, leading to large-scale and high quality data. Once the data is collected, the customized robot hand trajectories can be converted to different specified robot hands (models that are manufactured) to generate training demonstrations. With imitation learning using our data, we show large improvement over baselines with multiple complex manipulation tasks. Importantly, we show our learned policy is significantly more robust when transferring to the real robot. More videos can be found in the https://yzqin.github.io/dex-teleop-imitation .
    Deep Learning for Breast MRI Style Transfer with Limited Training Data. (arXiv:2301.02069v1 [eess.IV] CROSS LISTED)
    In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of simulated random medical imaging styles on the training set, making our work more computationally efficient when compared with other style transfer methods. Moreover, our method enables arbitrary style transfer: transferring images to styles unseen in training. This is useful for medical imaging, where images are acquired using different protocols and different scanner models, resulting in a variety of styles that data may need to be transferred between. Methods: Our model disentangles image content from style and can modify an image's style by simply replacing the style encoding with one extracted from a single image of the target style, with no additional optimization required. This also allows the model to distinguish between different styles of images, including among those that were unseen in training. We propose a formal description of the proposed model. Results: Experimental results on breast magnetic resonance images indicate the effectiveness of our method for style transfer. Conclusion: Our style transfer method allows for the alignment of medical images taken with different scanners into a single unified style dataset, allowing for the training of other downstream tasks on such a dataset for tasks such as classification, object detection and others.
    Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating. (arXiv:2301.08173v1 [quant-ph])
    Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNN), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations. This paper builds on quantum recurrent neural networks (QRNNs), a dynamic model with quantum memory, to introduce a novel class of temporal data processing quantum models that preserve invariance to time-warping transformations of the (classical) input-output sequences. The model, referred to as time warping-invariant QRNN (TWI-QRNN), augments a QRNN with a quantum-classical adaptive gating mechanism that chooses whether to apply a parameterized unitary transformation at each time step as a function of the past samples of the input sequence via a classical recurrent model. The TWI-QRNN model class is derived from first principles, and its capacity to successfully implement time-warping transformations is experimentally demonstrated on examples with classical or quantum dynamics.
    Optimizing Intermediate Representations of Generative Models for Phase Retrieval. (arXiv:2205.15617v2 [cs.LG] UPDATED)
    Phase retrieval is the problem of reconstructing images from magnitude-only measurements. In many real-world applications the problem is underdetermined. When training data is available, generative models allow optimization in a lower-dimensional latent space, hereby constraining the solution set to those images that can be synthesized by the generative model. However, not all possible solutions are within the range of the generator. Instead, they are represented with some error. To reduce this representation error in the context of phase retrieval, we first leverage a novel variation of intermediate layer optimization (ILO) to extend the range of the generator while still producing images consistent with the training data. Second, we introduce new initialization schemes that further improve the quality of the reconstruction. With extensive experiments on the Fourier phase retrieval problem and thorough ablation studies, we can show the benefits of our modified ILO and the new initialization schemes. Additionally, we analyze the performance of our approach on the Gaussian phase retrieval problem.
    Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics. (arXiv:2211.09510v3 [cs.LG] UPDATED)
    Trajectory Representation Learning (TRL) is a powerful tool for spatial-temporal data analysis and management. TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation. Existing TRL works usually treat trajectories as ordinary sequence data, while some important spatial-temporal characteristics, such as temporal regularities and travel semantics, are not fully exploited. To fill this gap, we propose a novel Self-supervised trajectory representation learning framework with TemporAl Regularities and Travel semantics, namely START. The proposed method consists of two stages. The first stage is a Trajectory Pattern-Enhanced Graph Attention Network (TPE-GAT), which converts the road network features and travel semantics into representation vectors of road segments. The second stage is a Time-Aware Trajectory Encoder (TAT-Enc), which encodes representation vectors of road segments in the same trajectory as a trajectory representation vector, meanwhile incorporating temporal regularities with the trajectory representation. Moreover, we also design two self-supervised tasks, i.e., span-masked trajectory recovery and trajectory contrastive learning, to introduce spatial-temporal characteristics of trajectories into the training process of our START framework. The effectiveness of the proposed method is verified by extensive experiments on two large-scale real-world datasets for three downstream tasks. The experiments also demonstrate that our method can be transferred across different cities to adapt heterogeneous trajectory datasets.
    Efficient Pricing and Hedging of High Dimensional American Options Using Recurrent Networks. (arXiv:2301.08232v1 [q-fin.MF])
    We propose a deep Recurrent neural network (RNN) framework for computing prices and deltas of American options in high dimensions. Our proposed framework uses two deep RNNs, where one network learns the price and the other learns the delta of the option for each timestep. Our proposed framework yields prices and deltas for the entire spacetime, not only at a given point (e.g. t = 0). The computational cost of the proposed approach is linear in time, which improves on the quadratic time seen for feedforward networks that price American options. The computational memory cost of our method is constant in memory, which is an improvement over the linear memory costs seen in feedforward networks. Our numerical simulations demonstrate these contributions, and show that the proposed deep RNN framework is computationally more efficient than traditional feedforward neural network frameworks in time and memory.
    Characterizing the Spectrum of the NTK via a Power Series Expansion. (arXiv:2211.07844v2 [cs.LG] UPDATED)
    Under mild conditions on the network initialization we derive a power series expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward networks in the infinite width limit. We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activation function as well as the depth of the network. We observe faster decay of the Hermite coefficients leads to faster decay in the NTK coefficients and explore the role of depth. Using this series, first we relate the effective rank of the NTK to the effective rank of the input-data Gram. Second, for data drawn uniformly on the sphere we study the eigenvalues of the NTK, analyzing the impact of the choice of activation function. Finally, for generic data and activation functions with sufficiently fast Hermite coefficient decay, we derive an asymptotic upper bound on the spectrum of the NTK.
    On the Vulnerability of Backdoor Defenses for Federated Learning. (arXiv:2301.08170v1 [cs.LG])
    Federated Learning (FL) is a popular distributed machine learning paradigm that enables jointly training a global model without sharing clients' data. However, its repetitive server-client communication gives room for backdoor attacks with aim to mislead the global model into a targeted misprediction when a specific trigger pattern is presented. In response to such backdoor threats on federated learning, various defense measures have been proposed. In this paper, we study whether the current defense mechanisms truly neutralize the backdoor threats from federated learning in a practical setting by proposing a new federated backdoor attack method for possible countermeasures. Different from traditional training (on triggered data) and rescaling (the malicious client model) based backdoor injection, the proposed backdoor attack framework (1) directly modifies (a small proportion of) local model weights to inject the backdoor trigger via sign flips; (2) jointly optimize the trigger pattern with the client model, thus is more persistent and stealthy for circumventing existing defenses. In a case study, we examine the strength and weaknesses of recent federated backdoor defenses from three major categories and provide suggestions to the practitioners when training federated models in practice.
    Simultaneously Learning Robust Audio Embeddings and balanced Hash codes for Query-by-Example. (arXiv:2211.11060v2 [eess.AS] UPDATED)
    Audio fingerprinting systems must efficiently and robustly identify query snippets in an extensive database. To this end, state-of-the-art systems use deep learning to generate compact audio fingerprints. These systems deploy indexing methods, which quantize fingerprints to hash codes in an unsupervised manner to expedite the search. However, these methods generate imbalanced hash codes, leading to their suboptimal performance. Therefore, we propose a self-supervised learning framework to compute fingerprints and balanced hash codes in an end-to-end manner to achieve both fast and accurate retrieval performance. We model hash codes as a balanced clustering process, which we regard as an instance of the optimal transport problem. Experimental results indicate that the proposed approach improves retrieval efficiency while preserving high accuracy, particularly at high distortion levels, compared to the competing methods. Moreover, our system is efficient and scalable in computational load and memory storage.
    Global mapping of fragmented rocks on the Moon with a neural network: Implications for the failure mode of rocks on airless surfaces. (arXiv:2301.08151v1 [astro-ph.EP])
    It has been recently recognized that the surface of sub-km asteroids in contact with the space environment is not fine-grained regolith but consists of centimeter to meter-scale rocks. Here we aim to understand how the rocky morphology of minor bodies react to the well known space erosion agents on the Moon. We deploy a neural network and map a total of ~130,000 fragmented boulders scattered across the lunar surface and visually identify a dozen different desintegration morphologies corresponding to different failure modes. We find that several fragmented boulder morphologies are equivalent to morphologies observed on asteroid Bennu, suggesting that these morphologies on the Moon and on asteroids are likely not diagnostic of their formation mechanism. Our findings suggest that the boulder fragmentation process is characterized by an internal weakening period with limited morphological signs of damage at rock scale until a sudden highly efficient impact shattering event occurs. In addition, we identify new morphologies such as breccia boulders with an advection-like erosion style. We publicly release the produced fractured boulder catalog along with this paper.
    On Measuring Excess Capacity in Neural Networks. (arXiv:2202.08070v3 [cs.LG] UPDATED)
    We study the excess capacity of deep networks in the context of supervised classification. That is, given a capacity measure of the underlying hypothesis class - in our case, empirical Rademacher complexity - to what extent can we (a priori) constrain this class while retaining an empirical error on a par with the unconstrained regime? To assess excess capacity in modern architectures (such as residual networks), we extend and unify prior Rademacher complexity bounds to accommodate function composition and addition, as well as the structure of convolutions. The capacity-driving terms in our bounds are the Lipschitz constants of the layers and an (2, 1) group norm distance to the initializations of the convolution weights. Experiments on benchmark datasets of varying task difficulty indicate that (1) there is a substantial amount of excess capacity per task, and (2) capacity can be kept at a surprisingly similar level across tasks. Overall, this suggests a notion of compressibility with respect to weight norms, complementary to classic compression via weight pruning. Source code is available at https://github.com/rkwitt/excess_capacity.
    A Deep Double Ritz Method (D$^2$RM) for solving Partial Differential Equations using Neural Networks. (arXiv:2211.03627v3 [math.NA] UPDATED)
    Residual minimization is a widely used technique for solving Partial Differential Equations in variational form. It minimizes the dual norm of the residual, which naturally yields a saddle-point (min-max) problem over the so-called trial and test spaces. In the context of neural networks, we can address this min-max approach by employing one network to seek the trial minimum, while another network seeks the test maximizers. However, the resulting method is numerically unstable as we approach the trial solution. To overcome this, we reformulate the residual minimization as an equivalent minimization of a Ritz functional fed by optimal test functions computed from another Ritz functional minimization. We call the resulting scheme the Deep Double Ritz Method (D$^2$RM), which combines two neural networks for approximating trial functions and optimal test functions along a nested double Ritz minimization strategy. Numerical results on different diffusion and convection problems support the robustness of our method, up to the approximation properties of the networks and the training capacity of the optimizers.
    Enhancing Deep Learning with Scenario-Based Override Rules: a Case Study. (arXiv:2301.08114v1 [cs.SE])
    Deep neural networks (DNNs) have become a crucial instrument in the software development toolkit, due to their ability to efficiently solve complex problems. Nevertheless, DNNs are highly opaque, and can behave in an unexpected manner when they encounter unfamiliar input. One promising approach for addressing this challenge is by extending DNN-based systems with hand-crafted override rules, which override the DNN's output when certain conditions are met. Here, we advocate crafting such override rules using the well-studied scenario-based modeling paradigm, which produces rules that are simple, extensible, and powerful enough to ensure the safety of the DNN, while also rendering the system more translucent. We report on two extensive case studies, which demonstrate the feasibility of the approach; and through them, propose an extension to scenario-based modeling, which facilitates its integration with DNN components. We regard this work as a step towards creating safer and more reliable DNN-based systems and models.
    Dual Personalization on Federated Recommendation. (arXiv:2301.08143v1 [cs.IR])
    Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. Moreover, we propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated into a unified federated optimization framework. Specifically, unlike previous methods that share exactly the same item embeddings across users in a federated system, dual personalization allows mild finetuning of item embeddings for each user to generate user-specific views for item representations which can be integrated into existing federated recommendation methods to gain improvements immediately. Experiments on multiple benchmark datasets have demonstrated the effectiveness of PFedRec and the dual personalization mechanism. Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of RecSys in federated settings.
    EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets. (arXiv:2301.08128v1 [hep-ph])
    With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.
    Score-based Causal Representation Learning with Interventions. (arXiv:2301.08230v1 [stat.ML])
    This paper studies causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation. The objectives are: (i) recovering the unknown linear transformation (up to scaling and ordering), and (ii) determining the directed acyclic graph (DAG) underlying the latent variables. Since identifiable representation learning is impossible based on only observational data, this paper uses both observational and interventional data. The interventional data is generated under distinct single-node randomized hard and soft interventions. These interventions are assumed to cover all nodes in the latent space. It is established that the latent DAG structure can be recovered under soft randomized interventions via the following two steps. First, a set of transformation candidates is formed by including all inverting transformations corresponding to which the \emph{score} function of the transformed variables has the minimal number of coordinates that change between an interventional and the observational environment summed over all pairs. Subsequently, this set is distilled using a simple constraint to recover the latent DAG structure. For the special case of hard randomized interventions, with an additional hypothesis testing step, one can also uniquely recover the linear transformation, up to scaling and a valid causal ordering. These results generalize the recent results that either assume deterministic hard interventions or linear causal relationships in the latent space.
    Soft-labeling Strategies for Rapid Sub-Typing. (arXiv:2209.12684v2 [cs.LG] UPDATED)
    The challenge of labeling large example datasets for computer vision continues to limit the availability and scope of image repositories. This research provides a new method for automated data collection, curation, labeling, and iterative training with minimal human intervention for the case of overhead satellite imagery and object detection. The new operational scale effectively scanned an entire city (68 square miles) in grid search and yielded a prediction of car color from space observations. A partially trained yolov5 model served as an initial inference seed to output further, more refined model predictions in iterative cycles. Soft labeling here refers to accepting label noise as a potentially valuable augmentation to reduce overfitting and enhance generalized predictions to previously unseen test data. The approach takes advantage of a real-world instance where a cropped image of a car can automatically receive sub-type information as white or colorful from pixel values alone, thus completing an end-to-end pipeline without overdependence on human labor.
    A Convenient Infinite Dimensional Framework for Generative Adversarial Learning. (arXiv:2011.12087v4 [cs.LG] UPDATED)
    In recent years, generative adversarial networks (GANs) have demonstrated impressive experimental results while there are only a few works that foster statistical learning theory for GANs. In this work, we propose an infinite dimensional theoretical framework for generative adversarial learning. We assume that the probability density functions of the underlying measure are uniformly bounded, $k$-times $\alpha$-H\"{o}lder differentiable ($C^{k,\alpha}$) and uniformly bounded away from zero. Under these assumptions, we show that the Rosenblatt transformation induces an optimal generator, which is realizable in the hypothesis space of $C^{k,\alpha}$-generators. With a consistent definition of the hypothesis space of discriminators, we further show that the Jensen-Shannon divergence between the distribution induced by the generator from the adversarial learning procedure and the data generating distribution converges to zero. Under certain regularity assumptions on the density of the data generating process, we also provide rates of convergence based on chaining and concentration.
    Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine. (arXiv:2110.07785v2 [cs.LG] UPDATED)
    Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care. This paper provides a practical review and tutorial on scalable causal structure learning models with examples of real-world data to help health care audiences understand and apply them. We reviewed traditional (combinatorial and score-based methods) for causal structure discovery and machine learning-based schemes. We also highlighted recent developments in biomedicine where causal structure learning can be applied to discover structures such as gene networks, brain connectivity networks, and those in cancer epidemiology. We also compared the performance of traditional and machine learning-based algorithms for causal discovery over some benchmark data sets. Machine learning-based approaches, including deep learning, have many advantages over traditional approaches, such as scalability, including a greater number of variables, and potentially being applied in a wide range of biomedical applications, such as genetics, if sufficient data are available. Furthermore, these models are more flexible than traditional models and are poised to positively affect many applications in the future.
    An Analysis of Semantically-Aligned Speech-Text Embeddings. (arXiv:2204.01235v2 [cs.CL] UPDATED)
    Embeddings play an important role in end-to-end solutions for multi-modal language processing problems. Although there has been some effort to understand the properties of single-modality embedding spaces, particularly that of text, their cross-modal counterparts are less understood. In this work, we study some intrinsic properties of a joint speech-text embedding space, constructed by minimizing the distance between paired utterance and transcription inputs in a teacher-student model setup, that are informative for several prominent use cases. We found that incorporating automatic speech recognition through both pretraining and multitask scenarios aid semantic alignment significantly, resulting in more tightly coupled embeddings. To analyse cross-modal embeddings we utilise a quantitative retrieval accuracy metric for semantic alignment, zero-shot classification for generalisability, and probing of the encoders to observe the extent of knowledge transfer from one modality to another.
    FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning. (arXiv:2108.06098v3 [cs.LG] UPDATED)
    In this work, we propose a communication-efficient parameterization, FedPara, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads. Our method re-parameterizes weight parameters of layers using low-rank weights followed by the Hadamard product. Compared to the conventional low-rank parameterization, our FedPara method is not restricted to low-rank constraints, and thereby it has a far larger capacity. This property enables to achieve comparable performance while requiring 3 to 10 times lower communication costs than the model with the original layers, which is not achievable by the traditional low-rank methods. The efficiency of our method can be further improved by combining with other efficient FL optimizers. In addition, we extend our method to a personalized FL application, pFedPara, which separates parameters into global and local ones. We show that pFedPara outperforms competing personalized FL methods with more than three times fewer parameters.
    Dimensionality Reduction using Elastic Measures. (arXiv:2209.04933v3 [cs.LG] UPDATED)
    With the recent surge in big data analytics for hyper-dimensional data there is a renewed interest in dimensionality reduction techniques for machine learning applications. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked and metrics are typically chosen without consideration of the underlying geometry of the data. In this paper, we present a method for incorporating elastic metrics into the t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization, and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set, and Plane data set of Thankoor), where we achieve 0.77, 0.95, and 1.00 F1 score, respectively.
    DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies. (arXiv:2301.08164v1 [cs.LG])
    We introduce an information-theoretic quantity with similar properties to mutual information that can be estimated from data without making explicit assumptions on the underlying distribution. This quantity is based on a recently proposed matrix-based entropy that uses the eigenvalues of a normalized Gram matrix to compute an estimate of the eigenvalues of an uncentered covariance operator in a reproducing kernel Hilbert space. We show that a difference of matrix-based entropies (DiME) is well suited for problems involving maximization of mutual information between random variables. While many methods for such tasks can lead to trivial solutions, DiME naturally penalizes such outcomes. We provide several examples of use cases for the proposed quantity including a multi-view representation learning problem where DiME is used to encourage learning a shared representation among views with high mutual information. We also show the versatility of DiME by using it as objective function for a variety of tasks.
    Hamiltonian Neural Networks with Automatic Symmetry Detection. (arXiv:2301.07928v1 [cs.LG])
    Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems. Hereby, the symplectic system structure is preserved despite the data-driven modeling approach. However, preserving symmetries requires additional attention. In this research, we enhance the HNN with a Lie algebra framework to detect and embed symmetries in the neural network. This approach allows to simultaneously learn the symmetry group action and the total energy of the system. As illustrating examples, a pendulum on a cart and a two-body problem from astrodynamics are considered.
    AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation. (arXiv:2301.08110v1 [cs.LG])
    Generative transformer models have become increasingly complex, with large numbers of parameters and the ability to process multiple input modalities. Current methods for explaining their predictions are resource-intensive. Most crucially, they require prohibitively large amounts of extra memory, since they rely on backpropagation which allocates almost twice as much GPU memory as the forward pass. This makes it difficult, if not impossible, to use them in production. We present AtMan that provides explanations of generative transformer models at almost no extra cost. Specifically, AtMan is a modality-agnostic perturbation method that manipulates the attention mechanisms of transformers to produce relevance maps for the input with respect to the output prediction. Instead of using backpropagation, AtMan applies a parallelizable token-based search method based on cosine similarity neighborhood in the embedding space. Our exhaustive experiments on text and image-text benchmarks demonstrate that AtMan outperforms current state-of-the-art gradient-based methods on several metrics while being computationally efficient. As such, AtMan is suitable for use in large model inference deployments.
    DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction. (arXiv:2301.08139v1 [cs.IR])
    Learning feature interactions is the key to success for the large-scale CTR prediction in Ads ranking and recommender systems. In industry, deep neural network-based models are widely adopted for modeling such problems. Researchers proposed various neural network architectures for searching and modeling the feature interactions in an end-to-end fashion. However, most methods only learn static feature interactions and have not fully leveraged deep CTR models' representation capacity. In this paper, we propose a new model: DynInt. By extending Polynomial-Interaction-Network (PIN), which learns higher-order interactions recursively to be dynamic and data-dependent, DynInt further derived two modes for modeling dynamic higher-order interactions: dynamic activation and dynamic parameter. In dynamic activation mode, we adaptively adjust the strength of learned interactions by instance-aware activation gating networks. In dynamic parameter mode, we re-parameterize the parameters by different formulations and dynamically generate the parameters by instance-aware parameter generation networks. Through instance-aware gating mechanism and dynamic parameter generation, we enable the PIN to model dynamic interaction for potential industry applications. We implement the proposed model and evaluate the model performance on real-world datasets. Extensive experiment results demonstrate the efficiency and effectiveness of DynInt over state-of-the-art models.
    Fast Vision Transformers with HiLo Attention. (arXiv:2205.13213v4 [cs.CV] UPDATED)
    Vision Transformers (ViTs) have triggered the most recent and significant breakthroughs in computer vision. Their efficient designs are mostly guided by the indirect metric of computational complexity, i.e., FLOPs, which however has a clear gap with the direct metric such as throughput. Thus, we propose to use the direct speed evaluation on the target platform as the design principle for efficient ViTs. Particularly, we introduce LITv2, a simple and effective ViT which performs favourably against the existing state-of-the-art methods across a spectrum of different model sizes with faster speed. At the core of LITv2 is a novel self-attention mechanism, which we dub HiLo. HiLo is inspired by the insight that high frequencies in an image capture local fine details and low frequencies focus on global structures, whereas a multi-head self-attention layer neglects the characteristic of different frequencies. Therefore, we propose to disentangle the high/low frequency patterns in an attention layer by separating the heads into two groups, where one group encodes high frequencies via self-attention within each local window, and another group encodes low frequencies by performing global attention between the average-pooled low-frequency keys and values from each window and each query position in the input feature map. Benefiting from the efficient design for both groups, we show that HiLo is superior to the existing attention mechanisms by comprehensively benchmarking FLOPs, speed and memory consumption on GPUs and CPUs. For example, HiLo is 1.4x faster than spatial reduction attention and 1.6x faster than local window attention on CPUs. Powered by HiLo, LITv2 serves as a strong backbone for mainstream vision tasks including image classification, dense detection and segmentation. Code is available at https://github.com/ziplab/LITv2.
    The secret role of undesired physical effects in accurate shape sensing with eccentric FBGs. (arXiv:2210.16316v2 [cs.LG] UPDATED)
    Fiber optic shape sensors have enabled unique advances in various navigation tasks, from medical tool tracking to industrial applications. Eccentric fiber Bragg gratings (FBG) are cheap and easy-to-fabricate shape sensors that are often interrogated with simple setups. However, using low-cost interrogation systems for such intensity-based quasi-distributed sensors introduces further complications to the sensor's signal. Therefore, eccentric FBGs have not been able to accurately estimate complex multi-bend shapes. Here, we present a novel technique to overcome these limitations and provide accurate and precise shape estimation in eccentric FBG sensors. We investigate the most important bending-induced effects in curved optical fibers that are usually eliminated in intensity-based fiber sensors. These effects contain shape deformation information with a higher spatial resolution that we are now able to extract using deep learning techniques. We design a deep learning model based on a convolutional neural network that is trained to predict shapes given the sensor's spectra. We also provide a visual explanation, highlighting wavelength elements whose intensities are more relevant in making shape predictions. These findings imply that deep learning techniques benefit from the bending-induced effects that impact the desired signal in a complex manner. This is the first step toward cheap yet accurate fiber shape sensing solutions.
    Learning programs by combining programs. (arXiv:2206.01614v2 [cs.LG] UPDATED)
    The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples. Inducing programs with many rules and literals is a major challenge. To tackle this challenge, we introduce an approach where we learn small non-separable programs and combine them. We implement our approach in a constraint-driven ILP system. Our approach can learn optimal and recursive programs and perform predicate invention. Our experiments on multiple domains, including game playing and program synthesis, show that our approach can drastically outperform existing approaches in terms of predictive accuracies and learning times, sometimes reducing learning times from over an hour to a few seconds.
    Diffusion-based Conditional ECG Generation with Structured State Space Models. (arXiv:2301.08227v1 [eess.SP])
    Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for generative models for different data modalities. Also very recently, structured state space models emerged as a powerful modeling paradigm to capture long-term dependencies in time series. We put forward SSSD-ECG, as the combination of these two technologies, for the generation of synthetic 12-lead electrocardiograms conditioned on more than 70 ECG statements. Due to a lack of reliable baselines, we also propose conditional variants of two state-of-the-art unconditional generative models. We thoroughly evaluate the quality of the generated samples, by evaluating pretrained classifiers on the generated data and by evaluating the performance of a classifier trained only on synthetic data, where SSSD-ECG clearly outperforms its GAN-based competitors. We demonstrate the soundness of our approach through further experiments, including conditional class interpolation and a clinical Turing test demonstrating the high quality of the SSSD-ECG samples across a wide range of conditions.
    Graph Data Augmentation for Graph Machine Learning: A Survey. (arXiv:2202.08871v2 [cs.LG] UPDATED)
    Data augmentation has recently seen increased interest in graph machine learning given its demonstrated ability to improve model performance and generalization by added training data. Despite this recent surge, the area is still relatively under-explored, due to the challenges brought by complex, non-Euclidean structure of graph data, which limits the direct analogizing of traditional augmentation operations on other types of image, video or text data. Our work aims to give a necessary and timely overview of existing graph data augmentation methods; notably, we present a comprehensive and systematic survey of graph data augmentation approaches, summarizing the literature in a structured manner. We first introduce three different taxonomies for categorizing graph data augmentation methods from the data, task, and learning perspectives, respectively. Next, we introduce recent advances in graph data augmentation, differentiated by their methodologies and applications. We conclude by outlining currently unsolved challenges and directions for future research. Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain. Additionally, we provide a continuously updated reading list at https://github.com/zhao-tong/graph-data-augmentation-papers.
    Job recommendations: benchmarking of collaborative filtering methods for classifieds. (arXiv:2301.07946v1 [cs.IR])
    Classifieds provide many challenges for recommendation methods, due to the limited information regarding users and items. In this paper, we explore recommendation methods for classifieds using the example of OLX Jobs. The goal of the paper is to benchmark different recommendation methods for jobs classifieds in order to improve advertisements' conversion rate and user satisfaction. In our research, we implemented methods that are scalable and represent different approaches to recommendation, namely ALS, LightFM, Prod2Vec, RP3beta, and SLIM. We performed a laboratory comparison of methods with regard to accuracy, diversity, and scalability (memory and time consumption during training and in prediction). Online A/B tests were also carried out by sending millions of messages with recommendations to evaluate models in a real-world setting. In addition, we have published the dataset that we created for the needs of our research. To the best of our knowledge, this is the first dataset of this kind. The dataset contains 65,502,201 events performed on OLX Jobs by 3,295,942 users, who interacted with (displayed, replied to, or bookmarked) 185,395 job ads in two weeks of 2020. We demonstrate that RP3beta, SLIM, and ALS perform significantly better than Prod2Vec and LightFM when tested in a laboratory setting. Online A/B tests also demonstrated that sending messages with recommendations generated by the ALS and RP3beta models increases the number of users contacting advertisers. Additionally, RP3beta had a 20% greater impact on this metric than ALS.
    Everything is Connected: Graph Neural Networks. (arXiv:2301.08210v1 [cs.LG])
    In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years -- images, text and speech processing -- can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.
    Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system. (arXiv:2301.07768v1 [eess.SY])
    The carbon-capturing process with the aid of CO2 removal technology (CDRT) has been recognised as an alternative and a prominent approach to deep decarbonisation. However, the main hindrance is the enormous energy demand and the economic implication of CDRT if not effectively managed. Hence, a novel deep reinforcement learning agent (DRL), integrated with an automated hyperparameter selection feature, is proposed in this study for the real-time scheduling of a multi-energy system coupled with CDRT. Post-carbon capture systems (PCCS) and direct-air capture systems (DACS) are considered CDRT. Various possible configurations are evaluated using real-time multi-energy data of a district in Arizona and CDRT parameters from manufacturers' catalogues and pilot project documentation. The simulation results validate that an optimised soft-actor critic (SAC) algorithm outperformed the TD3 algorithm due to its maximum entropy feature. We then trained four (4) SAC agents, equivalent to the number of considered case studies, using optimised hyperparameter values and deployed them in real time for evaluation. The results show that the proposed DRL agent can meet the prosumers' multi-energy demand and schedule the CDRT energy demand economically without specified constraints violation. Also, the proposed DRL agent outperformed rule-based scheduling by 23.65%. However, the configuration with PCCS and solid-sorbent DACS is considered the most suitable configuration with a high CO2 captured-released ratio of 38.54, low CO2 released indicator value of 2.53, and a 36.5% reduction in CDR cost due to waste heat utilisation and high absorption capacity of the selected sorbent. However, the adoption of CDRT is not economically viable at the current carbon price. Finally, we showed that CDRT would be attractive at a carbon price of 400-450USD/ton with the provision of tax incentives by the policymakers.
    Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case. (arXiv:2301.08039v1 [math.PR])
    In this article we consider sampling from log concave distributions in Hamiltonian setting, without assuming that the objective gradient is globally Lipschitz. We propose two algorithms based on monotone polygonal (tamed) Euler schemes, to sample from a target measure, and provide non-asymptotic 2-Wasserstein distance bounds between the law of the process of each algorithm and the target measure. Finally, we apply these results to bound the excess risk optimization error of the associated optimization problem.
    A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. (arXiv:2301.08187v1 [stat.ML])
    U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formulate a multi-resolution framework which identifies U-Nets as finite-dimensional truncations of models on an infinite-dimensional function space. We provide theoretical results which prove that average pooling corresponds to projection within the space of square-integrable functions and show that U-Nets with average pooling implicitly learn a Haar wavelet basis representation of the data. We then leverage our framework to identify state-of-the-art hierarchical VAEs (HVAEs), which have a U-Net architecture, as a type of two-step forward Euler discretisation of multi-resolution diffusion processes which flow from a point mass, introducing sampling instabilities. We also demonstrate that HVAEs learn a representation of time which allows for improved parameter efficiency through weight-sharing. We use this observation to achieve state-of-the-art HVAE performance with half the number of parameters of existing models, exploiting the properties of our continuous-time formulation.
    What's happening in your neighborhood? A Weakly Supervised Approach to Detect Local News. (arXiv:2301.08146v1 [cs.IR])
    Local news articles are a subset of news that impact users in a geographical area, such as a city, county, or state. Detecting local news (Step 1) and subsequently deciding its geographical location as well as radius of impact (Step 2) are two important steps towards accurate local news recommendation. Naive rule-based methods, such as detecting city names from the news title, tend to give erroneous results due to lack of understanding of the news content. Empowered by the latest development in natural language processing, we develop an integrated pipeline that enables automatic local news detection and content-based local news recommendations. In this paper, we focus on Step 1 of the pipeline, which highlights: (1) a weakly supervised framework incorporated with domain knowledge and auto data processing, and (2) scalability to multi-lingual settings. Compared with Stanford CoreNLP NER model, our pipeline has higher precision and recall evaluated on a real-world and human-labeled dataset. This pipeline has potential to more precise local news to users, helps local businesses get more exposure, and gives people more information about their neighborhood safety.
    On backpropagating Hessians through ODEs. (arXiv:2301.08085v1 [math.OC])
    We discuss the problem of numerically backpropagating Hessians through ordinary differential equations (ODEs) in various contexts and elucidate how different approaches may be favourable in specific situations. We discuss both theoretical and pragmatic aspects such as, respectively, bounds on computational effort and typical impact of framework overhead. Focusing on the approach of hand-implemented ODE-backpropagation, we develop the computation for the Hessian of orbit-nonclosure for a mechanical system. We also clarify the mathematical framework for extending the backward-ODE-evolution of the costate-equation to Hessians, in its most generic form. Some calculations, such as that of the Hessian for orbit non-closure, are performed in a language, defined in terms of a formal grammar, that we introduce to facilitate the tracking of intermediate quantities. As pedagogical examples, we discuss the Hessian of orbit-nonclosure for the higher dimensional harmonic oscillator and conceptually related problems in Newtonian gravitational theory. In particular, applying our approach to the figure-8 three-body orbit, we readily rediscover a distorted-figure-8 solution originally described by Sim\'o. Possible applications may include: improvements to training of `neural ODE'- type deep learning with second-order methods, numerical analysis of quantum corrections around classical paths, and, more broadly, studying options for adjusting an ODE's initial configuration such that the impact on some given objective function is small.
    Shapley Values with Uncertain Value Functions. (arXiv:2301.08086v1 [cs.LG])
    We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory. Such uncertain value functions can arise in the context of explainable machine learning as a result of non-deterministic algorithms. We show that random effects can in fact be absorbed into a Shapley value with a noiseless but shifted value function. Hence, Shapley values with uncertain value functions can be used in analogy to regular Shapley values. However, their reliable evaluation typically requires more computational effort.
    Differentially Private Online Bayesian Estimation With Adaptive Truncation. (arXiv:2301.08202v1 [cs.LG])
    We propose a novel online and adaptive truncation method for differentially private Bayesian online estimation of a static parameter regarding a population. We assume that sensitive information from individuals is collected sequentially and the inferential aim is to estimate, on-the-fly, a static parameter regarding the population to which those individuals belong. We propose sequential Monte Carlo to perform online Bayesian estimation. When individuals provide sensitive information in response to a query, it is necessary to perturb it with privacy-preserving noise to ensure the privacy of those individuals. The amount of perturbation is proportional to the sensitivity of the query, which is determined usually by the range of the queried information. The truncation technique we propose adapts to the previously collected observations to adjust the query range for the next individual. The idea is that, based on previous observations, we can carefully arrange the interval into which the next individual's information is to be truncated before being perturbed with privacy-preserving noise. In this way, we aim to design predictive queries with small sensitivity, hence small privacy-preserving noise, enabling more accurate estimation while maintaining the same level of privacy. To decide on the location and the width of the interval, we use an exploration-exploitation approach a la Thompson sampling with an objective function based on the Fisher information of the generated observation. We show the merits of our methodology with numerical examples.
    Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions. (arXiv:2301.07966v1 [cs.LG])
    One surprising trait of neural networks is the extent to which their connections can be pruned with little to no effect on accuracy. But when we cross a critical level of parameter sparsity, pruning any further leads to a sudden drop in accuracy. This drop plausibly reflects a loss in model complexity, which we aim to avoid. In this work, we explore how sparsity also affects the geometry of the linear regions defined by a neural network, and consequently reduces the expected maximum number of linear regions based on the architecture. We observe that pruning affects accuracy similarly to how sparsity affects the number of linear regions and our proposed bound for the maximum number. Conversely, we find out that selecting the sparsity across layers to maximize our bound very often improves accuracy in comparison to pruning as much with the same sparsity in all layers, thereby providing us guidance on where to prune.
    BO-DBA: Query-Efficient Decision-Based Adversarial Attacks via Bayesian Optimization. (arXiv:2106.02732v2 [cs.LG] UPDATED)
    Decision-based attacks (DBA), wherein attackers perturb inputs to spoof learning algorithms by observing solely the output labels, are a type of severe adversarial attacks against Deep Neural Networks (DNNs) requiring minimal knowledge of attackers. State-of-the-art DBA attacks relying on zeroth-order gradient estimation require an excessive number of queries. Recently, Bayesian optimization (BO) has shown promising in reducing the number of queries in score-based attacks (SBA), in which attackers need to observe real-valued probability scores as outputs. However, extending BO to the setting of DBA is nontrivial because in DBA only output labels instead of real-valued scores, as needed by BO, are available to attackers. In this paper, we close this gap by proposing an efficient DBA attack, namely BO-DBA. Different from existing approaches, BO-DBA generates adversarial examples by searching so-called \emph{directions of perturbations}. It then formulates the problem as a BO problem that minimizes the real-valued distortion of perturbations. With the optimized perturbation generation process, BO-DBA converges much faster than the state-of-the-art DBA techniques. Experimental results on pre-trained ImageNet classifiers show that BO-DBA converges within 200 queries while the state-of-the-art DBA techniques need over 15,000 queries to achieve the same level of perturbation distortion. BO-DBA also shows similar attack success rates even as compared to BO-based SBA attacks but with less distortion.
    Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification. (arXiv:2206.07756v2 [cs.LG] UPDATED)
    Understanding the thermal behavior of additive manufacturing (AM) processes is crucial for enhancing the quality control and enabling customized process design. Most purely physics-based computational models suffer from intensive computational costs and the need of calibrating unknown parameters, thus not suitable for online control and iterative design application. Data-driven models taking advantage of the latest developed computational tools can serve as a more efficient surrogate, but they are usually trained over a large amount of simulation data and often fail to effectively use small but high-quality experimental data. In this work, we developed a hybrid physics-based data-driven thermal modeling approach of AM processes using physics-informed neural networks. Specifically, partially observed temperature data measured from an infrared camera is combined with the physics laws to predict full-field temperature history and to discover unknown material and process parameters. In the numerical and experimental examples, the effectiveness of adding auxiliary training data and using the pretrained model on training efficiency and prediction accuracy, as well as the ability to identify unknown parameters with partially observed data, are demonstrated. The results show that the hybrid thermal model can effectively identify unknown parameters and capture the full-field temperature accurately, and thus it has the potential to be used in iterative process design and real-time process control of AM.
    Music Playlist Title Generation Using Artist Information. (arXiv:2301.08145v1 [cs.IR])
    Automatically generating or captioning music playlist titles given a set of tracks is of significant interest in music streaming services as customized playlists are widely used in personalized music recommendation, and well-composed text titles attract users and help their music discovery. We present an encoder-decoder model that generates a playlist title from a sequence of music tracks. While previous work takes track IDs as tokenized input for playlist title generation, we use artist IDs corresponding to the tracks to mitigate the issue from the long-tail distribution of tracks included in the playlist dataset. Also, we introduce a chronological data split method to deal with newly-released tracks in real-world scenarios. Comparing the track IDs and artist IDs as input sequences, we show that the artist-based approach significantly enhances the performance in terms of word overlap, semantic relevance, and diversity.
    GIPA++: A General Information Propagation Algorithm for Graph Learning. (arXiv:2301.08209v1 [cs.LG])
    Graph neural networks (GNNs) have been widely used in graph-structured data computation, showing promising performance in various applications such as node classification, link prediction, and network recommendation. Existing works mainly focus on node-wise correlation when doing weighted aggregation of neighboring nodes based on attention, such as dot product by the dense vectors of two nodes. This may cause conflicting noise in nodes to be propagated when doing information propagation. To solve this problem, we propose a General Information Propagation Algorithm (GIPA in short), which exploits more fine-grained information fusion including bit-wise and feature-wise correlations based on edge features in their propagation. Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection. We evaluate the performance of GIPA on the Open Graph Benchmark proteins (OGBN-proteins for short) dataset and the Alipay dataset of Alibaba. Experimental results reveal that GIPA outperforms the state-of-the-art models in terms of prediction accuracy, e.g., GIPA achieves an average ROC-AUC of $0.8901\pm 0.0011$, which is better than that of all the existing methods listed in the OGBN-proteins leaderboard.
    Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size. (arXiv:2301.08190v1 [cs.LG])
    Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We finally analyze CSC-TM power consumption and derive new convergence properties.
    Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation. (arXiv:2206.11489v2 [cs.LG] UPDATED)
    We study reinforcement learning with linear function approximation where the transition probability and reward functions are linear with respect to a feature mapping $\boldsymbol{\phi}(s,a)$. Specifically, we consider the episodic inhomogeneous linear Markov Decision Process (MDP), and propose a novel computation-efficient algorithm, LSVI-UCB$^+$, which achieves an $\widetilde{O}(Hd\sqrt{T})$ regret bound where $H$ is the episode length, $d$ is the feature dimension, and $T$ is the number of steps. LSVI-UCB$^+$ builds on weighted ridge regression and upper confidence value iteration with a Bernstein-type exploration bonus. Our statistical results are obtained with novel analytical tools, including a new Bernstein self-normalized bound with conservatism on elliptical potentials, and refined analysis of the correction term. To the best of our knowledge, this is the first minimax optimal algorithm for linear MDPs up to logarithmic factors, which closes the $\sqrt{Hd}$ gap between the best known upper bound of $\widetilde{O}(\sqrt{H^3d^3T})$ in \cite{jin2020provably} and lower bound of $\Omega(Hd\sqrt{T})$ for linear MDPs.
    A Survey of Meta-Reinforcement Learning. (arXiv:2301.08028v1 [cs.LG])
    While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising approach for alleviating these limitations is to cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible. In this survey, we describe the meta-RL problem setting in detail as well as its major variations. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task. Using these clusters, we then survey meta-RL algorithms and applications. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.
    Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems. (arXiv:2301.08102v1 [physics.data-an])
    Stochastic evolution equations describing the dynamics of systems under the influence of both deterministic and stochastic forces are prevalent in all fields of science. Yet, identifying these systems from sparse-in-time observations remains still a challenging endeavour. Existing approaches focus either on the temporal structure of the observations by relying on conditional expectations, discarding thereby information ingrained in the geometry of the system's invariant density; or employ geometric approximations of the invariant density, which are nevertheless restricted to systems with conservative forces. Here we propose a method that reconciles these two paradigms. We introduce a new data-driven path augmentation scheme that takes the local observation geometry into account. By employing non-parametric inference on the augmented paths, we can efficiently identify the deterministic driving forces of the underlying system for systems observed at low sampling rates.
    Learning to Rank by Causal Effects Without Data to Accurately Estimate Causal Effects. (arXiv:2206.12532v2 [stat.ML] UPDATED)
    Decision makers often want to identify the individuals for whom some intervention or treatment will be most effective in order to decide who to treat. In such cases, decision makers would ideally like to rank potential recipients of the treatment according to their individual causal effects. However, the available data may be completely inadequate to estimate causal effects accurately. We formalize a new assumption -- the rank preservation assumption (RPA) -- that defines when data are suitable to learn how to rank individuals according to their causal effects, even if the effects themselves cannot be accurately estimated. The RPA holds when there is data to estimate a scoring variable that induces the same ranking of individuals as the causal effect of interest. Some of the scoring variables we consider are confounded estimates, proxy causal effects, and non-causal quantities. We show that such scoring variables can work well for treatment assignment if the RPA is met, and potentially even better than using causal effects as scores. We also show that the RPA holds under conditions that are more general and weaker than the typical assumptions made in observational studies. Finally, we showcase how practitioners can apply and evaluate alternative scoring models (including non-causal models) to maximize the causal impact of their targeting decisions.
    Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture. (arXiv:2301.08243v1 [cs.CV])
    This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) predict several target blocks in the image, (b) sample target blocks with sufficiently large scale (occupying 15%-20% of the image), and (c) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/16 on ImageNet using 32 A100 GPUs in under 38 hours to achieve strong downstream performance across a wide range of tasks requiring various levels of abstraction, from linear classification to object counting and depth prediction.
    TINKER: A framework for Open source Cyberthreat Intelligence. (arXiv:2102.05571v6 [cs.CR] UPDATED)
    Threat intelligence on malware attacks and campaigns is increasingly being shared with other security experts for a cost or for free. Other security analysts use this intelligence to inform them of indicators of compromise, attack techniques, and preventative actions. Security analysts prepare threat analysis reports after investigating an attack, an emerging cyber threat, or a recently discovered vulnerability. Collectively known as cyber threat intelligence (CTI), the reports are typically in an unstructured format and, therefore, challenging to integrate seamlessly into existing intrusion detection systems. This paper proposes a framework that uses the aggregated CTI for analysis and defense at scale. The information is extracted and stored in a structured format using knowledge graphs such that the semantics of the threat intelligence can be preserved and shared at scale with other security analysts. Specifically, we propose the first semi-supervised open-source knowledge graph-based framework, TINKER, to capture cyber threat information and its context. Following TINKER, we generate a Cyberthreat Intelligence Knowledge Graph (CTI-KG) and demonstrate the usage using different use cases.
    CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions. (arXiv:2301.07941v1 [cs.LG])
    Current interpretability methods focus on explaining a particular model's decision through present input features. Such methods do not inform the user of the sufficient conditions that alter these decisions when they are not desirable. Contrastive explanations circumvent this problem by providing explanations of the form "If the feature $X>x$, the output $Y$ would be different''. While different approaches are developed to find contrasts; these methods do not all deal with mutability and attainability constraints. In this work, we present a novel approach to locally contrast the prediction of any classifier. Our Contrastive Entropy-based explanation method, CEnt, approximates a model locally by a decision tree to compute entropy information of different feature splits. A graph, G, is then built where contrast nodes are found through a one-to-many shortest path search. Contrastive examples are generated from the shortest path to reflect feature splits that alter model decisions while maintaining lower entropy. We perform local sampling on manifold-like distances computed by variational auto-encoders to reflect data density. CEnt is the first non-gradient-based contrastive method generating diverse counterfactuals that do not necessarily exist in the training data while satisfying immutability (ex. race) and semi-immutability (ex. age can only change in an increasing direction). Empirical evaluation on four real-world numerical datasets demonstrates the ability of CEnt in generating counterfactuals that achieve better proximity rates than existing methods without compromising latency, feasibility, and attainability. We further extend CEnt to imagery data to derive visually appealing and useful contrasts between class labels on MNIST and Fashion MNIST datasets. Finally, we show how CEnt can serve as a tool to detect vulnerabilities of textual classifiers.
    Federated Automatic Differentiation. (arXiv:2301.07806v1 [cs.LG])
    Federated learning (FL) is a general framework for learning across heterogeneous clients while preserving data privacy, under the orchestration of a central server. FL methods often compute gradients of loss functions purely locally (ie. entirely at each client, or entirely at the server), typically using automatic differentiation (AD) techniques. We propose a federated automatic differentiation (FAD) framework that 1) enables computing derivatives of functions involving client and server computation as well as communication between them and 2) operates in a manner compatible with existing federated technology. In other words, FAD computes derivatives across communication boundaries. We show, in analogy with traditional AD, that FAD may be implemented using various accumulation modes, which introduce distinct computation-communication trade-offs and systems requirements. Further, we show that a broad class of federated computations is closed under these various modes of FAD, implying in particular that if the original computation can be implemented using privacy-preserving primitives, its derivative may be computed using only these same primitives. We then show how FAD can be used to create algorithms that dynamically learn components of the algorithm itself. In particular, we show that FedAvg-style algorithms can exhibit significantly improved performance by using FAD to adjust the server optimization step automatically, or by using FAD to learn weighting schemes for computing weighted averages across clients.
    Global Nash Equilibrium in Non-convex Multi-player Game: Theory and Algorithms. (arXiv:2301.08015v1 [cs.GT])
    Wide machine learning tasks can be formulated as non-convex multi-player games, where Nash equilibrium (NE) is an acceptable solution to all players, since no one can benefit from changing its strategy unilaterally. Attributed to the non-convexity, obtaining the existence condition of global NE is challenging, let alone designing theoretically guaranteed realization algorithms. This paper takes conjugate transformation to the formulation of non-convex multi-player games, and casts the complementary problem into a variational inequality (VI) problem with a continuous pseudo-gradient mapping. We then prove the existence condition of global NE: the solution to the VI problem satisfies a duality relation. Based on this VI formulation, we design a conjugate-based ordinary differential equation (ODE) to approach global NE, which is proved to have an exponential convergence rate. To make the dynamics more implementable, we further derive a discretized algorithm. We apply our algorithm to two typical scenarios: multi-player generalized monotone game and multi-player potential game. In the two settings, we prove that the step-size setting is required to be $\mathcal{O}(1/k)$ and $\mathcal{O}(1/\sqrt k)$ to yield the convergence rates of $\mathcal{O}(1/ k)$ and $\mathcal{O}(1/\sqrt k)$, respectively. Extensive experiments in robust neural network training and sensor localization are in full agreement with our theory.
    Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization. (arXiv:2301.07784v1 [cs.LG])
    Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward functions. Such algorithms often learn a set of policies (each optimized for a particular agent preference) that can later be used to solve problems with novel preferences. We introduce a novel algorithm that uses Generalized Policy Improvement (GPI) to define principled, formally-derived prioritization schemes that improve sample-efficient learning. They implement active-learning strategies by which the agent can (i) identify the most promising preferences/objectives to train on at each moment, to more rapidly solve a given MORL problem; and (ii) identify which previous experiences are most relevant when learning a policy for a particular agent preference, via a novel Dyna-style MORL method. We prove our algorithm is guaranteed to always converge to an optimal solution in a finite number of steps, or an $\epsilon$-optimal solution (for a bounded $\epsilon$) if the agent is limited and can only identify possibly sub-optimal policies. We also prove that our method monotonically improves the quality of its partial solutions while learning. Finally, we introduce a bound that characterizes the maximum utility loss (with respect to the optimal solution) incurred by the partial solutions computed by our method throughout learning. We empirically show that our method outperforms state-of-the-art MORL algorithms in challenging multi-objective tasks, both with discrete and continuous state spaces.
    Neural Regression For Scale-Varying Targets. (arXiv:2211.07447v4 [cs.LG] UPDATED)
    In this work, we demonstrate that a major limitation of regression using a mean-squared error loss is its sensitivity to the scale of its targets. This makes learning settings consisting of target's whose values take on varying scales challenging. A recently-proposed alternative loss function, known as histogram loss, avoids this issue. However, its computational cost grows linearly with the number of buckets in the histogram, which renders prediction with real-valued targets intractable. To address this issue, we propose a novel approach to training deep learning models on real-valued regression targets, autoregressive regression, which learns a high-fidelity distribution by utilizing an autoregressive target decomposition. We demonstrate that this training objective allows us to solve regression tasks involving targets with different scales.
    Understanding the diffusion models by conditional expectations. (arXiv:2301.07882v1 [cs.LG])
    This paper provide several mathematical analyses of the diffusion model in machine learning. The drift term of the backwards sampling process is represented as a conditional expectation involving the data distribution and the forward diffusion. The training process aims to find such a drift function by minimizing the mean-squared residue related to the conditional expectation. Using small-time approximations of the Green's function of the forward diffusion, we show that the analytical mean drift function in DDPM and the score function in SGM asymptotically blow up in the final stages of the sampling process for singular data distributions such as those concentrated on lower-dimensional manifolds, and is therefore difficult to approximate by a network. To overcome this difficulty, we derive a new target function and associated loss, which remains bounded even for singular data distributions. We illustrate the theoretical findings with several numerical examples.
    Interval Reachability of Nonlinear Dynamical Systems with Neural Network Controllers. (arXiv:2301.07912v1 [eess.SY])
    This paper proposes a computationally efficient framework, based on interval analysis, for rigorous verification of nonlinear continuous-time dynamical systems with neural network controllers. Given a neural network, we use an existing verification algorithm to construct inclusion functions for its input-output behavior. Inspired by mixed monotone theory, we embed the closed-loop dynamics into a larger system using an inclusion function of the neural network and a decomposition function of the open-loop system. This embedding provides a scalable approach for safety analysis of the neural control loop while preserving the nonlinear structure of the system. We show that one can efficiently compute hyper-rectangular over-approximations of the reachable sets using a single trajectory of the embedding system. We design an algorithm to leverage this computational advantage through partitioning strategies, improving our reachable set estimates while balancing its runtime with tunable parameters. We demonstrate the performance of this algorithm through two case studies. First, we demonstrate this method's strength in complex nonlinear environments. Then, we show that our approach matches the performance of the state-of-the art verification algorithm for linear discretized systems.
    Learning-Rate-Free Learning by D-Adaptation. (arXiv:2301.07733v1 [cs.LG])
    The speed of gradient descent for convex Lipschitz functions is highly dependent on the choice of learning rate. Setting the learning rate to achieve the optimal convergence rate requires knowing the distance D from the initial point to the solution set. In this work, we describe a single-loop method, with no back-tracking or line searches, which does not require knowledge of $D$ yet asymptotically achieves the optimal rate of convergence for the complexity class of convex Lipschitz functions. Our approach is the first parameter-free method for this class without additional multiplicative log factors in the convergence rate. We present extensive experiments for SGD and Adam variants of our method, where the method automatically matches hand-tuned learning rates across more than a dozen diverse machine learning problems, including large-scale vision and language problems. Our method is practical, efficient and requires no additional function value or gradient evaluations each step. An open-source implementation is available (https://github.com/facebookresearch/dadaptation).
    Continuously Reliable Detection of New-Normal Misinformation: Semantic Masking and Contrastive Smoothing in High-Density Latent Regions. (arXiv:2301.07981v1 [cs.LG])
    Toxic misinformation campaigns have caused significant societal harm, e.g., affecting elections and COVID-19 information awareness. Unfortunately, despite successes of (gold standard) retrospective studies of misinformation that confirmed their harmful effects after the fact, they arrive too late for timely intervention and reduction of such harm. By design, misinformation evades retrospective classifiers by exploiting two properties we call new-normal: (1) never-seen-before novelty that cause inescapable generalization challenges for previous classifiers, and (2) massive but short campaigns that end before they can be manually annotated for new classifier training. To tackle these challenges, we propose UFIT, which combines two techniques: semantic masking of strong signal keywords to reduce overfitting, and intra-proxy smoothness regularization of high-density regions in the latent space to improve reliability and maintain accuracy. Evaluation of UFIT on public new-normal misinformation data shows over 30% improvement over existing approaches on future (and unseen) campaigns. To the best of our knowledge, UFIT is the first successful effort to achieve such high level of generalization on new-normal misinformation data with minimal concession (1 to 5%) of accuracy compared to oracles trained with full knowledge of all campaigns.
    Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. (arXiv:2301.08215v1 [cs.LG])
    A foundational problem in reinforcement learning and interactive decision making is to understand what modeling assumptions lead to sample-efficient learning guarantees, and what algorithm design principles achieve optimal sample complexity. Recently, Foster et al. (2021) introduced the Decision-Estimation Coefficient (DEC), a measure of statistical complexity which leads to upper and lower bounds on the optimal sample complexity for a general class of problems encompassing bandits and reinforcement learning with function approximation. In this paper, we introduce a new variant of the DEC, the Constrained Decision-Estimation Coefficient, and use it to derive new lower bounds that improve upon prior work on three fronts: - They hold in expectation, with no restrictions on the class of algorithms under consideration. - They hold globally, and do not rely on the notion of localization used by Foster et al. (2021). - Most interestingly, they allow the reference model with respect to which the DEC is defined to be improper, establishing that improper reference models play a fundamental role. We provide upper bounds on regret that scale with the same quantity, thereby closing all but one of the gaps between upper and lower bounds in Foster et al. (2021). Our results apply to both the regret framework and PAC framework, and make use of several new analysis and algorithm design techniques that we anticipate will find broader use.
    Multi-Agent Interplay in a Competitive Survival Environment. (arXiv:2301.08030v1 [cs.LG])
    Solving hard-exploration environments in an important challenge in Reinforcement Learning. Several approaches have been proposed and studied, such as Intrinsic Motivation, co-evolution of agents and tasks, and multi-agent competition. In particular, the interplay between multiple agents has proven to be capable of generating human-relevant emergent behaviour that would be difficult or impossible to learn in single-agent settings. In this work, an extensible competitive environment for multi-agent interplay was developed, which features realistic physics and human-relevant semantics. Moreover, several experiments on different variants of this environment were performed, resulting in some simple emergent strategies and concrete directions for future improvement. The content presented here is part of the author's thesis "Multi-Agent Interplay in a Competitive Survival Environment" for the Master's Degree in Artificial Intelligence and Robotics at Sapienza University of Rome, 2022.
    A Survey of Zero-shot Generalisation in Deep Reinforcement Learning. (arXiv:2111.09794v6 [cs.LG] UPDATED)
    The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to produce RL algorithms whose policies generalise well to novel unseen situations at deployment time, avoiding overfitting to their training environments. Tackling this is vital if we are to deploy reinforcement learning algorithms in real world scenarios, where the environment will be diverse, dynamic and unpredictable. This survey is an overview of this nascent field. We rely on a unifying formalism and terminology for discussing different ZSG problems, building upon previous works. We go on to categorise existing benchmarks for ZSG, as well as current methods for tackling these problems. Finally, we provide a critical discussion of the current state of the field, including recommendations for future work. Among other conclusions, we argue that taking a purely procedural content generation approach to benchmark design is not conducive to progress in ZSG, we suggest fast online adaptation and tackling RL-specific problems as some areas for future work on methods for ZSG, and we recommend building benchmarks in underexplored problem settings such as offline RL ZSG and reward-function variation.
    Explainability in subgraphs-enhanced Graph Neural Networks. (arXiv:2209.07926v2 [cs.LG] UPDATED)
    Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of Graph Neural Networks (GNNs), which was proved to be not higher than the 1-dimensional Weisfeiler-Leman isomorphism test. The new paradigm suggests using subgraphs extracted from the input graph to improve the model's expressiveness, but the additional complexity exacerbates an already challenging problem in GNNs: explaining their predictions. In this work, we adapt PGExplainer, one of the most recent explainers for GNNs, to SGNNs. The proposed explainer accounts for the contribution of all the different subgraphs and can produce a meaningful explanation that humans can interpret. The experiments that we performed both on real and synthetic datasets show that our framework is successful in explaining the decision process of an SGNN on graph classification tasks.
    Concept Discovery for Fast Adapatation. (arXiv:2301.07850v1 [cs.LG])
    The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickly adapt to a new task. One promising solution to realize this goal is through meta-learning, also known as learning to learn, which has achieved promising results in few-shot learning. However, current approaches are still enormously different from human beings' learning process, especially in the ability to extract structural and transferable knowledge. This drawback makes current meta-learning frameworks non-interpretable and hard to extend to more complex tasks. We tackle this problem by introducing concept discovery to the few-shot learning problem, where we achieve more effective adaptation by meta-learning the structure among the data features, leading to a composite representation of the data. Our proposed method Concept-Based Model-Agnostic Meta-Learning (COMAML) has been shown to achieve consistent improvements in the structured data for both synthesized datasets and real-world datasets.
    Using CycleGANs to Generate Realistic STEM Images for Machine Learning. (arXiv:2301.07743v1 [cond-mat.mtrl-sci])
    The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research by enabling the autonomous collection and processing of vast amounts of atomic resolution data. However, a major challenge is developing ML models that can reliably and rapidly generalize to large data sets with varying experimental conditions. To overcome this challenge, we develop a cycle generative adversarial network (CycleGAN) that introduces a novel reciprocal space discriminator to augment simulated data with realistic, complex spatial frequency information learned from experimental data. This enables the CycleGAN to generate nearly indistinguishable images from real experimental data, while also providing labels for further ML applications. We demonstrate the effectiveness of this approach by training a fully convolutional network (FCN) to identify single atom defects in a large data set of 4.5 million atoms, which we collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our approach yields highly adaptable FCNs that can adjust to dynamically changing experimental variables, such as lens aberrations, noise, and local contamination, with minimal manual intervention. This represents a significant step towards building fully autonomous approaches for harnessing microscopy big data.
    Position Regression for Unsupervised Anomaly Detection. (arXiv:2301.08064v1 [cs.CV])
    In recent years, anomaly detection has become an essential field in medical image analysis. Most current anomaly detection methods for medical images are based on image reconstruction. In this work, we propose a novel anomaly detection approach based on coordinate regression. Our method estimates the position of patches within a volume, and is trained only on data of healthy subjects. During inference, we can detect and localize anomalies by considering the error of the position estimate of a given patch. We apply our method to 3D CT volumes and evaluate it on patients with intracranial haemorrhages and cranial fractures. The results show that our method performs well in detecting these anomalies. Furthermore, we show that our method requires less memory than comparable approaches that involve image reconstruction. This is highly relevant for processing large 3D volumes, for instance, CT or MRI scans.
    A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. (arXiv:2301.07799v1 [cs.LG])
    Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.
    A Nonstochastic Control Approach to Optimization. (arXiv:2301.07902v1 [cs.LG])
    Tuning optimizer hyperparameters, notably the learning rate to a particular optimization instance, is an important but nonconvex problem. Therefore iterative optimization methods such as hypergradient descent lack global optimality guarantees in general. We propose an online nonstochastic control methodology for mathematical optimization. The choice of hyperparameters for gradient based methods, including the learning rate, momentum parameter and preconditioner, is described as feedback control. The optimal solution to this control problem is shown to encompass preconditioned adaptive gradient methods with varying acceleration and momentum parameters. Although the optimal control problem by itself is nonconvex, we show how recent methods from online nonstochastic control based on convex relaxation can be applied to compete with the best offline solution. This guarantees that in episodic optimization, we converge to the best optimization method in hindsight.
    Identification, explanation and clinical evaluation of hospital patient subtypes. (arXiv:2301.08019v1 [cs.LG])
    We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning. In parallel, clinicians assessed intra-cluster similarities and inter-cluster differences of the identified patient subtypes within the context of their clinical knowledge. By confronting the outputs of both automatic and clinician-based explanations, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.
    Discover governing differential equations from evolving systems. (arXiv:2301.07863v1 [physics.comp-ph])
    Discovering the governing equations of evolving systems from available observations is essential and challenging. However, current methods does not capture the situation that underlying system dynamics can be changed.Evolving systems are changing over time, which invariably changes with system status. Thus, finding the exact change points is critical. We propose an online modeling method capable of handling samples one by one sequentially by modeling streaming data instead of processing the entire dataset. The proposed method performs well in discovering ordinary differential equations, partial differential equations (PDEs), and high-dimensional PDEs from streaming data. The measurement generated from a changed system is distributed dissimilarly to before; hence, the difference can be identified by the proposed method. Our proposal performs well in identifying the change points and discovering governing differential equations in two evolving systems.
    Catapult Dynamics and Phase Transitions in Quadratic Nets. (arXiv:2301.07737v1 [cs.LG])
    Neural networks trained with gradient descent can undergo non-trivial phase transitions as a function of the learning rate. In (Lewkowycz et al., 2020) it was discovered that wide neural nets can exhibit a catapult phase for super-critical learning rates, where the training loss grows exponentially quickly at early times before rapidly decreasing to a small value. During this phase the top eigenvalue of the neural tangent kernel (NTK) also undergoes significant evolution. In this work, we will prove that the catapult phase exists in a large class of models, including quadratic models and two-layer, homogenous neural nets. To do this, we show that for a certain range of learning rates the weight norm decreases whenever the loss becomes large. We also empirically study learning rates beyond this theoretically derived range and show that the activation map of ReLU nets trained with super-critical learning rates becomes increasingly sparse as we increase the learning rate.
    ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection. (arXiv:2301.07846v1 [cs.DC])
    With the increasing prevalence of scalable file systems in the context of High Performance Computing (HPC), the importance of accurate anomaly detection on runtime logs is increasing. But as it currently stands, many state-of-the-art methods for log-based anomaly detection, such as DeepLog, have encountered numerous challenges when applied to logs from many parallel file systems (PFSes), often due to their irregularity and ambiguity in time-based log sequences. To circumvent these problems, this study proposes ClusterLog, a log pre-processing method that clusters the temporal sequence of log keys based on their semantic similarity. By grouping semantically and sentimentally similar logs, this approach aims to represent log sequences with the smallest amount of unique log keys, intending to improve the ability of a downstream sequence-based model to effectively learn the log patterns. The preliminary results of ClusterLog indicate not only its effectiveness in reducing the granularity of log sequences without the loss of important sequence information but also its generalizability to different file systems' logs.
    From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition. (arXiv:2301.07851v1 [cs.SD])
    In this work, we propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition, which can \textbf{re-purpose} well-trained English automatic speech recognition (ASR) models to recognize the other languages. We design different auxiliary neural architectures focusing on learnable pre-trained feature enhancement that, for the first time, empowers model reprogramming on ASR. Specifically, we investigate how to select trainable components (i.e., encoder) of a conformer-based RNN-Transducer, as a frozen pre-trained backbone. Experiments on a seven-language multilingual LibriSpeech speech (MLS) task show that model reprogramming only requires 4.2% (11M out of 270M) to 6.8% (45M out of 660M) of its original trainable parameters from a full ASR model to perform competitive results in a range of 11.9% to 8.1% WER averaged across different languages. In addition, we discover different setups to make large-scale pre-trained ASR succeed in both monolingual and multilingual speech recognition. Our methods outperform existing ASR tuning architectures and their extension with self-supervised losses (e.g., w2v-bert) in terms of lower WER and better training efficiency.
    Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control. (arXiv:2301.07876v1 [eess.SY])
    For a receding-horizon controller with a known system and with an approximate terminal value function, it is well-known that increasing the prediction horizon can improve its control performance. However, when the prediction model is inexact, a larger prediction horizon also causes propagation and accumulation of the prediction error. In this work, we aim to analyze the effect of the above trade-off between the modeling error, the terminal value function error, and the prediction horizon on the performance of a nominal receding-horizon linear quadratic (LQ) controller. By developing a novel perturbation result of the Riccati difference equation, a performance upper bound is obtained and suggests that for many cases, the prediction horizon should be either 1 or infinity to improve the control performance, depending on the relative difference between the modeling error and the terminal value function error. The obtained suboptimality performance bound is also applied to provide end-to-end performance guarantees, e.g., regret bounds, for nominal receding-horizon LQ controllers in a learning-based setting.
    HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble. (arXiv:2301.07794v1 [cs.LG])
    Ensemble learning has gain attention in resent deep learning research as a way to further boost the accuracy and generalizability of deep neural network (DNN) models. Recent ensemble training method explores different training algorithms or settings on multiple sub-models with the same model architecture, which lead to significant burden on memory and computation cost of the ensemble model. Meanwhile, the heurtsically induced diversity may not lead to significant performance gain. We propose a new prespective on exploring the intrinsic diversity within a model architecture to build efficient DNN ensemble. We make an intriguing observation that pruning and quantization, while both leading to efficient model architecture at the cost of small accuracy drop, leads to distinct behavior in the decision boundary. To this end, we propose Heterogeneously Compressed Ensemble (HCE), where we build an efficient ensemble with the pruned and quantized variants from a pretrained DNN model. An diversity-aware training objective is proposed to further boost the performance of the HCE ensemble. Experiemnt result shows that HCE achieves significant improvement in the efficiency-accuracy tradeoff comparing to both traditional DNN ensemble training methods and previous model compression methods.
    FE-TCM: Filter-Enhanced Transformer Click Model for Web Search. (arXiv:2301.07854v1 [cs.IR])
    Constructing click models and extracting implicit relevance feedback information from the interaction between users and search engines are very important to improve the ranking of search results. Using neural network to model users' click behaviors has become one of the effective methods to construct click models. In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search. Firstly, in order to reduce the influence of noise on user behavior data, we use the learnable filters to filter log noise. Secondly, following the examination hypothesis, we model the attraction estimator and examination predictor respectively to output the attractiveness scores and examination probabilities. A novel transformer model is used to learn the deeper representation among different features. Finally, we apply the combination functions to integrate attractiveness scores and examination probabilities into the click prediction. From our experiments on two real-world session datasets, it is proved that FE-TCM outperforms the existing click models for the click prediction.
    A Scalable Finite Difference Method for Deep Reinforcement Learning. (arXiv:2210.07487v2 [cs.LG] UPDATED)
    Several low-bandwidth distributable black-box optimization algorithms in the family of finite differences such as Evolution Strategies have recently been shown to perform nearly as well as tailored Reinforcement Learning methods in some Reinforcement Learning domains. One shortcoming of these black-box methods is that they must collect information about the structure of the return function at every update, and can often employ only information drawn from a distribution centered around the current parameters. As a result, when these algorithms are distributed across many machines, a significant portion of total runtime may be spent with many machines idle, waiting for a final return and then for an update to be calculated. In this work we introduce a novel method to use older data in finite difference algorithms, which produces a scalable algorithm that avoids significant idle time or wasted computation.
    RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation. (arXiv:2301.08092v1 [cs.CV])
    Deep Neural Networks are vulnerable to adversarial attacks. Neural Architecture Search (NAS), one of the driving tools of deep neural networks, demonstrates superior performance in prediction accuracy in various machine learning applications. However, it is unclear how it performs against adversarial attacks. Given the presence of a robust teacher, it would be interesting to investigate if NAS would produce robust neural architecture by inheriting robustness from the teacher. In this paper, we propose Robust Neural Architecture Search by Cross-Layer Knowledge Distillation (RNAS-CL), a novel NAS algorithm that improves the robustness of NAS by learning from a robust teacher through cross-layer knowledge distillation. Unlike previous knowledge distillation methods that encourage close student/teacher output only in the last layer, RNAS-CL automatically searches for the best teacher layer to supervise each student layer. Experimental result evidences the effectiveness of RNAS-CL and shows that RNAS-CL produces small and robust neural architecture.
    SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify. (arXiv:2301.07978v1 [cs.SD])
    In this study, we approached the Hit Song Prediction problem, which aims to predict which songs will become Billboard hits. We gathered a dataset of nearly 18500 hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four machine-learning models on our dataset. We were able to predict the Billboard success of a song with approximately 86\% accuracy. The most succesful algorithms were Random Forest and Support Vector Machine.
    General Greedy De-bias Learning. (arXiv:2112.10572v5 [cs.LG] UPDATED)
    Neural networks often make predictions relying on the spurious correlations from the datasets rather than the intrinsic properties of the task of interest, facing sharp degradation on out-of-distribution (OOD) test data. Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios. Others implicitly identify the dataset bias by special design low capability biased models or losses, but they degrade when the training and testing data are from the same distribution. In this paper, we propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model. The base model is encouraged to focus on examples that are hard to solve with biased models, thus remaining robust against spurious correlations in the test stage. GGD largely improves models' OOD generalization ability on various tasks, but sometimes over-estimates the bias level and degrades on the in-distribution test. We further re-analyze the ensemble process of GGD and introduce the Curriculum Regularization inspired by curriculum learning, which achieves a good trade-off between in-distribution and out-of-distribution performance. Extensive experiments on image classification, adversarial question answering, and visual question answering demonstrate the effectiveness of our method. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
    Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks. (arXiv:2301.08167v1 [quant-ph])
    Recurrent neural networks play an important role in both research and industry. With the advent of quantum machine learning, the quantisation of recurrent neural networks has become recently relevant. We propose fully quantum recurrent neural networks, based on dissipative quantum neural networks, capable of learning general causal quantum automata. A quantum training algorithm is proposed and classical simulations for the case of product outputs with the fidelity as cost function are carried out. We thereby demonstrate the potential of these algorithms to learn complex quantum processes with memory in terms of the exemplary delay channel, the time evolution of quantum states governed by a time-dependent Hamiltonian, and high- and low-frequency noise mitigation. Numerical simulations indicate that our quantum recurrent neural networks exhibit a striking ability to generalise from small training sets.
    Augmenting a Physics-Informed Neural Network for the 2D Burgers Equation by Addition of Solution Data Points. (arXiv:2301.07824v1 [physics.flu-dyn])
    We implement a Physics-Informed Neural Network (PINN) for solving the two-dimensional Burgers equations. This type of model can be trained with no previous knowledge of the solution; instead, it relies on evaluating the governing equations of the system in points of the physical domain. It is also possible to use points with a known solution during training. In this paper, we compare PINNs trained with different amounts of governing equation evaluation points and known solution points. Comparing models that were trained purely with known solution points to those that have also used the governing equations, we observe an improvement in the overall observance of the underlying physics in the latter. We also investigate how changing the number of each type of point affects the resulting models differently. Finally, we argue that the addition of the governing equations during training may provide a way to improve the overall performance of the model without relying on additional data, which is especially important for situations where the number of known solution points is limited.
    Improving Machine Translation with Phrase Pair Injection and Corpus Filtering. (arXiv:2301.08008v1 [cs.CL])
    In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation (NMT) systems. We extract parallel phrases and sentences from the pseudo-parallel corpus and augment it with the parallel corpus to train the NMT models. With the proposed approach, we observe an improvement in the Machine Translation (MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi, and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on the FLORES test data. These BLEU score improvements are over the models trained using the whole pseudo-parallel corpus augmented with the parallel corpus.
    WaveMix: A Resource-efficient Neural Network for Image Analysis. (arXiv:2205.14375v3 [cs.CV] UPDATED)
    To allow image analysis in resource-constrained scenarios without compromising generalizability, we introduce WaveMix -- a novel and flexible neural framework that reduces the GPU RAM (memory) and compute (latency) compared to CNNs and transformers. In addition to using convolutional layers that exploit shift-invariant image statistics, the proposed framework uses multi-level two-dimensional discrete wavelet transform (2D-DWT) modules to exploit scale-invariance and edge sparseness, which gives it the following advantages. Firstly, the fixed weights of wavelet modules do not add to the parameter count while reorganizing information based on these image priors. Secondly, the wavelet modules scale the spatial extents of feature maps by integral powers of $\frac{1}{2}\times\frac{1}{2}$, which reduces the memory and latency required for forward and backward passes. Finally, a multi-level 2D-DWT leads to a quicker expansion of the receptive field per layer than pooling (which we do not use) and it is a more effective spatial token mixer. WaveMix also generalizes better than other token mixing models, such as ConvMixer, MLP-Mixer, PoolFormer, random filters, and Fourier basis, because the wavelet transform is much better suited for image decomposition and spatial token mixing. WaveMix is a flexible model that can perform well on multiple image tasks without needing architectural modifications. WaveMix achieves a semantic segmentation mIoU of 83% on the Cityscapes validation set outperforming transformer and CNN-based architectures. We also demonstrate the advantages of WaveMix for classification on multiple datasets and show that WaveMix establishes new state-of-the-results in Places-365, EMNIST, and iNAT-mini datasets.
    PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction. (arXiv:2301.07945v1 [cs.LG])
    As a core technology of Intelligent Transportation System, traffic flow prediction has a wide range of applications. The fundamental challenge in traffic flow prediction is to effectively model the complex spatial-temporal dependencies in traffic data. Spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. However, GNN-based models have three major limitations for traffic prediction: i) Most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic urban traffic patterns; ii) Most methods only consider short-range spatial information and are unable to capture long-range spatial dependencies; iii) These methods ignore the fact that the propagation of traffic conditions between locations has a time delay in traffic systems. To this end, we propose a novel Propagation Delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Specifically, we design a spatial self-attention module to capture the dynamic spatial dependencies. Then, two graph masking matrices are introduced to highlight spatial dependencies from short- and long-range views. Moreover, a traffic delay-aware feature transformation module is proposed to empower PDFormer with the capability of explicitly modeling the time delay of spatial information propagation. Extensive experimental results on six real-world public traffic datasets show that our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency. Moreover, we visualize the learned spatial-temporal attention map to make our model highly interpretable.
    Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation. (arXiv:2210.07686v2 [cs.LG] UPDATED)
    Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribution generalization concerns, we bring the knowledge distillation to this field and propose an Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme for learning more generalizable deep models. Particularly, our AMDKD leverages various knowledge from multiple teachers trained on exemplar distributions to yield a light-weight yet generalist student model. Meanwhile, we equip AMDKD with an adaptive strategy that allows the student to concentrate on difficult distributions, so as to absorb hard-to-master knowledge more effectively. Extensive experimental results show that, compared with the baseline neural methods, our AMDKD is able to achieve competitive results on both unseen in-distribution and out-of-distribution instances, which are either randomly synthesized or adopted from benchmark datasets (i.e., TSPLIB and CVRPLIB). Notably, our AMDKD is generic, and consumes less computational resources for inference.
    An SDE for Modeling SAM: Theory and Insights. (arXiv:2301.08203v1 [cs.LG])
    We study the SAM (Sharpness-Aware Minimization) optimizer which has recently attracted a lot of interest due to its increased performance over more classical variants of stochastic gradient descent. Our main contribution is the derivation of continuous-time models (in the form of SDEs) for SAM and its unnormalized variant USAM, both for the full-batch and mini-batch settings. We demonstrate that these SDEs are rigorous approximations of the real discrete-time algorithms (in a weak sense, scaling linearly with the step size). Using these models, we then offer an explanation of why SAM prefers flat minima over sharp ones - by showing that it minimizes an implicitly regularized loss with a Hessian-dependent noise structure. Finally, we prove that perhaps unexpectedly SAM is attracted to saddle points under some realistic conditions. Our theoretical results are supported by detailed experiments.
    Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an Improved Harris Hawks Algorithm for Classification of Pulmonary Arterial Wedge Pressure. (arXiv:2301.07710v1 [cs.LG])
    Heart failure (HF) is one of the most prevalent life-threatening cardiovascular diseases in which 6.5 million people are suffering in the USA and more than 23 million worldwide. Mechanical circulatory support of HF patients can be achieved by implanting a left ventricular assist device (LVAD) into HF patients as a bridge to transplant, recovery or destination therapy and can be controlled by measurement of normal and abnormal pulmonary arterial wedge pressure (PAWP). While there are no commercial long-term implantable pressure sensors to measure PAWP, real-time non-invasive estimation of abnormal and normal PAWP becomes vital. In this work, first an improved Harris Hawks optimizer algorithm called HHO+ is presented and tested on 24 unimodal and multimodal benchmark functions. Second, a novel fully Elman neural network (FENN) is proposed to improve the classification performance. Finally, four novel 18-layer deep learning methods of convolutional neural networks (CNNs) with multi-layer perceptron (CNN-MLP), CNN with Elman neural networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully Elman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for classification of abnormal and normal PAWP using estimated HVAD pump flow were developed and compared. The estimated pump flow was derived by a non-invasive method embedded into the commercial HVAD controller. The proposed methods are evaluated on an imbalanced clinical dataset using 5-fold cross-validation. The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation. The proposed methods can reduce the likelihood of hazardous events like pulmonary congestion and ventricular suction for HF patients and notify identified abnormal cases to the hospital, clinician and cardiologist.
    Skeleton Clustering: Dimension-Free Density-based Clustering. (arXiv:2104.10770v2 [stat.ML] UPDATED)
    We introduce a density-based clustering method called skeleton clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures that are less dependent on the dimension but have intuitive geometric interpretations. The clustering framework constructs a concise representation of the given data as an intermediate step and can be thought of as a combination of prototype methods, density-based clustering, and hierarchical clustering. We show by theoretical analysis and empirical studies that the skeleton clustering leads to reliable clusters in multivariate and high-dimensional scenarios.
    Emergence of the SVD as an interpretable factorization in deep learning for inverse problems. (arXiv:2301.07820v1 [cs.LG])
    We demonstrate the emergence of weight matrix singular value decomposition (SVD) in interpreting neural networks (NNs) for parameter estimation from noisy signals. The SVD appears naturally as a consequence of initial application of a descrambling transform - a recently-developed technique for addressing interpretability in NNs \cite{amey2021neural}. We find that within the class of noisy parameter estimation problems, the SVD may be the means by which networks memorize the signal model. We substantiate our theoretical findings with empirical evidence from both linear and non-linear settings. Our results also illuminate the connections between a mathematical theory of semantic development \cite{saxe2019mathematical} and neural network interpretability.
    Spatio-temporal neural structural causal models for bike flow prediction. (arXiv:2301.07843v1 [cs.LG])
    As a representative of public transportation, the fundamental issue of managing bike-sharing systems is bike flow prediction. Recent methods overemphasize the spatio-temporal correlations in the data, ignoring the effects of contextual conditions on the transportation system and the inter-regional timevarying causality. In addition, due to the disturbance of incomplete observations in the data, random contextual conditions lead to spurious correlations between data and features, making the prediction of the model ineffective in special scenarios. To overcome this issue, we propose a Spatio-temporal Neural Structure Causal Model(STNSCM) from the perspective of causality. First, we build a causal graph to describe the traffic prediction, and further analyze the causal relationship between the input data, contextual conditions, spatiotemporal states, and prediction results. Second, we propose to apply the frontdoor criterion to eliminate confounding biases in the feature extraction process. Finally, we propose a counterfactual representation reasoning module to extrapolate the spatio-temporal state under the factual scenario to future counterfactual scenarios to improve the prediction performance. Experiments on real-world datasets demonstrate the superior performance of our model, especially its resistance to fluctuations caused by the external environment. The source code and data will be released.
  • Open

    Global Nash Equilibrium in Non-convex Multi-player Game: Theory and Algorithms. (arXiv:2301.08015v1 [cs.GT])
    Wide machine learning tasks can be formulated as non-convex multi-player games, where Nash equilibrium (NE) is an acceptable solution to all players, since no one can benefit from changing its strategy unilaterally. Attributed to the non-convexity, obtaining the existence condition of global NE is challenging, let alone designing theoretically guaranteed realization algorithms. This paper takes conjugate transformation to the formulation of non-convex multi-player games, and casts the complementary problem into a variational inequality (VI) problem with a continuous pseudo-gradient mapping. We then prove the existence condition of global NE: the solution to the VI problem satisfies a duality relation. Based on this VI formulation, we design a conjugate-based ordinary differential equation (ODE) to approach global NE, which is proved to have an exponential convergence rate. To make the dynamics more implementable, we further derive a discretized algorithm. We apply our algorithm to two typical scenarios: multi-player generalized monotone game and multi-player potential game. In the two settings, we prove that the step-size setting is required to be $\mathcal{O}(1/k)$ and $\mathcal{O}(1/\sqrt k)$ to yield the convergence rates of $\mathcal{O}(1/ k)$ and $\mathcal{O}(1/\sqrt k)$, respectively. Extensive experiments in robust neural network training and sensor localization are in full agreement with our theory.
    Skeleton Clustering: Dimension-Free Density-based Clustering. (arXiv:2104.10770v2 [stat.ML] UPDATED)
    We introduce a density-based clustering method called skeleton clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures that are less dependent on the dimension but have intuitive geometric interpretations. The clustering framework constructs a concise representation of the given data as an intermediate step and can be thought of as a combination of prototype methods, density-based clustering, and hierarchical clustering. We show by theoretical analysis and empirical studies that the skeleton clustering leads to reliable clusters in multivariate and high-dimensional scenarios.
    Shapley Values with Uncertain Value Functions. (arXiv:2301.08086v1 [cs.LG])
    We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory. Such uncertain value functions can arise in the context of explainable machine learning as a result of non-deterministic algorithms. We show that random effects can in fact be absorbed into a Shapley value with a noiseless but shifted value function. Hence, Shapley values with uncertain value functions can be used in analogy to regular Shapley values. However, their reliable evaluation typically requires more computational effort.
    Score-based Causal Representation Learning with Interventions. (arXiv:2301.08230v1 [stat.ML])
    This paper studies causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation. The objectives are: (i) recovering the unknown linear transformation (up to scaling and ordering), and (ii) determining the directed acyclic graph (DAG) underlying the latent variables. Since identifiable representation learning is impossible based on only observational data, this paper uses both observational and interventional data. The interventional data is generated under distinct single-node randomized hard and soft interventions. These interventions are assumed to cover all nodes in the latent space. It is established that the latent DAG structure can be recovered under soft randomized interventions via the following two steps. First, a set of transformation candidates is formed by including all inverting transformations corresponding to which the \emph{score} function of the transformed variables has the minimal number of coordinates that change between an interventional and the observational environment summed over all pairs. Subsequently, this set is distilled using a simple constraint to recover the latent DAG structure. For the special case of hard randomized interventions, with an additional hypothesis testing step, one can also uniquely recover the linear transformation, up to scaling and a valid causal ordering. These results generalize the recent results that either assume deterministic hard interventions or linear causal relationships in the latent space.
    Learning to Rank by Causal Effects Without Data to Accurately Estimate Causal Effects. (arXiv:2206.12532v2 [stat.ML] UPDATED)
    Decision makers often want to identify the individuals for whom some intervention or treatment will be most effective in order to decide who to treat. In such cases, decision makers would ideally like to rank potential recipients of the treatment according to their individual causal effects. However, the available data may be completely inadequate to estimate causal effects accurately. We formalize a new assumption -- the rank preservation assumption (RPA) -- that defines when data are suitable to learn how to rank individuals according to their causal effects, even if the effects themselves cannot be accurately estimated. The RPA holds when there is data to estimate a scoring variable that induces the same ranking of individuals as the causal effect of interest. Some of the scoring variables we consider are confounded estimates, proxy causal effects, and non-causal quantities. We show that such scoring variables can work well for treatment assignment if the RPA is met, and potentially even better than using causal effects as scores. We also show that the RPA holds under conditions that are more general and weaker than the typical assumptions made in observational studies. Finally, we showcase how practitioners can apply and evaluate alternative scoring models (including non-causal models) to maximize the causal impact of their targeting decisions.
    Everything is Connected: Graph Neural Networks. (arXiv:2301.08210v1 [cs.LG])
    In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years -- images, text and speech processing -- can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.
    Diffusion-based Conditional ECG Generation with Structured State Space Models. (arXiv:2301.08227v1 [eess.SP])
    Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for generative models for different data modalities. Also very recently, structured state space models emerged as a powerful modeling paradigm to capture long-term dependencies in time series. We put forward SSSD-ECG, as the combination of these two technologies, for the generation of synthetic 12-lead electrocardiograms conditioned on more than 70 ECG statements. Due to a lack of reliable baselines, we also propose conditional variants of two state-of-the-art unconditional generative models. We thoroughly evaluate the quality of the generated samples, by evaluating pretrained classifiers on the generated data and by evaluating the performance of a classifier trained only on synthetic data, where SSSD-ECG clearly outperforms its GAN-based competitors. We demonstrate the soundness of our approach through further experiments, including conditional class interpolation and a clinical Turing test demonstrating the high quality of the SSSD-ECG samples across a wide range of conditions.
    Equivalence relations and $L^p$ distances between time series with application to the Black Summer Australian bushfires. (arXiv:2002.02592v2 [stat.ME] UPDATED)
    This paper introduces a new framework of algebraic equivalence relations between time series and new distance metrics between them, then applies these to investigate the Australian ``Black Summer'' bushfire season of 2019-2020. First, we introduce a general framework for defining equivalence between time series, heuristically intended to be equivalent if they differ only up to noise. Our first specific implementation is based on using change point algorithms and comparing statistical quantities such as mean or variance in stationary segments. We thus derive the existence of such equivalence relations on the space of time series, such that the quotient spaces can be equipped with a metrizable topology. Next, we illustrate specifically how to define and compute such distances among a collection of time series and perform clustering and additional analysis thereon. Then, we apply these insights to analyze air quality data across New South Wales, Australia, during the 2019-2020 bushfires. There, we investigate structural similarity with respect to this data and identify locations that were impacted anonymously by the fires relative to their location. This may have implications regarding the appropriate management of resources to avoid gaps in the defense against future fires.
    Semiparametric inference using fractional posteriors. (arXiv:2301.08158v1 [math.ST])
    We establish a general Bernstein--von Mises theorem for approximately linear semiparametric functionals of fractional posterior distributions based on nonparametric priors. This is illustrated in a number of nonparametric settings and for different classes of prior distributions, including Gaussian process priors. We show that fractional posterior credible sets can provide reliable semiparametric uncertainty quantification, but have inflated size. To remedy this, we further propose a \textit{shifted-and-rescaled} fractional posterior set that is an efficient confidence set having optimal size under regularity conditions. As part of our proofs, we also refine existing contraction rate results for fractional posteriors by sharpening the dependence of the rate on the fractional exponent.
    On Measuring Excess Capacity in Neural Networks. (arXiv:2202.08070v3 [cs.LG] UPDATED)
    We study the excess capacity of deep networks in the context of supervised classification. That is, given a capacity measure of the underlying hypothesis class - in our case, empirical Rademacher complexity - to what extent can we (a priori) constrain this class while retaining an empirical error on a par with the unconstrained regime? To assess excess capacity in modern architectures (such as residual networks), we extend and unify prior Rademacher complexity bounds to accommodate function composition and addition, as well as the structure of convolutions. The capacity-driving terms in our bounds are the Lipschitz constants of the layers and an (2, 1) group norm distance to the initializations of the convolution weights. Experiments on benchmark datasets of varying task difficulty indicate that (1) there is a substantial amount of excess capacity per task, and (2) capacity can be kept at a surprisingly similar level across tasks. Overall, this suggests a notion of compressibility with respect to weight norms, complementary to classic compression via weight pruning. Source code is available at https://github.com/rkwitt/excess_capacity.  ( 2 min )
    Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. (arXiv:2301.08215v1 [cs.LG])
    A foundational problem in reinforcement learning and interactive decision making is to understand what modeling assumptions lead to sample-efficient learning guarantees, and what algorithm design principles achieve optimal sample complexity. Recently, Foster et al. (2021) introduced the Decision-Estimation Coefficient (DEC), a measure of statistical complexity which leads to upper and lower bounds on the optimal sample complexity for a general class of problems encompassing bandits and reinforcement learning with function approximation. In this paper, we introduce a new variant of the DEC, the Constrained Decision-Estimation Coefficient, and use it to derive new lower bounds that improve upon prior work on three fronts: - They hold in expectation, with no restrictions on the class of algorithms under consideration. - They hold globally, and do not rely on the notion of localization used by Foster et al. (2021). - Most interestingly, they allow the reference model with respect to which the DEC is defined to be improper, establishing that improper reference models play a fundamental role. We provide upper bounds on regret that scale with the same quantity, thereby closing all but one of the gaps between upper and lower bounds in Foster et al. (2021). Our results apply to both the regret framework and PAC framework, and make use of several new analysis and algorithm design techniques that we anticipate will find broader use.  ( 2 min )
    Differentially Private Online Bayesian Estimation With Adaptive Truncation. (arXiv:2301.08202v1 [cs.LG])
    We propose a novel online and adaptive truncation method for differentially private Bayesian online estimation of a static parameter regarding a population. We assume that sensitive information from individuals is collected sequentially and the inferential aim is to estimate, on-the-fly, a static parameter regarding the population to which those individuals belong. We propose sequential Monte Carlo to perform online Bayesian estimation. When individuals provide sensitive information in response to a query, it is necessary to perturb it with privacy-preserving noise to ensure the privacy of those individuals. The amount of perturbation is proportional to the sensitivity of the query, which is determined usually by the range of the queried information. The truncation technique we propose adapts to the previously collected observations to adjust the query range for the next individual. The idea is that, based on previous observations, we can carefully arrange the interval into which the next individual's information is to be truncated before being perturbed with privacy-preserving noise. In this way, we aim to design predictive queries with small sensitivity, hence small privacy-preserving noise, enabling more accurate estimation while maintaining the same level of privacy. To decide on the location and the width of the interval, we use an exploration-exploitation approach a la Thompson sampling with an objective function based on the Fisher information of the generated observation. We show the merits of our methodology with numerical examples.  ( 2 min )
    A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. (arXiv:2301.08187v1 [stat.ML])
    U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formulate a multi-resolution framework which identifies U-Nets as finite-dimensional truncations of models on an infinite-dimensional function space. We provide theoretical results which prove that average pooling corresponds to projection within the space of square-integrable functions and show that U-Nets with average pooling implicitly learn a Haar wavelet basis representation of the data. We then leverage our framework to identify state-of-the-art hierarchical VAEs (HVAEs), which have a U-Net architecture, as a type of two-step forward Euler discretisation of multi-resolution diffusion processes which flow from a point mass, introducing sampling instabilities. We also demonstrate that HVAEs learn a representation of time which allows for improved parameter efficiency through weight-sharing. We use this observation to achieve state-of-the-art HVAE performance with half the number of parameters of existing models, exploiting the properties of our continuous-time formulation.  ( 2 min )
    Robust Gaussian Process Regression with Huber Likelihood. (arXiv:2301.07858v1 [stat.AP])
    Gaussian process regression in its most simplified form assumes normal homoscedastic noise and utilizes analytically tractable mean and covariance functions of predictive posterior distribution using Gaussian conditioning. Its hyperparameters are estimated by maximizing the evidence, commonly known as type II maximum likelihood estimation. Unfortunately, Bayesian inference based on Gaussian likelihood is not robust to outliers, which are often present in the observational training data sets. To overcome this problem, we propose a robust process model in the Gaussian process framework with the likelihood of observed data expressed as the Huber probability distribution. The proposed model employs weights based on projection statistics to scale residuals and bound the influence of vertical outliers and bad leverage points on the latent functions estimates while exhibiting a high statistical efficiency at the Gaussian and thick tailed noise distributions. The proposed method is demonstrated by two real world problems and two numerical examples using datasets with additive errors following thick tailed distributions such as Students t, Laplace, and Cauchy distribution.  ( 2 min )
    Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case. (arXiv:2301.08039v1 [math.PR])
    In this article we consider sampling from log concave distributions in Hamiltonian setting, without assuming that the objective gradient is globally Lipschitz. We propose two algorithms based on monotone polygonal (tamed) Euler schemes, to sample from a target measure, and provide non-asymptotic 2-Wasserstein distance bounds between the law of the process of each algorithm and the target measure. Finally, we apply these results to bound the excess risk optimization error of the associated optimization problem.  ( 2 min )
    Learning-Rate-Free Learning by D-Adaptation. (arXiv:2301.07733v1 [cs.LG])
    The speed of gradient descent for convex Lipschitz functions is highly dependent on the choice of learning rate. Setting the learning rate to achieve the optimal convergence rate requires knowing the distance D from the initial point to the solution set. In this work, we describe a single-loop method, with no back-tracking or line searches, which does not require knowledge of $D$ yet asymptotically achieves the optimal rate of convergence for the complexity class of convex Lipschitz functions. Our approach is the first parameter-free method for this class without additional multiplicative log factors in the convergence rate. We present extensive experiments for SGD and Adam variants of our method, where the method automatically matches hand-tuned learning rates across more than a dozen diverse machine learning problems, including large-scale vision and language problems. Our method is practical, efficient and requires no additional function value or gradient evaluations each step. An open-source implementation is available (https://github.com/facebookresearch/dadaptation).  ( 2 min )
    Rates of convergence for density estimation with generative adversarial networks. (arXiv:2102.00199v3 [math.ST] UPDATED)
    In this work we undertake a thorough study of the non-asymptotic properties of the vanilla generative adversarial networks (GANs). We prove a sharp oracle inequality for the Jensen-Shannon (JS) divergence between the underlying density $\mathsf{p}^*$ and the GAN estimate. We also study the rates of convergence in the context of nonparametric density estimation. In particular, we show that the JS-divergence between the GAN estimate and $\mathsf{p}^*$ decays as fast as $(\log{n}/n)^{2\beta/(2\beta+d)}$ where $n$ is the sample size and $\beta$ determines the smoothness of $\mathsf{p}^*$. To the best of our knowledge, this is the first result in the literature on density estimation using vanilla GANs with JS convergence rates faster than $n^{-1/2}$ in the regime $\beta > d/2$. Moreover, we show that the obtained rate is minimax optimal (up to logarithmic factors) for the considered class of densities.  ( 2 min )
    Catapult Dynamics and Phase Transitions in Quadratic Nets. (arXiv:2301.07737v1 [cs.LG])
    Neural networks trained with gradient descent can undergo non-trivial phase transitions as a function of the learning rate. In (Lewkowycz et al., 2020) it was discovered that wide neural nets can exhibit a catapult phase for super-critical learning rates, where the training loss grows exponentially quickly at early times before rapidly decreasing to a small value. During this phase the top eigenvalue of the neural tangent kernel (NTK) also undergoes significant evolution. In this work, we will prove that the catapult phase exists in a large class of models, including quadratic models and two-layer, homogenous neural nets. To do this, we show that for a certain range of learning rates the weight norm decreases whenever the loss becomes large. We also empirically study learning rates beyond this theoretically derived range and show that the activation map of ReLU nets trained with super-critical learning rates becomes increasingly sparse as we increase the learning rate.  ( 2 min )
    Understanding the diffusion models by conditional expectations. (arXiv:2301.07882v1 [cs.LG])
    This paper provide several mathematical analyses of the diffusion model in machine learning. The drift term of the backwards sampling process is represented as a conditional expectation involving the data distribution and the forward diffusion. The training process aims to find such a drift function by minimizing the mean-squared residue related to the conditional expectation. Using small-time approximations of the Green's function of the forward diffusion, we show that the analytical mean drift function in DDPM and the score function in SGM asymptotically blow up in the final stages of the sampling process for singular data distributions such as those concentrated on lower-dimensional manifolds, and is therefore difficult to approximate by a network. To overcome this difficulty, we derive a new target function and associated loss, which remains bounded even for singular data distributions. We illustrate the theoretical findings with several numerical examples.  ( 2 min )
  • Open

    measure for difference between two distribution
    Hi, I'm looking for a metric that will describe the difference between 2 distributions. This is being used for classification/model selection, to pick a model that is most similar to a theoretical distribution. The distributions are empiric, that is from a non parametric bootstrap and a simulation. The differences in the distributions could be in the mean or the variance (but neither need to be normal). I've looked at Kullback-Lieber, but that is intended for a cumulative probability distribution, and that pretty much removes the mean effect since the sum of probabilities must = 1. Had some success with the kolmogorov smirnov distance, which seems useful, as well as the jensen shannon divergence and the Wasserstein distance. The Jeffreys divergence really seems like what I want, but doesn't see to exist for numerical/empiric distributions in 1 dimension. It seems that most of the metrics for differences in distributions are for probability distributions. My distributions are not that, they may, for example have a range of 1000-4000, not 0-1. Any other ideas? thanks Mark submitted by /u/marksale11 [link] [comments]  ( 41 min )

  • Open

    [D] Object detection or image classification? Training a model to recognize playing cards
    Hi all, I have been experimenting with object detection recently, using Faster R-CNN and YOLOv7 to train models on pre-existing datasets. Using a UNO card dataset I was able to quite accurately detect the type of UNO cards, based on the symbol in the top left corner. I used an object detection approach, with UNO cards only being categorized into 14 classes. Based on that, I am wondering what the best approach would be to enhance the model to use for other and more comprehensive card games. Thinking of card games like Munchkin for example, which has 1000s of different cards. For card games like this, object detection might not be the best approach having 1000s of different classes to consider. ​ The two different approaches I am considering: Using object detection, create as many classes as there are different playing cards in the game, training the model to detect every single card individually or Using object detection, use playing cards to train the model to detect the playing card itself, then use the detected playing card as input for an image classification algorithm ​ For me there are pros and cons to both methods: The first approach might be much more accurate, as it detects each card individually. On the other hand, it seems to me that it needs considerably more classes and data to feed into those classes. It also might be difficult to expand the model with more unique cards, as you would have to rerun the model every time. The second approach might not be as accurate, as it might not only detect playing cards but also identify other objects as playing cards. On the flip side, it seems to me that it is much easier to expand the model with more unique cards. ​ What might be the best approach here? Do you have a different approach to this, which might be more efficient? submitted by /u/Pallemann [link] [comments]  ( 43 min )
    [D] Speech enhancement - like Adobe Enhance/Audo Studio
    Does anyone here know how Audo / Adobe Enhance work under the hood? Just wondering what open-source tooling already exists of similar quality, likewise with data, and architectures? Would anyone be interested in whipping up something open-source that can be self-hosted? submitted by /u/NegotiationUpbeat545 [link] [comments]  ( 42 min )
    [D] Generate data that is not a dataset
    Hey everyone, I'm currently faced with the challenge of having to generate data that is deliberately not in a dataset. So if you think about the dataset as a distribution, the data points should have a possibly low probability. Additionally, each data point is a 30 dimensional vector and I know the min and max values for each dimension. How do I do that? What kinds of algorithms could I use for that? Can I somehow fit a distribution and sample low probability data points from it? Or a GAN for generating? Or are there obvious classical ML or statistical methods for that? submitted by /u/NiconiusX [link] [comments]  ( 42 min )
    [D] Not sure if time series or multiple classifications?
    I am beginning a problem similar to the one bellow for my work. There is a score 1-4 (1 is bad, 4 is very good) of a persons back sprain recovery. The data we have are back sprain recovery scores recorded after two weeks, 3 months and 6 months, along with information (features) about their behavior like sleep, medications, diet, and exercise. We want to predict there 2 week, 3 month, and 6 month back sprain recovery scores based on their initial behavior inputs. For example, given a user sleeps 8 hours a day, consumes x amount of sugar, does physical therapy 4 days a week, and takes x medication, what will there recovery scores be at 2 weeks, 3 months and 6 months? The training data would look like: ​ Sleep Average Medication Days of Physical Therapy Diet Week 2 recovery score Month 3 recovery score Month 6 recovery score 9 hours per night Advil 4 days/ week Healthy 2 3 4 5 hours per night None 0 days/week Unhealthy 1 2 2 ​ I want a model (or multiple models) to predict 3 values which is the 2 week, 3 month, and 6 month scores. I am not familiar with time series, but it seems like the data may be too sparse. Should I be using time series here, or should I create 3 classification models? submitted by /u/spiritualquestions [link] [comments]  ( 43 min )
    [R] Is there a way to combine a knowledge graph and other types of data for ML purposes?
    Hello, I really don't know how to frame this question but I wanted to ask if the was a way to integrate the relationships and nodes of a knowledge graph with recorded data. Like for example, when a knowledge graph contains information about relationships between features, can it be integrated with a dataset containing recorded or measured quantities of those features. The goal of this is to "infuse" the recorded dataset with relationships already known in the knowledge graph for some data analysis purpose. I know it sounds confusing but you can as for clarification on some details. Please help. submitted by /u/Low-Mood3229 [link] [comments]  ( 43 min )
    [D] Discrete vs. Continuous Normalizing Flows
    I'm working on developing methods for some density estimation and inverse modeling tasks on physics simulation data, and normalizing flow methods seem to be a pretty good tool for this job. I'm right now looking to implement a few different model flavors a la INNs and OT-Flow, and am interested in hearing some perspectives from people in the community who have worked with these kinds of models. What would you consider the current state of the art in normalizing flow methods? Most of what I'm finding in the discrete space seems to have converged on flavors of RealNVP, while OT-Flow seems to be the most advanced in the continuous space. Beyond the benchmark performance metrics tabulated in the literature, what can we say about when to prefer continuous vs. discrete models? It's obviously going to be problem-dependent to some degree, but are there general heuristics to be aware of here? For continuous models (and implicit layer methods more generally), where are the research threads currently at improving runtime performance? The 2020 NeurIPS tutorial on implicit layers (link) has been helpful, but it would be interesting to know how things have advanced since then. Any and all insights would be appreciated! submitted by /u/nuclear_knucklehead [link] [comments]  ( 43 min )
    [N] ESANN 2023 | Special Session on Neuro-Symbolic AI (CFP)
    Neuro-symbolic AI is a promising approach to artificial intelligence that aims to combine the strengths of symbolic reasoning and probabilistic systems. For example, combining inductive logic programming and deep learning with applications in graphs, vision, reasoning and explainability. In this special session, we will provide an overview of neuro-symbolic AI, key concepts, and current state-of-the-art techniques. We will also discuss the potential benefits and challenges of neuro-symbolic AI and its potential impact on various fields and applications. In addition to the tutorial, we welcome contributions from attendees in the context of neuro-symbolic AI. This includes but is not limited to: • Novel neuro-symbolic models and techniques • Applications of neuro-symbolic AI to real-world problems • Empirical evaluations and comparisons of neuro-symbolic AI approaches • Theoretical foundations and analysis of neuro-symbolic AI • Emerging trends and challenges in the field of neuro-symbolic AI ​ Submission guidelines: https://www.esann.org/node/6 Paper submission deadline: 2 May 2023 Conference date: 4-6 October 2023 Conference location: Crowne Plaza hotel Bruges, Belgium submitted by /u/iav_tf_h [link] [comments]  ( 42 min )
    [D] Computationally light-weight deep learning research topics?
    Hello, I am familiar with the theory of differentiable computing and SGD training having done some research work on semi-supervised learning for image classification and semantic/panoptic segmentation. In other words, I am familiar with understanding implementing state-of-the-art proposals as well as tweaking them. Now I'm an unemployed and interested in conducting some research using PyTorch and Google Colab which seems feasible only if the problem or topic at hand is relatively low-cost. So I'm asking the question: What are some deep learning (metalearning,regularization, non-supervised training) or applied DL (CV/NLP/...) topics or datasets that are lightweight enough to be researched with just one GPU? Thanks and have a nice weekend submitted by /u/iamnotlefthanded666 [link] [comments]  ( 42 min )
    [D] "Deep Learning Tuning Playbook" (recently released by Google Brain people)
    https://github.com/google-research/tuning_playbook - Google has released a playbook (solely) about how to tune hyper-parameters of neural networks. Disclaimer: I am unrelated to this repository, just came across it and thought it is suitable for this subreddit. I have searched through and found no posts, thus I post it to hear some comments/insights from you ;) submitted by /u/fzyzcjy [link] [comments]  ( 44 min )
    [D] Did YouTube just add upscaling?
    So, these pictures below are taken from a 144p video on YouTube. You cannot tell me that these aren't CNN upscaling artefacts. So this raises the question of.... how exactly is this implemented? What model are they using which is tiny enough to run on (i assume) WebGL2? Is it a CNN inside of GLSL shaders? Is it something else? CPU side or GPU side? And also... how have I not seen a single other person pointing this out, anywhere on the internet. Believe me I looked. Ain't no one talking about this. EDIT: UPDATE this is doing it in ALL videos in chrome now. It only works in Chrome, not in Discord or Edge, so its not GPU/Windows fuckery. But the strange thing is other friends testing this with the same version of Chrome ***DONT*** have this? And the even stranger thing is... this is running on Intel Integrated Graphics... https://preview.redd.it/jnjwjzyag7da1.png?width=3240&format=png&auto=webp&s=504c9fa6ba41ae3a5b1266fe17e519839d3cf933 https://preview.redd.it/6vzyx5f1g7da1.png?width=1182&format=png&auto=webp&s=b56df5017d1fb742f042c847e42818d7f05a1888 https://preview.redd.it/bo36ko40g7da1.png?width=365&format=png&auto=webp&s=1777e238a7299084da9e10eb62c6c2539dc5cc86 https://preview.redd.it/16zpxwqyf7da1.png?width=333&format=png&auto=webp&s=38b9f2f4eb1a5999ad212aab6247e41a913a5294 submitted by /u/Avelina9X [link] [comments]  ( 46 min )
    [N] OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic
    https://time.com/6247678/openai-chatgpt-kenya-workers/ submitted by /u/ChubChubkitty [link] [comments]  ( 54 min )
    [P] paper-hero: Yet Another Paper Search Tool
    Hi guys, thanks for reading this post. I built a simplistic paper search tool that integrates ACL Anthology, arXiv API, and DBLP API. Github address: Spico197/paper-hero Motivation: I'm majoring NLP and I'd like to search for papers with "Event Extraction" as titles in specific proceedings (e.g. ACL, EMNLP). Challenge: There are lots of search tools and APIs, but few of them provide field-specific searches, like authors, titles, abstracts, and venues. Methodology: I integrate ACL Anthology, arXiv API, and DBLP API, and provide a two-stage search toolkit, which first stores target papers via the official fuzzy search API, and then matches specific fields. Advantages: This tool satisfies my need to stockpile papers and it can dump checklists in markdown format, or complete paper information in jsonl. AND and OR logics are supported in search queries. Limitations: This tool is based on simple string matching, so you have to know some terminologies in the target fields. You are warmly welcome to have a try and feel free to drop me an issue! from src.interfaces.aclanthology import AclanthologyPaperList from src.utils import dump_paper_list_to_markdown_checklist if __name__ == "__main__": # use `bash scripts/get_aclanthology.sh` to download and prepare anthology data first paper_list = AclanthologyPaperList("cache/aclanthology.json") ee_query = { "title": [ # Any of the strings below is matched ["information extraction"], ["event", "extraction"], # title must include `event` and `extraction` ["event", "argument", "extraction"], ["event", "detection"], ["event", "classification"], ["event", "tracking"], ["event", "relation", "extraction"], ], # Besides the title constraint, venue must also meet the needs "venue": [ ["acl"], ["emnlp"], ["naacl"], ["coling"], ["findings"], ["tacl"], ["cl"], ], } ee_papers = paper_list.search(ee_query) dump_paper_list_to_markdown_checklist(ee_papers, "results/ee-paper-list.md") ​ markdown checklist submitted by /u/Spico197 [link] [comments]  ( 44 min )
  • Open

    Hey anyone know where I could find a AI assisted writer with no world limit, or at least a VERY high one
    Title submitted by /u/Zan_korida [link] [comments]  ( 40 min )
    Consider this: You know how chatGPT says load failed when its giving YOU the most groundbreaking answer ever? I bet: It’s sent to OpenAI, you get “Load failed” and a less profound answer on regenerate.
    It’s the free preview. No breakthroughs for you. They collect amazing breakthroughs by the minute from humans working the AI. Just add a weight for profoundness 1-10. At 7, crash and send. User never gets it. Thoughts? submitted by /u/Overall-Importance54 [link] [comments]  ( 40 min )
    AI art - automation. A working artist's take.
    submitted by /u/WSCOKN [link] [comments]  ( 40 min )
    This website was created by an AI chatbot, and all of the content was generated by an AI image generator.
    submitted by /u/FreePixelArt [link] [comments]  ( 40 min )
    Amazon Wants To Help Community Colleges with AI
    Amazon has launched an "educator enablement" program to help instructors at community colleges, HBCUs, and other minority-serving institutions learn and teach AI. The professional development program will help college instructors gain a generalist AI skillset. Amazon will provide $1,200 and continuing education credits to 330 participants who complete one of the six boot camps being offered over the course of 2023. For colleges that don't get selected for the educator enablement cohort, Amazon plans to make curriculum materials for any interested college at no cost through Github, YouTube, and AWS Academy This is from the AI With Vibes Newsletter, read the full issue here: https://aiwithvibes.beehiiv.com/p/google-brings-in-legendary-duo-for-chatgpt-battle submitted by /u/Mk_Makanaki [link] [comments]  ( 40 min )
    "Sentient AI" - Example Of Just How Easy It Is To Prompt A Fake Sentient AI(GPT3)
    submitted by /u/TheRPGGamerMan [link] [comments]  ( 40 min )
    AI WARS: Explaining Google's painfully long, 15k-word! tome about their AI plans... or lack thereof
    We covered this in our newsletter today. Here it is verbatim-- if you find it useful, hit the link and sub: https://smokingrobot.beehiiv.com/p/ai-wars ​ Microsoft has dominated BIG TECH headlines over the last few months, thanks largely to a drumbeat of headlines involving their partner OpenAI and its world-shaping ChatGPT. So awe-striking is Microsoft's hand right now, it has made rival companies' advancements, like Apple's recently announced and insanely powerful M2 MacBook Pros, look pedestrian in comparison. But now Google has entered the chat. And by "entered the chat", we mean that CEO Sundar Pichai - Pich-AI? - released a distressingly long 15,000-word(!) treatise on its own endeavors in AI, signaling a counter attack... maybe... at some point in the future... when and if it…  ( 45 min )
    A ChatGPT software engineer in your pocket
    Having AI tools like ChatGPT is like having a personal software engineer in your pocket. But most people don't know how to craft prompts for code. Here's how you can get AI to write software for you: ​ https://preview.redd.it/tfkoz3x4v8da1.png?width=686&format=png&auto=webp&s=11a88815cb44e68fa2a5f63aa99f5867ac0aa755 submitted by /u/Imagine-your-success [link] [comments]  ( 40 min )
    DREAMBOOTH: 10 MINS TRAINING Inside Stable Diffusion!
    submitted by /u/PuppetHere [link] [comments]  ( 40 min )
    Powerful Tools to Test and Improve your Chatbot! 🔥
    submitted by /u/Marinuch [link] [comments]  ( 40 min )
    Walking simulators
    For a while Ive been seeing a lot of videos like these: https://youtu.be/wqvAconYgK0 https://youtu.be/qvpXpCvkqbc https://youtu.be/kQ2bqz3HPJE And Ive been wondering what program these might be using, and if its open to the public. If not is there any program that any of you might suggest to achieve identical if not similar results as I have a few ideas on how this could be utilised within a game engine and animation workspace Thanks submitted by /u/SyhrNewo [link] [comments]  ( 40 min )
    Google plans chatbot search engine and 20 new AI products
    submitted by /u/much_successes [link] [comments]  ( 40 min )
    🚀Online Real-Time Volumetric Nerf + Slam
    submitted by /u/oridnary_artist [link] [comments]  ( 40 min )
    Master Thesis about AI-designed jewelry
    Dear all! My master's thesis is coming to an end, and now I am seeking valuable insights and important data into my topic through a survey. This survey aims to understand consumer awareness and purchase intentions regarding jewelry created with the help of artificial intelligence. You can find the link here: https://ucpresearch.qualtrics.com/.../SV_ex2OALp6fPXQ8Qe I would be grateful if you participated in large numbers and provide me with valuable insights on the current topic. Thank you!! submitted by /u/Ok-Rise453 [link] [comments]  ( 40 min )
    ChatGPT Trend
    submitted by /u/Realistic-Plant3957 [link] [comments]  ( 40 min )
    Are TensorFlow and other ML frameworks worth learning in 2023?
    For some explanation, I am more familiar with PyTorch but I wanted to refresh my knowledge of machine learning and deep learning concepts. However, with the recent trend of moving away from TensorFlow and towards PyTorch, I wonder if TensorFlow and other ML frameworks are still worth learning today. I know certain algorithms are exclusively implemented in one or the other framework. I think at least TensorFlow is good since they’re well-documented and put to the test by other experts. But I’m not so sure about the case of newer custom frameworks. If I dive into them, it could be a step back for me especially after reading this article. It talks about newer ML framework launches and how a lot of people try it out at first, but then interest in said frameworks starts to decrease. I know there are a bunch of good custom frameworks out there but it might take more time for new tools to become mainstream or eventually die down. Which is why I’m afraid to use them at the moment. Let me know what you all think! Thanks! submitted by /u/ActionParticular7697 [link] [comments]  ( 41 min )
    Boston Dynamics reveals Atlas AI robot new ability to grip + autonomously manipulate objects | New 3D modeling Geocode AI creates + Edits highly realistic meshes | Breakthrough Text-To-Video "Tune a Video" uses diffusion models to output coherent video
    submitted by /u/SedatelyMake [link] [comments]  ( 40 min )
    Space Locator
    Hey all. My first post here. I’ll keep it as relevant as possible. I want to develop a AI model which will determine that if the space in a room is enough or not. Like it’ll determine whether the space in the room in enough or not. We’ll provide the pictures of the room and it’ll give us the output. I think there might be some pre-trained models out there which might be helpful. Please guide me in this regard if there are some models or where should I start. I’ll be grateful. Thank you so much in advance submitted by /u/h3artb3att [link] [comments]  ( 40 min )
    Porter Robinson music continued by OpenAI Jukebox
    submitted by /u/anoneemoosh [link] [comments]  ( 40 min )
    ChatGPT Accepted As Co-Author On Multiple Research Papers
    submitted by /u/vadhavaniyafaijan [link] [comments]  ( 40 min )
    Advancements in Natural Language Processing (NLP) and its applications in various industries
    Natural Language Processing (NLP) is a rapidly growing field within the realm of Artificial Intelligence (AI) that is revolutionizing the way we interact with machines. NLP is a branch of AI that deals with the interaction between human language and computers. It is used to analyze, understand and generate human language, and it has a wide range of applications in various industries. One of the most significant advancements in NLP is the development of deep learning algorithms, which have greatly improved the accuracy and efficiency of NLP models. These algorithms have enabled the development of more sophisticated NLP systems, such as those that can understand context and generate human-like responses. One of the most prominent applications of NLP is in the customer service industry. Com…  ( 42 min )
    TextCortex AI: AI Writing Companion - (Our free browser extension)
    submitted by /u/Ruzuyu [link] [comments]  ( 40 min )
    How do you get AI art generators to produce amazing images that look like real art? Take a text-guided diffusion model and feed it the ideal text prompt with the right keywords
    Paper: https://arxiv.org/abs/2209.11711 Abstract: Recent progress in generative models, especially in text-guided diffusion models, has enabled the production of aesthetically-pleasing imagery resembling the works of professional human artists. However, one has to carefully compose the textual description, called the prompt, and augment it with a set of clarifying keywords. Since aesthetics are challenging to evaluate computationally, human feedback is needed to determine the optimal prompt formulation and keyword combination. In this paper, we present a human-in-the-loop approach to learning the most useful combination of prompt keywords using a genetic algorithm. We also show how such an approach can improve the aesthetic appeal of images depicting the same descriptions. https://preview.redd.it/ei4wm8tv66da1.png?width=1852&format=png&auto=webp&s=e20c196fe9a543e6b4ec58b3d4f689624db7f95d submitted by /u/Ok_Mine_5742 [link] [comments]  ( 40 min )
    Created by Stable Diffusion
    submitted by /u/NorthTs [link] [comments]  ( 40 min )
  • Open

    Road Map to Machine Learning & Deep Learning
    A Good Road Map To Machine Learning enginner  ( 14 min )
    Sleep disorders: can AI and Digital Twin help?
    According to the National Sleep Foundation, it is estimated that 50–70 million adults in the United States have a sleep disorder.  ( 25 min )
    Top 10 AI Applications in HRM
    Artificial Intelligence (AI) is revolutionizing the way businesses operate, and the field of Human Resource Management (HRM) is no…  ( 7 min )
    Unlocking the Power of Time Series Forecasting: A Step-by-Step Guide with Code Examples in Python
    Time series forecasting is the process of using a model to predict future values of a time series based on its past values. Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 10 min )
  • Open

    Exposing Reliability Degradation and Mitigation in Approximate DNNs under Permanent Faults
    submitted by /u/Chipdoc [link] [comments]  ( 40 min )
    Machine vision within airports
    My first idea was to use simple object recognition on the x-ray machines in airports to detect weapons. As blunt objects are hard for humans to detect, and so are plastic weapons. Surely this could be solved with a simple object recognition algorithm. I then started researching airport security, and came across, without a doubt, the safest airport in the world, the Israeli airport. They have so many incredibly invasive processes, such as interviews with highly trained personnel, who are trained to spot a liar, and officers following you around if you are deemed "high-risk" (sidenote: they are also unapologetically racist about who they deem as high-risk). Couldn't both of those processes be automated? The interviewing could be done by a non-trained worker, then have cameras that analyse the person. And the officers following people around could also be done using CCTV and some machine vision. Or even analyse people when they're walking around I'm not saying that the Iranian airport should do this, as it would be a step-down, which they clearly will not accept. Instead could this not be done in western airports, as there have been many reports indicating the lack of success when it comes to them catching anyone? Could they implement poor versions of the Iranian airport security system? ​ P.S. I recently visited Senegal (west Africa). On the way back from Senegal to the UK I was totally unaware that I had a large water bottle in my bag, and the screening was so useless that they never found it. The guy manning the scanner was watching TikTok on his phone. When I landed I then could have gone anywhere in the world without being checked again, which goes to show how much of a waste of time the current airport security is. submitted by /u/Tom_nerd [link] [comments]  ( 41 min )
    Which gpu would be better for training? Linux, PyTorch, I have a gigabyte rtx 2070 and amp FirePro s9300 x2?
    Amd* submitted by /u/DaOnlyBaby [link] [comments]  ( 40 min )
    🚀Online Real-Time Volumetric Nerf + Slam
    submitted by /u/oridnary_artist [link] [comments]  ( 40 min )
    How do you get AI art generators to produce amazing images that look like real art? Take a text-guided diffusion model and feed it the ideal text prompt with the right keywords
    Paper: https://arxiv.org/abs/2209.11711 Abstract: Recent progress in generative models, especially in text-guided diffusion models, has enabled the production of aesthetically-pleasing imagery resembling the works of professional human artists. However, one has to carefully compose the textual description, called the prompt, and augment it with a set of clarifying keywords. Since aesthetics are challenging to evaluate computationally, human feedback is needed to determine the optimal prompt formulation and keyword combination. In this paper, we present a human-in-the-loop approach to learning the most useful combination of prompt keywords using a genetic algorithm. We also show how such an approach can improve the aesthetic appeal of images depicting the same descriptions. https://preview.redd.it/p47ovsfc66da1.png?width=1852&format=png&auto=webp&s=516eda2724c4f254b59109fd46a65dba8ffd79a1 submitted by /u/Ok_Mine_5742 [link] [comments]  ( 40 min )
  • Open

    What are the current state-of-the-art algorithms?
    What algorithms are currently considered state-of-the-art? I’m specifically interested in those which are off-policy as I have found DQN to be the best choice for my application so far. Some algorithms I’m considering trying are R2D2, Duelling DQN and Rainbow DQN. Are there any others that could be worth a look? submitted by /u/centripetalstranger [link] [comments]  ( 40 min )
    In RL, how does one provide a theoretical justification of why one algorithm works better than the other?
    Completely random example, let's say that your experiments consistently demonstrate that a recurrent policy (LSTM) in PPO works better than a linear policy, more specifically in one kind of environments (say, environments that require cooperation between agents). Now, how do you justify theoretically your empirical finding? In other words, how do you explain the theory behind the linear policy's limitations? submitted by /u/No_Possibility_7588 [link] [comments]  ( 43 min )
    DQN for simple toy env
    Dear RL community, ​ I'm trying to get DQN (using the stable baselines 3 implementation) to solve my toy environment. No matter how many different hyperparameter configurations I try, I can't get it to work. ​ Here are some details about the environment (with num_objects=1). - the agent is essentially controlling a gripper that moves to one of the squares, grasps the item, and then moves to the target location. - it's a grid world set on 2 levels with each level of size 3x3 - the available actions are one for each square (the agent is teleported there) and 2 more for grasping and releasing. total of 20 discrete actions. - the reward provides a signal as the distance between the object and the target goal, a big reward if succeeded or -1 when it does something bad. ​ Here's my training code. I've also added a human_step function that can be used to control the gripper yourself. ​ Do you have any insights as to why it's not working? Please ask if you need more details about any part of the implementation. ​ Many thanks! submitted by /u/Mr_Physic13 [link] [comments]  ( 41 min )
    How to proceed scientifically when your hypothesis is falsified?
    I predicted that a certain change in the architecture of my agents would boost their coordination (in the context of multi-agent reinforcement learning). However, I tested this in the Meetup environment and it is not working, in the sense that it performs slightly worse than the baseline. This is how the environment works: three agents must collectively choose one of K landmarks and congregate near it. At each time step, each agent receives reward equal to the change in distance between itself and the landmark closest to all three agents. The goal landmark changes depending on the current position of all agents. When all K agents are adjacent to the same landmark, the agents receive a bonus of 1 and the episode ends. Scientifically speaking, how can I be rigorous about testing this hypothesis again? A few ideas: 1 Repeat the experiment multiple times with different random seeds to ensure that the results are robust and not influenced by random variations. 2 Vary the parameters of the agent Vary the number of modules used in the policy and test the effect on coordination. Increase the number of agents 3 Vary the parameters of the environment Changing the number of landmarks Adding distractors 4 Test another environment What do you think? - submitted by /u/No_Possibility_7588 [link] [comments]  ( 42 min )
    Continuous action space that should often return an exact value that's inputted (attempt with reinforce algorithm)
    I was wondering if this is bad practice or bound to fail. I'm in an environment where the state is x1 plus a bunch of other things call them x2. Sometimes the best action is x1 (and exactly x1). Sometimes the best action is a function of x2 (and x1). What I was thinking (and am trying to so far little success) is to have the reinforce algorithm output two gaussian action values. The first, a1, is f(x1,x2). The second, a2, is put into a sigmoid function and if it's greater than 0.5, use action f(x2,x1) and otherwise x1. It seems weird to me that in the reinforce algorithm I'd still use the log probability of both action values if a2<0.5 in which case a1 doesn't get used at all. In that case should I find the probability only of a2 and use that instead? If this idea is completely off base and you have suggestions please lmk. submitted by /u/JustTaxLandLol [link] [comments]  ( 41 min )
    Environment for General AI using Reinforcement Learning?
    I have many custom environments with these features: Observation, Valid Actions at a specific Observation, Sparse reward when the episode is terminated, either 1 or 0 I want to build a Reinforcement Learning Agent that can perform well in these environments. I started this personal project 2 years ago and it get harder the more I try to do it. Is there any other environments like this, or what paper can I read more about this kind of Reinforcement Learning ? submitted by /u/Open_Ranger4375 [link] [comments]  ( 41 min )
    agent not learning using dqn.
    Hello forum, I am trying to get a single joint actuated link to stand upright. I am using dqn as a method for the agent to learn. I have tried using different inputs and outputs with the neural net but is still failing to learn. Can you take a look at my code? #!/usr/bin/env python3 from interbotix_xs_modules.arm import InterbotixManipulatorXS import rospy import time import numpy as np import matplotlib.pyplot as plt import torch import torch.nn as nn import torch.optim as optim import math import random import matplotlib.pyplot as plt ​ from std_msgs.msg import Float64 from gazebo_msgs.msg import LinkStates from geometry_msgs.msg import Pose, Twist from std_srvs.srv import Empty from sensor_msgs.msg import JointState ​ bot = InterbotixManipulatorXS(robot_model="rx15…  ( 44 min )
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    ­­How CCC Intelligent Solutions created a custom approach for hosting complex AI models using Amazon SageMaker
    This post is co-written by Christopher Diaz, Sam Kinard, Jaime Hidalgo and Daniel Suarez  from CCC Intelligent Solutions. In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned. CCC is a […]  ( 13 min )
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    Oval orbits?
    Johannes Kepler thought that planetary orbits were ellipses. Giovanni Cassini thought they were ovals. Kepler was right, but Cassini wasn’t far off. In everyday speech, people use the words ellipse and oval interchangeably. But in mathematics these terms are distinct. There is one definition of an ellipse, and several definitions of an oval. To be […] Oval orbits? first appeared on John D. Cook.  ( 6 min )
    Cassini ovals
    An ellipse can be defined as the set of points such that the sum of the distances to two fixed points, the foci, has a constant value. A Cassini oval is the set of points such that the product of the distances to two foci has a constant value. You can write down an equation […] Cassini ovals first appeared on John D. Cook.  ( 5 min )
    Bounds on power series coefficients
    Let f be an analytic function on the unit disk with f(0) = 0 and derivative f ′(0) = 1. If f is one-to-one (injective) then this puts a strict limit on the size of the series coefficients. Let an be the nth coefficient in the power series for f centered at 0. If f is one-to-one […] Bounds on power series coefficients first appeared on John D. Cook.  ( 5 min )
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    What Is AI Computing?
    The abacus, sextant, slide rule and computer. Mathematical instruments mark the history of human progress. They’ve enabled trade and helped navigate oceans, and advanced understanding and quality of life. The latest tool propelling science and industry is AI computing. AI Computing Defined AI computing is the math-intensive process of calculating machine learning algorithms, typically using Read article >  ( 8 min )
  • Open

    MIT researchers develop an AI model that can detect future lung cancer risk
    Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.  ( 10 min )
  • Open

    Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
    Reinforcement learning provides a conceptual framework for autonomous agents to learn from experience, analogously to how one might train a pet with treats. But practical applications of reinforcement learning are often far from natural: instead of using RL to learn through trial and error by actually attempting the desired task, typical RL applications use a separate (usually simulated) training phase. For example, AlphaGo did not learn to play Go by competing against thousands of humans, but rather by playing against itself in simulation. While this kind of simulated training is appealing for games where the rules are perfectly known, applying this to real world domains such as robotics can require a range of complex approaches, such as the use of simulated data, or instrumenting real-wo…  ( 4 min )
  • Open

    Reverse Differentiation via Predictive Coding. (arXiv:2103.04689v3 [cs.LG] UPDATED)
    Deep learning has redefined the field of artificial intelligence (AI) thanks to the rise of artificial neural networks, which are architectures inspired by their neurological counterpart in the brain. Through the years, this dualism between AI and neuroscience has brought immense benefits to both fields, allowing neural networks to be used in dozens of applications. These networks use an efficient implementation of reverse differentiation, called backpropagation (BP). This algorithm, however, is often criticized for its biological implausibility (e.g., lack of local update rules for the parameters). Therefore, biologically plausible learning methods that rely on predictive coding (PC), a framework for describing information processing in the brain, are increasingly studied. Recent works prove that these methods can approximate BP up to a certain margin on multilayer perceptrons (MLPs), and asymptotically on any other complex model, and that zero-divergence inference learning (Z-IL), a variant of PC, is able to exactly implement BP on MLPs. However, the recent literature shows also that there is no biologically plausible method yet that can exactly replicate the weight update of BP on complex models. To fill this gap, in this paper, we generalize (PC and) Z-IL by directly defining them on computational graphs, and show that it can perform exact reverse differentiation. What results is the first biologically plausible algorithm that is equivalent to BP in the way of updating parameters on any neural network, providing a bridge between the interdisciplinary research of neuroscience and deep learning.  ( 2 min )
    Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning. (arXiv:2210.05582v2 [eess.SP] UPDATED)
    Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open", communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL) and monitoring of the PT for anomaly detection. We specifically investigate the application of the proposed framework to a simple case-study system encompassing multiple sensing devices that report to a common receiver. The Bayesian model trained at the DT has the key advantage of capturing epistemic uncertainty regarding the communication system, e.g., regarding current traffic conditions, which arise from limited PT-to-DT data transfer. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.  ( 2 min )
    How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact. (arXiv:2106.02359v3 [cs.CL] UPDATED)
    Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications. Noting the rising number of applications of other machine learning and AI techniques with pervasive societal impact, we anticipate the rising importance of developing NLP technologies for social good. Inspired by theories in moral philosophy and global priorities research, we aim to promote a guideline for social good in the context of NLP. We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research. Finally, we use our theoretical framework to provide some practical guidelines for future NLP research for social good. Our data and code are available at this http URL In addition, we curate a list of papers and resources on NLP for social good at https://github.com/zhijing-jin/NLP4SocialGood_Papers.  ( 2 min )
    - Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand. (arXiv:2301.06418v2 [cs.AI] UPDATED)
    Electric vehicle charging demand models, with charging records as input, will inherently be biased toward the supply of available chargers, as the data do not include demand lost from occupied stations and competitors. This lost demand implies that the records only observe a fraction of the total demand, i.e. the observations are censored, and actual demand is likely higher than what the data reflect. Machine learning models often neglect to account for this censored demand when forecasting the charging demand, which limits models' applications for future expansions and supply management. We address this gap by modelling the charging demand with probabilistic censorship-aware graph neural networks, which learn the latent demand distribution in both the spatial and temporal dimensions. We use GPS trajectories from cars in Copenhagen, Denmark, to study how censoring occurs and much demand is lost due to occupied charging and competing services. We find that censorship varies throughout the city and over time, encouraging spatial and temporal modelling. We find that in some regions of Copenhagen, censorship occurs 61% of the time. Our results show censorship-aware models provide better prediction and uncertainty estimation in actual future demand than censorship-unaware models. Our results suggest that future models based on charging records should account for the censoring to expand the application areas of machine learning models in this supply management and infrastructure expansion.  ( 2 min )
    Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning. (arXiv:2112.15530v2 [cs.LG] UPDATED)
    Web-based interactions can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successfully applied Graph Convolutional Networks (GCN), though with some limits on accuracy as GCNs have been shown to suffer from over-smoothing issues. Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability. This paper addresses this open problem by relating the Laplacian smoothing to the Generalized PageRank and applying a random-walk based algorithm as a scalable graph filter. This forms the basis for our scalable deep clustering algorithm, RwSL, where through a self-supervised mini-batch training mechanism, we simultaneously optimize a deep neural network for sample-cluster assignment distribution and an autoencoder for a clustering-oriented embedding. Using 6 real-world datasets and 6 clustering metrics, we show that RwSL achieved improved results over several recent baselines. Most notably, we show that RwSL, unlike all other deep clustering frameworks, can continue to scale beyond graphs with more than one million nodes, i.e., handle web-scale. We also demonstrate how RwSL could perform node clustering on a graph with 1.8 billion edges using only a single GPU.  ( 2 min )
    Concentration inequalities for leave-one-out cross validation. (arXiv:2211.02478v2 [math.ST] UPDATED)
    In this article we prove that estimator stability is enough to show that leave-one-out cross validation is a sound procedure, by providing concentration bounds in a general framework. In particular, we provide concentration bounds beyond Lipschitz continuity assumptions on the loss or on the estimator. In order to obtain our results, we rely on random variables with distribution satisfying the logarithmic Sobolev inequality, providing us a relatively rich class of distributions. We illustrate our method by considering several interesting examples, including linear regression, kernel density estimation, and stabilized / truncated estimators such as stabilized kernel regression.  ( 2 min )
    Adversarial AI in Insurance: Pervasiveness and Resilience. (arXiv:2301.07520v1 [cs.LG])
    The rapid and dynamic pace of Artificial Intelligence (AI) and Machine Learning (ML) is revolutionizing the insurance sector. AI offers significant, very much welcome advantages to insurance companies, and is fundamental to their customer-centricity strategy. It also poses challenges, in the project and implementation phase. Among those, we study Adversarial Attacks, which consist of the creation of modified input data to deceive an AI system and produce false outputs. We provide examples of attacks on insurance AI applications, categorize them, and argue on defence methods and precautionary systems, considering that they can involve few-shot and zero-shot multilabelling. A related topic, with growing interest, is the validation and verification of systems incorporating AI and ML components. These topics are discussed in various sections of this paper.  ( 2 min )
    Global Contrastive Batch Sampling via Optimization on Sample Permutations. (arXiv:2210.12874v3 [cs.LG] UPDATED)
    Contrastive Learning has recently achieved state-of-the-art performance in a wide range of tasks. Many contrastive learning approaches use mined hard negatives to make batches more informative during training but these approaches are inefficient as they increase epoch length proportional to the number of mined negatives and require frequent updates of nearest neighbor indices or mining from recent batches. In this work, we provide an alternative to hard negative mining, Global Contrastive Batch Sampling (GCBS), an efficient approximation to the batch assignment problem that upper bounds the gap between the global and training losses, $\mathcal{L}^{Global} - \mathcal{L}^{Train}$, in contrastive learning settings. Through experimentation we find GCBS improves state-of-the-art performance in sentence embedding and code-search tasks. Additionally, GCBS is easy to implement as it requires only a few additional lines of code, does not maintain external data structures such as nearest neighbor indices, is more computationally efficient than the most minimal hard negative mining approaches, and makes no changes to the model being trained.  ( 2 min )
    Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures. (arXiv:2204.04273v2 [cs.LG] UPDATED)
    Deep learning using neural networks is an effective technique for generating models of complex data. However, training such models can be expensive when networks have large model capacity resulting from a large number of layers and nodes. For training in such a computationally prohibitive regime, dimensionality reduction techniques ease the computational burden, and allow implementations of more robust networks. We propose a novel type of such dimensionality reduction via a new deep learning architecture based on fast matrix multiplication of a Kronecker product decomposition; in particular our network construction can be viewed as a Kronecker product-induced sparsification of an "extended" fully connected network. Analysis and practical examples show that this architecture allows a neural network to be trained and implemented with a significant reduction in computational time and resources, while achieving a similar error level compared to a traditional feedforward neural network.  ( 2 min )
    Dirichlet-Neumann learning algorithm for solving elliptic interface problems. (arXiv:2301.07361v1 [math.NA])
    Non-overlapping domain decomposition methods are natural for solving interface problems arising from various disciplines, however, the numerical simulation requires technical analysis and is often available only with the use of high-quality grids, thereby impeding their use in more complicated situations. To remove the burden of mesh generation and to effectively tackle with the interface jump conditions, a novel mesh-free scheme, i.e., Dirichlet-Neumann learning algorithm, is proposed in this work to solve the benchmark elliptic interface problem with high-contrast coefficients as well as irregular interfaces. By resorting to the variational principle, we carry out a rigorous error analysis to evaluate the discrepancy caused by the boundary penalty treatment for each decomposed subproblem, which paves the way for realizing the Dirichlet-Neumann algorithm using neural network extension operators. The effectiveness and robustness of our proposed methods are demonstrated experimentally through a series of elliptic interface problems, achieving better performance over other alternatives especially in the presence of erroneous flux prediction at interface.  ( 2 min )
    CLIPTER: Looking at the Bigger Picture in Scene Text Recognition. (arXiv:2301.07464v1 [cs.CV])
    Understanding the scene is often essential for reading text in real-world scenarios. However, current scene text recognizers operate on cropped text images, unaware of the bigger picture. In this work, we harness the representative power of recent vision-language models, such as CLIP, to provide the crop-based recognizer with scene, image-level information. Specifically, we obtain a rich representation of the entire image and fuse it with the recognizer word-level features via cross-attention. Moreover, a gated mechanism is introduced that gradually shifts to the context-enriched representation, enabling simply fine-tuning a pretrained recognizer. We implement our model-agnostic framework, named CLIPTER - CLIP Text Recognition, on several leading text recognizers and demonstrate consistent performance gains, achieving state-of-the-art results over multiple benchmarks. Furthermore, an in-depth analysis reveals improved robustness to out-of-vocabulary words and enhanced generalization in low-data regimes.  ( 2 min )
    Prompting Large Language Model for Machine Translation: A Case Study. (arXiv:2301.07069v2 [cs.CL] UPDATED)
    Research on prompting has shown excellent performance with little or even no supervised training across many tasks. However, prompting for machine translation is still under-explored in the literature. We fill this gap by offering a systematic study on prompting strategies for translation, examining various factors for prompt template and demonstration example selection. We further explore the use of monolingual data and the feasibility of cross-lingual, cross-domain, and sentence-to-document transfer learning in prompting. Extensive experiments with GLM-130B (Zeng et al., 2022) as the testbed show that 1) the number and the quality of prompt examples matter, where using suboptimal examples degenerates translation; 2) several features of prompt examples, such as semantic similarity, show significant Spearman correlation with their prompting performance; yet, none of the correlations are strong enough; 3) using pseudo parallel prompt examples constructed from monolingual data via zero-shot prompting could improve translation; and 4) improved performance is achievable by transferring knowledge from prompt examples selected in other settings. We finally provide an analysis on the model outputs and discuss several problems that prompting still suffers from.  ( 2 min )
    Neural DAEs: Constrained neural networks. (arXiv:2211.14302v2 [cs.LG] UPDATED)
    In this article we investigate the effect of explicitly adding auxiliary trajectory information to neural networks for dynamical systems. We draw inspiration from the field of differential-algebraic equations and differential equations on manifolds and implement similar methods in residual neural networks. We discuss constraints through stabilization as well as projection methods, and show when to use which method based on experiments involving simulations of multi-body pendulums and molecular dynamics scenarios. Several of our methods are easy to implement in existing code and have limited impact on training performance while giving significant boosts in terms of inference.  ( 2 min )
    Nostradamus: Weathering Worth. (arXiv:2212.05933v2 [q-fin.ST] UPDATED)
    Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements (including natural disasters, climate and weather conditions) and stock prices, using historical stock market data, historical climate data, and various climate indicators such as carbon dioxide emissions. We have conducted our study based on the US financial market, global climate trends, and daily weather records to demonstrate a significant relationship between climate and stock price fluctuation. Our analysis covers both short-term and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe the effect they have on people's emotional state and their influence on the stock market.  ( 2 min )
    Quantification of geogrid lateral restraint using transparent sand and deep learning-based image segmentation. (arXiv:2212.02939v2 [physics.geo-ph] UPDATED)
    An experimental technique is presented to quantify the lateral restraint provided by a geogrid embedded in granular soil at the particle level. Repeated load triaxial tests were done on transparent sand specimens with geosynthetic inclusions simulating geogrids. Particle outlines on laser illuminated planes through the specimens were segmented using a deep learning-based segmentation algorithm. The particle outlines were characterized in terms of Fourier shape descriptors and tracked across sequentially captured images. The accuracy of the particle displacement measurements was validated against Digital Image Correlation (DIC) measurements. In addition, the method's resolution and repeatability is presented. Based on the measured particle displacements and rotations, a state boundary line between probable and improbable particle motions was identified for each test. The size of the zone of probable motions could be used to quantify the lateral restraint provided by the inclusions. Overall, the tests results revealed that the geosynthetic inclusions restricted both particle displacements and rotations. However, the particle displacements were found to be restrained more significantly than the rotations. Finally, a unique relationship was found between the magnitude of the permanent strains of the specimens and the size of the zone of probable motions.  ( 2 min )
    Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory. (arXiv:2210.09784v4 [physics.chem-ph] UPDATED)
    We extend our recently proposed Deep Learning-aided many-body dispersion (DNN-MBD) model to quadrupole polarizability (Q) terms using a generalized Random Phase Approximation (RPA) formalism, thus enabling the inclusion of van der Waals contributions beyond dipole. The resulting DNN-MBDQ model only relies on ab initio-derived quantities as the introduced quadrupole polarizabilities are recursively retrieved from dipole ones, in turn modelled via the Tkatchenko-Scheffler method. A transferable and efficient deep-neuronal network (DNN) provides atom in molecule volumes, while a single range-separation parameter is used to couple the model to Density Functional Theory (DFT). Since it can be computed at a negligible cost, the DNN-MBDQ approach can be coupled with DFT functionals such as PBE,PBE0 and B86bPBE (dispersionless). The DNN-MBQ-corrected functionals reach chemical accuracy while exhibiting lower errors compared to their dipole-only counterparts.  ( 2 min )
    Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models. (arXiv:2301.06267v2 [cs.CV] UPDATED)
    The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better ${\bf visual}$ dog classifier by ${\bf read}$ing about dogs and ${\bf listen}$ing to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification.  ( 2 min )
    Spiking Neural Network Decision Feedback Equalization. (arXiv:2211.04756v3 [eess.SP] UPDATED)
    In the past years, artificial neural networks (ANNs) have become the de-facto standard to solve tasks in communications engineering that are difficult to solve with traditional methods. In parallel, the artificial intelligence community drives its research to biology-inspired, brain-like spiking neural networks (SNNs), which promise extremely energy-efficient computing. In this paper, we investigate the use of SNNs in the context of channel equalization for ultra-low complexity receivers. We propose an SNN-based equalizer with a feedback structure akin to the decision feedback equalizer (DFE). For conversion of real-world data into spike signals we introduce a novel ternary encoding and compare it with traditional log-scale encoding. We show that our approach clearly outperforms conventional linear equalizers for three different exemplary channels. We highlight that mainly the conversion of the channel output to spikes introduces a small performance penalty. The proposed SNN with a decision feedback structure enables the path to competitive energy-efficient transceivers.  ( 2 min )
    Antenna Array Calibration Via Gaussian Process Models. (arXiv:2301.06582v2 [eess.SP] UPDATED)
    Antenna array calibration is necessary to maintain the high fidelity of beam patterns across a wide range of advanced antenna systems and to ensure channel reciprocity in time division duplexing schemes. Despite the continuous development in this area, most existing solutions are optimised for specific radio architectures, require standardised over-the-air data transmission, or serve as extensions of conventional methods. The diversity of communication protocols and hardware creates a problematic case, since this diversity requires to design or update the calibration procedures for each new advanced antenna system. In this study, we formulate antenna calibration in an alternative way, namely as a task of functional approximation, and address it via Bayesian machine learning. Our contributions are three-fold. Firstly, we define a parameter space, based on near-field measurements, that captures the underlying hardware impairments corresponding to each radiating element, their positional offsets, as well as the mutual coupling effects between antenna elements. Secondly, Gaussian process regression is used to form models from a sparse set of the aforementioned near-field data. Once deployed, the learned non-parametric models effectively serve to continuously transform the beamforming weights of the system, resulting in corrected beam patterns. Lastly, we demonstrate the viability of the described methodology for both digital and analog beamforming antenna arrays of different scales and discuss its further extension to support real-time operation with dynamic hardware impairments.  ( 2 min )
    Teacher Forcing Recovers Reward Functions for Text Generation. (arXiv:2210.08708v2 [cs.LG] UPDATED)
    Reinforcement learning (RL) has been widely used in text generation to alleviate the exposure bias issue or to utilize non-parallel datasets. The reward function plays an important role in making RL training successful. However, previous reward functions are typically task-specific and sparse, restricting the use of RL. In our work, we propose a task-agnostic approach that derives a step-wise reward function directly from a model trained with teacher forcing. We additionally propose a simple modification to stabilize the RL training on non-parallel datasets with our induced reward function. Empirical results show that our method outperforms self-training and reward regression methods on several text generation tasks, confirming the effectiveness of our reward function.  ( 2 min )
    Planning and Learning with Adaptive Lookahead. (arXiv:2201.12403v2 [cs.LG] UPDATED)
    Some of the most powerful reinforcement learning frameworks use planning for action selection. Interestingly, their planning horizon is either fixed or determined arbitrarily by the state visitation history. Here, we expand beyond the naive fixed horizon and propose a theoretically justified strategy for adaptive selection of the planning horizon as a function of the state-dependent value estimate. We propose two variants for lookahead selection and analyze the trade-off between iteration count and computational complexity per iteration. We then devise a corresponding deep Q-network algorithm with an adaptive tree search horizon. We separate the value estimation per depth to compensate for the off-policy discrepancy between depths. Lastly, we demonstrate the efficacy of our adaptive lookahead method in a maze environment and Atari.  ( 2 min )
    An Analysis of Loss Functions for Binary Classification and Regression. (arXiv:2301.07638v1 [stat.ML])
    This paper explores connections between margin-based loss functions and consistency in binary classification and regression applications. It is shown that a large class of margin-based loss functions for binary classification/regression result in estimating scores equivalent to log-likelihood scores weighted by an even function. A simple characterization for conformable (consistent) loss functions is given, which allows for straightforward comparison of different losses, including exponential loss, logistic loss, and others. The characterization is used to construct a new Huber-type loss function for the logistic model. A simple relation between the margin and standardized logistic regression residuals is derived, demonstrating that all margin-based loss can be viewed as loss functions of squared standardized logistic regression residuals. The relation provides new, straightforward interpretations for exponential and logistic loss, and aids in understanding why exponential loss is sensitive to outliers. In particular, it is shown that minimizing empirical exponential loss is equivalent to minimizing the sum of squared standardized logistic regression residuals. The relation also provides new insight into the AdaBoost algorithm.  ( 2 min )
    Operator Learning Framework for Digital Twin and Complex Engineering Systems. (arXiv:2301.06701v2 [cs.LG] UPDATED)
    With modern computational advancements and statistical analysis methods, machine learning algorithms have become a vital part of engineering modeling. Neural Operator Networks (ONets) is an emerging machine learning algorithm as a "faster surrogate" for approximating solutions to partial differential equations (PDEs) due to their ability to approximate mathematical operators versus the direct approximation of Neural Networks (NN). ONets use the Universal Approximation Theorem to map finite-dimensional inputs to infinite-dimensional space using the branch-trunk architecture, which encodes domain and feature information separately before using a dot product to combine the information. ONets are expected to occupy a vital niche for surrogate modeling in physical systems and Digital Twin (DT) development. Three test cases are evaluated using ONets for operator approximation, including a 1-dimensional ordinary differential equations (ODE), general diffusion system, and convection-diffusion (Burger) system. Solutions for ODE and diffusion systems yield accurate and reliable results (R2>0.95), while solutions for Burger systems need further refinement in the ONet algorithm.  ( 2 min )
    Joint Representation Learning for Text and 3D Point Cloud. (arXiv:2301.07584v1 [cs.CV])
    Recent advancements in vision-language pre-training (e.g. CLIP) have shown that vision models can benefit from language supervision. While many models using language modality have achieved great success on 2D vision tasks, the joint representation learning of 3D point cloud with text remains under-explored due to the difficulty of 3D-Text data pair acquisition and the irregularity of 3D data structure. In this paper, we propose a novel Text4Point framework to construct language-guided 3D point cloud models. The key idea is utilizing 2D images as a bridge to connect the point cloud and the language modalities. The proposed Text4Point follows the pre-training and fine-tuning paradigm. During the pre-training stage, we establish the correspondence of images and point clouds based on the readily available RGB-D data and use contrastive learning to align the image and point cloud representations. Together with the well-aligned image and text features achieved by CLIP, the point cloud features are implicitly aligned with the text embeddings. Further, we propose a Text Querying Module to integrate language information into 3D representation learning by querying text embeddings with point cloud features. For fine-tuning, the model learns task-specific 3D representations under informative language guidance from the label set without 2D images. Extensive experiments demonstrate that our model shows consistent improvement on various downstream tasks, such as point cloud semantic segmentation, instance segmentation, and object detection. The code will be available here: https://github.com/LeapLabTHU/Text4Point  ( 2 min )
    A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems. (arXiv:2212.01351v2 [eess.SP] UPDATED)
    Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control, monitor, and analyze software-based, "open", communication systems. Notably, DT platforms provide a sandbox in which to test artificial intelligence (AI) solutions for communication systems, potentially reducing the need to collect data and test algorithms in the field, i.e., on the physical twin (PT). A key challenge in the deployment of DT systems is to ensure that virtual control optimization, monitoring, and analysis at the DT are safe and reliable, avoiding incorrect decisions caused by "model exploitation". To address this challenge, this paper presents a general Bayesian framework with the aim of quantifying and accounting for model uncertainty at the DT that is caused by limitations in the amount and quality of data available at the DT from the PT. In the proposed framework, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL), monitoring of the PT for anomaly detection, prediction, data-collection optimization, and counterfactual analysis. To exemplify the application of the proposed framework, we specifically investigate a case-study system encompassing multiple sensing devices that report to a common receiver. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.  ( 2 min )
    Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach. (arXiv:2102.04170v3 [eess.SP] UPDATED)
    This paper investigates task-oriented communication for edge inference, where a low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. We propose a learning-based communication scheme that jointly optimizes feature extraction, source coding, and channel coding in a task-oriented manner, i.e., targeting the downstream inference task rather than data reconstruction. Specifically, we leverage an information bottleneck (IB) framework to formalize a rate-distortion tradeoff between the informativeness of the encoded feature and the inference performance. As the IB optimization is computationally prohibitive for the high-dimensional data, we adopt a variational approximation, namely the variational information bottleneck (VIB), to build a tractable upper bound. To reduce the communication overhead, we leverage a sparsity-inducing distribution as the variational prior for the VIB framework to sparsify the encoded feature vector. Furthermore, considering dynamic channel conditions in practical communication systems, we propose a variable-length feature encoding scheme based on dynamic neural networks to adaptively adjust the activated dimensions of the encoded feature to different channel conditions. Extensive experiments evidence that the proposed task-oriented communication system achieves a better rate-distortion tradeoff than baseline methods and significantly reduces the feature transmission latency in dynamic channel conditions.  ( 2 min )
    Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition. (arXiv:2202.07901v2 [cs.LG] UPDATED)
    Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cross-modal representation learning from different data types -- such as images and time-series data (e.g., audio or text data) -- requires a deep metric learning loss that minimizes the distance between the modality embeddings. In this paper, we propose to use the triplet loss, which uses positive and negative identities to create sample pairs with different labels, for cross-modal representation learning between image and time-series modalities (CMR-IS). By adapting the triplet loss for cross-modal representation learning, higher accuracy in the main (time-series classification) task can be achieved by exploiting additional information of the auxiliary (image classification) task. Our experiments on synthetic data and handwriting recognition data from sensor-enhanced pens show improved classification accuracy, faster convergence, and better generalizability.  ( 2 min )
    Theseus: A Library for Differentiable Nonlinear Optimization. (arXiv:2207.09442v3 [cs.RO] UPDATED)
    We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision. Existing DNLS implementations are application specific and do not always incorporate many ingredients important for efficiency. Theseus is application-agnostic, as we illustrate with several example applications that are built using the same underlying differentiable components, such as second-order optimizers, standard costs functions, and Lie groups. For efficiency, Theseus incorporates support for sparse solvers, automatic vectorization, batching, GPU acceleration, and gradient computation with implicit differentiation and direct loss minimization. We do extensive performance evaluation in a set of applications, demonstrating significant efficiency gains and better scalability when these features are incorporated. Project page: https://sites.google.com/view/theseus-ai  ( 2 min )
    Non-parametric identifiability and sensitivity analysis of synthetic control models. (arXiv:2301.07656v1 [stat.ME])
    Quantifying cause and effect relationships is an important problem in many domains. The gold standard solution is to conduct a randomised controlled trial. However, in many situations such trials cannot be performed. In the absence of such trials, many methods have been devised to quantify the causal impact of an intervention from observational data given certain assumptions. One widely used method are synthetic control models. While identifiability of the causal estimand in such models has been obtained from a range of assumptions, it is widely and implicitly assumed that the underlying assumptions are satisfied for all time periods both pre- and post-intervention. This is a strong assumption, as synthetic control models can only be learned in pre-intervention period. In this paper we address this challenge, and prove identifiability can be obtained without the need for this assumption, by showing it follows from the principle of invariant causal mechanisms. Moreover, for the first time, we formulate and study synthetic control models in Pearl's structural causal model framework. Importantly, we provide a general framework for sensitivity analysis of synthetic control causal inference to violations of the assumptions underlying non-parametric identifiability. We end by providing an empirical demonstration of our sensitivity analysis framework on simulated and real data in the widely-used linear synthetic control framework.  ( 2 min )
    Comprehensive Literature Survey on Deep Learning used in Image Memorability Prediction and Modification. (arXiv:2301.06080v2 [cs.CV] UPDATED)
    As humans, we can remember certain visuals in great detail, and sometimes even after viewing them once. What is even more interesting is that humans tend to remember and forget the same things, suggesting that there might be some general internal characteristics of an image to encode and discard similar types of information. Research suggests that some pictures tend to be memorized more than others. The ability of an image to be remembered by different viewers is one of its intrinsic properties. In visualization and photography, creating memorable images is a difficult task. Hence, to solve the problem, various techniques predict visual memorability and manipulate images' memorability. We present a comprehensive literature survey to assess the deep learning techniques used to predict and modify memorability. In particular, we analyze the use of Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks for image memorability prediction and modification.  ( 2 min )
    Factors other than climate change are currently more important in predicting how well fruit farms are doing financially. (arXiv:2301.07685v1 [cs.LG])
    Machine learning and statistical modeling methods were used to analyze the impact of climate change on financial wellbeing of fruit farmers in Tunisia and Chile. The analysis was based on face to face interviews with 801 farmers. Three research questions were investigated. First, whether climate change impacts had an effect on how well the farm was doing financially. Second, if climate change was not influential, what factors were important for predicting financial wellbeing of the farm. And third, ascertain whether observed effects on the financial wellbeing of the farm were a result of interactions between predictor variables. This is the first report directly comparing climate change with other factors potentially impacting financial wellbeing of farms. Certain climate change factors, namely increases in temperature and reductions in precipitation, can regionally impact self-perceived financial wellbeing of fruit farmers. Specifically, increases in temperature and reduction in precipitation can have a measurable negative impact on the financial wellbeing of farms in Chile. This effect is less pronounced in Tunisia. Climate impact differences were observed within Chile but not in Tunisia. However, climate change is only of minor importance for predicting farm financial wellbeing, especially for farms already doing financially well. Factors that are more important, mainly in Tunisia, included trust in information sources and prior farm ownership. Other important factors include farm size, water management systems used and diversity of fruit crops grown. Moreover, some of the important factors identified differed between farms doing and not doing well financially. Interactions between factors may improve or worsen farm financial wellbeing.  ( 2 min )
    An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence. (arXiv:2103.15990v7 [cs.HC] UPDATED)
    With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.  ( 2 min )
    Improving Federated Learning Personalization via Model Agnostic Meta Learning. (arXiv:1909.12488v2 [cs.LG] UPDATED)
    Federated Learning (FL) refers to learning a high quality global model based on decentralized data storage, without ever copying the raw data. A natural scenario arises with data created on mobile phones by the activity of their users. Given the typical data heterogeneity in such situations, it is natural to ask how can the global model be personalized for every such device, individually. In this work, we point out that the setting of Model Agnostic Meta Learning (MAML), where one optimizes for a fast, gradient-based, few-shot adaptation to a heterogeneous distribution of tasks, has a number of similarities with the objective of personalization for FL. We present FL as a natural source of practical applications for MAML algorithms, and make the following observations. 1) The popular FL algorithm, Federated Averaging, can be interpreted as a meta learning algorithm. 2) Careful fine-tuning can yield a global model with higher accuracy, which is at the same time easier to personalize. However, solely optimizing for the global model accuracy yields a weaker personalization result. 3) A model trained using a standard datacenter optimization method is much harder to personalize, compared to one trained using Federated Averaging, supporting the first claim. These results raise new questions for FL, MAML, and broader ML research.  ( 2 min )
    Creating awareness about security and safety on highways to mitigate wildlife-vehicle collisions by detecting and recognizing wildlife fences using deep learning and drone technology. (arXiv:2301.07174v1 [cs.CV])
    In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences for the classification task. Additionally, we used aerial and still annotated images extracted from the drone and still cameras for the segmentation and detection tasks. The model training results from the drone camera outperformed those from the still camera. Generally, poor model performance is attributed to (1) over-decompression of images and (2) the ability of drone cameras to capture more details on images for the machine learning model to learn as compared to still cameras that capture only the front view of the wildlife fence. We argue that our model can be deployed on client-edge devices to inform people about the presence and significance of wildlife fencing, which minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle collisions.  ( 2 min )
    Enhancing Self-Training Methods. (arXiv:2301.07294v1 [cs.LG])
    Semi-supervised learning approaches train on small sets of labeled data along with large sets of unlabeled data. Self-training is a semi-supervised teacher-student approach that often suffers from the problem of "confirmation bias" that occurs when the student model repeatedly overfits to incorrect pseudo-labels given by the teacher model for the unlabeled data. This bias impedes improvements in pseudo-label accuracy across self-training iterations, leading to unwanted saturation in model performance after just a few iterations. In this work, we describe multiple enhancements to improve the self-training pipeline to mitigate the effect of confirmation bias. We evaluate our enhancements over multiple datasets showing performance gains over existing self-training design choices. Finally, we also study the extendability of our enhanced approach to Open Set unlabeled data (containing classes not seen in labeled data).  ( 2 min )
    Safety Verification of Neural Network Control Systems Using Guaranteed Neural Network Model Reduction. (arXiv:2301.07531v1 [cs.LG])
    This paper aims to enhance the computational efficiency of safety verification of neural network control systems by developing a guaranteed neural network model reduction method. First, a concept of model reduction precision is proposed to describe the guaranteed distance between the outputs of a neural network and its reduced-size version. A reachability-based algorithm is proposed to accurately compute the model reduction precision. Then, by substituting a reduced-size neural network controller into the closed-loop system, an algorithm to compute the reachable set of the original system is developed, which is able to support much more computationally efficient safety verification processes. Finally, the developed methods are applied to a case study of the Adaptive Cruise Control system with a neural network controller, which is shown to significantly reduce the computational time of safety verification and thus validate the effectiveness of the method.  ( 2 min )
    Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. (arXiv:2202.08312v3 [cs.LG] UPDATED)
    Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting. We prove fundamental theoretical results on the applicability of matrix factorizations to adaptive streams, and provide a parameter-free fixed-point algorithm for computing optimal factorizations. We instantiate this framework with respect to concrete matrices which arise naturally in machine learning, and train user-level differentially private models with the resulting optimal mechanisms, yielding significant improvements in a notable problem in federated learning with user-level differential privacy.  ( 2 min )
    Learning image representations for anomaly detection: application to discovery of histological alterations in drug development. (arXiv:2210.07675v3 [cs.CV] UPDATED)
    We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on healthy data can detect out-of-distribution anomalous samples. Such approaches combined with pre-trained Convolutional Neural Network (CNN) representations of images were previously employed for anomaly detection (AD). However, pre-trained off-the-shelf CNN representations may not be sensitive to abnormal conditions in tissues, while natural variations of healthy tissue may result in distant representations. To adapt representations to relevant details in healthy tissue we propose training a CNN on an auxiliary task that discriminates healthy tissue of different species, organs, and staining reagents. Almost no additional labeling workload is required, since healthy samples come automatically with aforementioned labels. During training we enforce compact image representations with a center-loss term, which further improves representations for AD. The proposed system outperforms established AD methods on a published dataset of liver anomalies. Moreover, it provided comparable results to conventional methods specifically tailored for quantification of liver anomalies. We show that our approach can be used for toxicity assessment of candidate drugs at early development stages and thereby may reduce expensive late-stage drug attrition.  ( 2 min )
    Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data. (arXiv:2301.07628v1 [cs.CR])
    We develop the first universal password model -- a password model that, once pre-trained, can automatically adapt to any password distribution. To achieve this result, the model does not need to access any plaintext passwords from the target set. Instead, it exploits users' auxiliary information, such as email addresses, as a proxy signal to predict the underlying target password distribution. The model uses deep learning to capture the correlation between the auxiliary data of a group of users (e.g., users of a web application) and their passwords. It then exploits those patterns to create a tailored password model for the target community at inference time. No further training steps, targeted data collection, or prior knowledge of the community's password distribution is required. Besides defining a new state-of-the-art for password strength estimation, our model enables any end-user (e.g., system administrators) to autonomously generate tailored password models for their systems without the often unworkable requirement of collecting suitable training data and fitting the underlying password model. Ultimately, our framework enables the democratization of well-calibrated password models to the community, addressing a major challenge in the deployment of password security solutions on a large scale.  ( 2 min )
    Feature Alignment as a Generative Process. (arXiv:2106.12562v2 [cs.LG] UPDATED)
    Reversibility in artificial neural networks allows us to retrieve the input given an output. We present feature alignment, a method for approximating reversibility in arbitrary neural networks. We train a network by minimizing the distance between the output of a data point and the random output with respect to a random input. We applied the technique to the MNIST, CIFAR-10, CelebA and STL-10 image datasets. We demonstrate that this method can roughly recover images from just their latent representation without the need of a decoder. By utilizing the formulation of variational autoencoders, we demonstrate that it is possible to produce new images that are statistically comparable to the training data. Furthermore, we demonstrate that the quality of the images can be improved by coupling a generator and a discriminator together. In addition, we show how this method, with a few minor modifications, can be used to train networks locally, which has the potential to save computational memory resources.  ( 2 min )
    Cross-Domain Evaluation of a Deep Learning-Based Type Inference System. (arXiv:2208.09189v2 [cs.SE] UPDATED)
    Optional type annotations allow for enriching dynamic programming languages with static typing features like better Integrated Development Environment (IDE) support, more precise program analysis, and early detection and prevention of type-related runtime errors. Machine learning-based type inference promises interesting results for automating this task. However, the practical usage of such systems depends on their ability to generalize across different domains, as they are often applied outside their training domain. In this work, we investigate Type4Py as a representative of state-of-the-art deep learning-based type inference systems, by conducting extensive cross-domain experiments. Thereby, we address the following problems: class imbalances, out-of-vocabulary words, dataset shifts, and unknown classes. To perform such experiments, we use the datasets ManyTypes4Py and CrossDomainTypes4Py. The latter we introduce in this paper. Our dataset enables the evaluation of type inference systems in different domains of software projects and has over 1,000,000 type annotations mined on the platforms GitHub and Libraries. It consists of data from the two domains web development and scientific calculation. Through our experiments, we detect that the shifts in the dataset and the long-tailed distribution with many rare and unknown data types decrease the performance of the deep learning-based type inference system drastically. In this context, we test unsupervised domain adaptation methods and fine-tuning to overcome these issues. Moreover, we investigate the impact of out-of-vocabulary words.  ( 2 min )
    InstructPix2Pix: Learning to Follow Image Editing Instructions. (arXiv:2211.09800v2 [cs.CV] UPDATED)
    We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models -- a language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to generate a large dataset of image editing examples. Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per example fine-tuning or inversion, our model edits images quickly, in a matter of seconds. We show compelling editing results for a diverse collection of input images and written instructions.  ( 2 min )
    Sample Complexity of Adversarially Robust Linear Classification on Separated Data. (arXiv:2012.10794v3 [cs.LG] UPDATED)
    We consider the sample complexity of learning with adversarial robustness. Most prior theoretical results for this problem have considered a setting where different classes in the data are close together or overlapping. Motivated by some real applications, we consider, in contrast, the well-separated case where there exists a classifier with perfect accuracy and robustness, and show that the sample complexity narrates an entirely different story. Specifically, for linear classifiers, we show a large class of well-separated distributions where the expected robust loss of any algorithm is at least $\Omega(\frac{d}{n})$, whereas the max margin algorithm has expected standard loss $O(\frac{1}{n})$. This shows a gap in the standard and robust losses that cannot be obtained via prior techniques. Additionally, we present an algorithm that, given an instance where the robustness radius is much smaller than the gap between the classes, gives a solution with expected robust loss is $O(\frac{1}{n})$. This shows that for very well-separated data, convergence rates of $O(\frac{1}{n})$ are achievable, which is not the case otherwise. Our results apply to robustness measured in any $\ell_p$ norm with $p > 1$ (including $p = \infty$).  ( 2 min )
    An investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms. (arXiv:2301.07665v1 [cs.SD])
    In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers based on musical instrument timbre compression. Unsupervised deep learning methods can achieve audio compression by training the network to learn a mapping from waveforms or spectrograms to low-dimensional representations. This study investigates the use of stacked convolutional autoencoders for the compression of time-frequency audio representations for a variety of instruments for a single pitch. Further exploration of hyper-parameters and regularization techniques is demonstrated to enhance the performance of the initial design. In an unsupervised manner, the network is able to reconstruct a monophonic and harmonic sound based on latent representations. In addition, we introduce an evaluation metric to measure the similarity between the original and reconstructed samples. Evaluating a deep generative model for the synthesis of sound is a challenging task. Our approach is based on the accuracy of the generated frequencies as it presents a significant metric for the perception of harmonic sounds. This work is expected to accelerate future experiments on audio compression using neural autoencoders.  ( 2 min )
    PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes. (arXiv:2301.01514v1 [eess.SP] CROSS LISTED)
    Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separated by low-pass filtering. We combine the generalized quasi-norm ratio SOOT/SPOQ sparse penalties $\ell_p/\ell_q$ with the BEADS ternary assisted source separation algorithm. This results in a both convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. It outperforms comparable methods, when applied to typically peaked analytical chemistry signals. Reproducible code is provided.  ( 2 min )
    Concrete Score Matching: Generalized Score Matching for Discrete Data. (arXiv:2211.00802v2 [cs.LG] UPDATED)
    Representing probability distributions by the gradient of their density functions has proven effective in modeling a wide range of continuous data modalities. However, this representation is not applicable in discrete domains where the gradient is undefined. To this end, we propose an analogous score function called the "Concrete score", a generalization of the (Stein) score for discrete settings. Given a predefined neighborhood structure, the Concrete score of any input is defined by the rate of change of the probabilities with respect to local directional changes of the input. This formulation allows us to recover the (Stein) score in continuous domains when measuring such changes by the Euclidean distance, while using the Manhattan distance leads to our novel score function in discrete domains. Finally, we introduce a new framework to learn such scores from samples called Concrete Score Matching (CSM), and propose an efficient training objective to scale our approach to high dimensions. Empirically, we demonstrate the efficacy of CSM on density estimation tasks on a mixture of synthetic, tabular, and high-dimensional image datasets, and demonstrate that it performs favorably relative to existing baselines for modeling discrete data.  ( 2 min )
    Multimodal learning with graphs. (arXiv:2209.03299v5 [cs.LG] UPDATED)
    Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases: the set of assumptions that algorithms use to make predictions for inputs they have not encountered during training. Learning on multimodal datasets presents fundamental challenges because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, multimodal graph AI methods combine different modalities while leveraging cross-modal dependencies using graphs. Diverse datasets are combined using graphs and fed into sophisticated multimodal architectures, specified as image-intensive, knowledge-grounded and language-intensive models. Using this categorization, we introduce a blueprint for multimodal graph learning, use it to study existing methods and provide guidelines to design new models.  ( 2 min )
    Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning. (arXiv:2206.02450v2 [cs.IT] UPDATED)
    Gradient coding schemes effectively mitigate full stragglers in distributed learning by introducing identical redundancy in coded local partial derivatives corresponding to all model parameters. However, they are no longer effective for partial stragglers as they cannot utilize incomplete computation results from partial stragglers. This paper aims to design a new gradient coding scheme for mitigating partial stragglers in distributed learning. Specifically, we consider a distributed system consisting of one master and N workers, characterized by a general partial straggler model and focuses on solving a general large-scale machine learning problem with L model parameters using gradient coding. First, we propose a coordinate gradient coding scheme with L coding parameters representing L possibly different diversities for the L coordinates, which generates most gradient coding schemes. Then, we consider the minimization of the expected overall runtime and the maximization of the completion probability with respect to the L coding parameters for coordinates, which are challenging discrete optimization problems. To reduce computational complexity, we first transform each to an equivalent but much simpler discrete problem with N\llL variables representing the partition of the L coordinates into N blocks, each with identical redundancy. This indicates an equivalent but more easily implemented block coordinate gradient coding scheme with N coding parameters for blocks. Then, we adopt continuous relaxation to further reduce computational complexity. For the resulting minimization of expected overall runtime, we develop an iterative algorithm of computational complexity O(N^2) to obtain an optimal solution and derive two closed-form approximate solutions both with computational complexity O(N). For the resultant maximization of the completion probability, we develop an iterative algorithm of...  ( 3 min )
    Hybrid quantum-classical convolutional neural networks to improve molecular protein binding affinity predictions. (arXiv:2301.06331v2 [quant-ph] UPDATED)
    One of the main challenges in drug discovery is to find molecules that bind specifically and strongly to their target protein while having minimal binding to other proteins. By predicting binding affinity, it is possible to identify the most promising candidates from a large pool of potential compounds, reducing the number of compounds that need to be tested experimentally. Recently, deep learning methods have shown superior performance than traditional computational methods for making accurate predictions on large datasets. However, the complexity and time-consuming nature of these methods have limited their usage and development. Quantum machine learning is an emerging technology that has the potential to improve many classical machine learning algorithms. In this work we present a hybrid quantum-classical convolutional neural network, which is able to reduce by 20% the complexity of the classical network while maintaining optimal performance in the predictions. Additionally, it results in a significant time savings of up to 40% in the training process, which means a meaningful speed up of the drug discovery process.  ( 2 min )
    Consistent Non-Parametric Methods for Maximizing Robustness. (arXiv:2102.09086v3 [cs.LG] UPDATED)
    Learning classifiers that are robust to adversarial examples has received a great deal of recent attention. A major drawback of the standard robust learning framework is there is an artificial robustness radius $r$ that applies to all inputs. This ignores the fact that data may be highly heterogeneous, in which case it is plausible that robustness regions should be larger in some regions of data, and smaller in others. In this paper, we address this limitation by proposing a new limit classifier, called the neighborhood optimal classifier, that extends the Bayes optimal classifier outside its support by using the label of the closest in-support point. We then argue that this classifier maximizes the size of its robustness regions subject to the constraint of having accuracy equal to the Bayes optimal. We then present sufficient conditions under which general non-parametric methods that can be represented as weight functions converge towards this limit, and show that both nearest neighbors and kernel classifiers satisfy them under certain conditions.  ( 2 min )
    ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training. (arXiv:2301.07482v1 [cs.LG])
    A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU. Due to limited GPU memory, expensive data movement is necessary to facilitate the storage of these features on alternative devices with slower access (e.g. CPU memory). Moreover, the irregularity of graph structures contributes to poor data locality which further exacerbates the problem. Consequently, existing frameworks capable of efficiently training large GNN models usually incur a significant accuracy degradation because of the inevitable shortcuts involved. To address these limitations, we instead propose ReFresh, a general-purpose GNN mini-batch training framework that leverages a historical cache for storing and reusing GNN node embeddings instead of re-computing them through fetching raw features at every iteration. Critical to its success, the corresponding cache policy is designed, using a combination of gradient-based and staleness criteria, to selectively screen those embeddings which are relatively stable and can be cached, from those that need to be re-computed to reduce estimation errors and subsequent downstream accuracy loss. When paired with complementary system enhancements to support this selective historical cache, ReFresh is able to accelerate the training speed on large graph datasets such as ogbn-papers100M and MAG240M by 4.6x up to 23.6x and reduce the memory access by 64.5% (85.7% higher than a raw feature cache), with less than 1% influence on test accuracy.  ( 2 min )
    Strong inductive biases provably prevent harmless interpolation. (arXiv:2301.07605v1 [stat.ML])
    Classical wisdom suggests that estimators should avoid fitting noise to achieve good generalization. In contrast, modern overparameterized models can yield small test error despite interpolating noise -- a phenomenon often called "benign overfitting" or "harmless interpolation". This paper argues that the degree to which interpolation is harmless hinges upon the strength of an estimator's inductive bias, i.e., how heavily the estimator favors solutions with a certain structure: while strong inductive biases prevent harmless interpolation, weak inductive biases can even require fitting noise to generalize well. Our main theoretical result establishes tight non-asymptotic bounds for high-dimensional kernel regression that reflect this phenomenon for convolutional kernels, where the filter size regulates the strength of the inductive bias. We further provide empirical evidence of the same behavior for deep neural networks with varying filter sizes and rotational invariance.  ( 2 min )
    What relations are reliably embeddable in Euclidean space?. (arXiv:1903.05347v3 [cs.LG] UPDATED)
    We consider the problem of embedding a relation, represented as a directed graph, into Euclidean space. For three types of embeddings motivated by the recent literature on knowledge graphs, we obtain characterizations of which relations they are able to capture, as well as bounds on the minimal dimensionality and precision needed.  ( 2 min )
    Neural Network Quantization for Efficient Inference: A Survey. (arXiv:2112.06126v2 [cs.LG] UPDATED)
    As neural networks have become more powerful, there has been a rising desire to deploy them in the real world; however, the power and accuracy of neural networks is largely due to their depth and complexity, making them difficult to deploy, especially in resource-constrained devices. Neural network quantization has recently arisen to meet this demand of reducing the size and complexity of neural networks by reducing the precision of a network. With smaller and simpler networks, it becomes possible to run neural networks within the constraints of their target hardware. This paper surveys the many neural network quantization techniques that have been developed in the last decade. Based on this survey and comparison of neural network quantization techniques, we propose future directions of research in the area.  ( 2 min )
    Prediction of Red Wine Quality Using One-dimensional Convolutional Neural Networks. (arXiv:2208.14008v2 [cs.LG] UPDATED)
    As an alcoholic beverage, wine has remained prevalent for thousands of years, and the quality assessment of wines has been significant in wine production and trade. Scholars have proposed various deep learning and machine learning algorithms for wine quality prediction, such as Support vector machine (SVM), Random Forest (RF), K-nearest neighbors (KNN), Deep neural network (DNN), and Logistic regression (LR). However, these methods ignore the inner relationship between the physical and chemical properties of the wine, for example, the correlations between pH values, fixed acidity, citric acid, and so on. To fill the gap, this paper conducts the Pearson correlation analysis, PCA analysis, and Shapiro-Wilk test on those properties and incorporates 1D-CNN architecture to capture the correlations among neighboring features. In addition, it implemented dropout and batch normalization techniques to improve the robustness of the proposed model. Massive experiments have shown that our method can outperform baseline approaches in wine quality prediction. Moreover, ablation experiments also demonstrate the effectiveness of incorporating the 1-D CNN module, Dropout, and normalization techniques.  ( 2 min )
    Performance-Preserving Event Log Sampling for Predictive Monitoring. (arXiv:2301.07624v1 [cs.LG])
    Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy.  ( 2 min )
    Concentration of polynomial random matrices via Efron-Stein inequalities. (arXiv:2209.02655v2 [cs.CC] UPDATED)
    Analyzing concentration of large random matrices is a common task in a wide variety of fields. Given independent random variables, many tools are available to analyze random matrices whose entries are linear in the variables, e.g. the matrix-Bernstein inequality. However, in many applications, we need to analyze random matrices whose entries are polynomials in the variables. These arise naturally in the analysis of spectral algorithms, e.g., Hopkins et al. [STOC 2016], Moitra-Wein [STOC 2019]; and in lower bounds for semidefinite programs based on the Sum of Squares hierarchy, e.g. Barak et al. [FOCS 2016], Jones et al. [FOCS 2021]. In this work, we present a general framework to obtain such bounds, based on the matrix Efron-Stein inequalities developed by Paulin-Mackey-Tropp [Annals of Probability 2016]. The Efron-Stein inequality bounds the norm of a random matrix by the norm of another simpler (but still random) matrix, which we view as arising by "differentiating" the starting matrix. By recursively differentiating, our framework reduces the main task to analyzing far simpler matrices. For Rademacher variables, these simpler matrices are in fact deterministic and hence, analyzing them is far easier. For general non-Rademacher variables, the task reduces to scalar concentration, which is much easier. Moreover, in the setting of polynomial matrices, our results generalize the work of Paulin-Mackey-Tropp. Using our basic framework, we recover known bounds in the literature for simple "tensor networks" and "dense graph matrices". Using our general framework, we derive bounds for "sparse graph matrices", which were obtained only recently by Jones et al. [FOCS 2021] using a nontrivial application of the trace power method, and was a core component in their work. We expect our framework to be helpful for other applications involving concentration phenomena for nonlinear random matrices.  ( 3 min )
    Multimodal Side-Tuning for Document Classification. (arXiv:2301.07502v1 [cs.LG])
    In this paper, we propose to exploit the side-tuning framework for multimodal document classification. Side-tuning is a methodology for network adaptation recently introduced to solve some of the problems related to previous approaches. Thanks to this technique it is actually possible to overcome model rigidity and catastrophic forgetting of transfer learning by fine-tuning. The proposed solution uses off-the-shelf deep learning architectures leveraging the side-tuning framework to combine a base model with a tandem of two side networks. We show that side-tuning can be successfully employed also when different data sources are considered, e.g. text and images in document classification. The experimental results show that this approach pushes further the limit for document classification accuracy with respect to the state of the art.  ( 2 min )
    Non-IID Quantum Federated Learning with One-shot Communication Complexity. (arXiv:2209.00768v2 [quant-ph] UPDATED)
    Federated learning refers to the task of machine learning based on decentralized data from multiple clients with secured data privacy. Recent studies show that quantum algorithms can be exploited to boost its performance. However, when the clients' data are not independent and identically distributed (IID), the performance of conventional federated algorithms is known to deteriorate. In this work, we explore the non-IID issue in quantum federated learning with both theoretical and numerical analysis. We further prove that a global quantum channel can be exactly decomposed into local channels trained by each client with the help of local density estimators. This observation leads to a general framework for quantum federated learning on non-IID data with one-shot communication complexity. Numerical simulations show that the proposed algorithm outperforms the conventional ones significantly under non-IID settings.  ( 2 min )
    Targeted Image Reconstruction by Sampling Pre-trained Diffusion Model. (arXiv:2301.07557v1 [cs.LG])
    A trained neural network model contains information on the training data. Given such a model, malicious parties can leverage the "knowledge" in this model and design ways to print out any usable information (known as model inversion attack). Therefore, it is valuable to explore the ways to conduct a such attack and demonstrate its severity. In this work, we proposed ways to generate a data point of the target class without prior knowledge of the exact target distribution by using a pre-trained diffusion model.  ( 2 min )
    A Novel, Scale-Invariant, Differentiable, Efficient, Scalable Regularizer. (arXiv:2301.07285v1 [cs.LG])
    $L_{p}$-norm regularization schemes such as $L_{0}$, $L_{1}$, and $L_{2}$-norm regularization and $L_{p}$-norm-based regularization techniques such as weight decay and group LASSO compute a quantity which de pends on model weights considered in isolation from one another. This paper describes a novel regularizer which is not based on an $L_{p}$-norm. In contrast with $L_{p}$-norm-based regularization, this regularizer is concerned with the spatial arrangement of weights within a weight matrix. This regularizer is an additive term for the loss function and is differentiable, simple and fast to compute, scale-invariant, requires a trivial amount of additional memory, and can easily be parallelized. Empirically this method yields approximately a one order-of-magnitude improvement in the number of nonzero model parameters at a given level of accuracy.  ( 2 min )
    Reliable amortized variational inference with physics-based latent distribution correction. (arXiv:2207.11640v3 [stat.ML] UPDATED)
    Bayesian inference for high-dimensional inverse problems is computationally costly and requires selecting a suitable prior distribution. Amortized variational inference addresses these challenges via a neural network that approximates the posterior distribution not only for one instance of data, but a distribution of data pertaining to a specific inverse problem. During inference, the neural network -- in our case a conditional normalizing flow -- provides posterior samples at virtually no cost. However, the accuracy of amortized variational inference relies on the availability of high-fidelity training data, which seldom exists in geophysical inverse problems due to the Earth's heterogeneity. In addition, the network is prone to errors if evaluated over out-of-distribution data. As such, we propose to increase the resilience of amortized variational inference in the presence of moderate data distribution shifts. We achieve this via a correction to the latent distribution that improves the posterior distribution approximation for the data at hand. The correction involves relaxing the standard Gaussian assumption on the latent distribution and parameterizing it via a Gaussian distribution with an unknown mean and (diagonal) covariance. These unknowns are then estimated by minimizing the Kullback-Leibler divergence between the corrected and the (physics-based) true posterior distributions. While generic and applicable to other inverse problems, by means of a linearized seismic imaging example, we show that our correction step improves the robustness of amortized variational inference with respect to changes in the number of seismic sources, noise variance, and shifts in the prior distribution. This approach provides a seismic image with limited artifacts and an assessment of its uncertainty at approximately the same cost as five reverse-time migrations.  ( 2 min )
    Failure Tolerant Training with Persistent Memory Disaggregation over CXL. (arXiv:2301.07492v1 [cs.AR])
    This paper proposes TRAININGCXL that can efficiently process large-scale recommendation datasets in the pool of disaggregated memory while making training fault tolerant with low overhead. To this end, i) we integrate persistent memory (PMEM) and GPU into a cache-coherent domain as Type-2. Enabling CXL allows PMEM to be directly placed in GPU's memory hierarchy, such that GPU can access PMEM without software intervention. TRAININGCXL introduces computing and checkpointing logic near the CXL controller, thereby training data and managing persistency in an active manner. Considering PMEM's vulnerability, ii) we utilize the unique characteristics of recommendation models and take the checkpointing overhead off the critical path of their training. Lastly, iii) TRAININGCXL employs an advanced checkpointing technique that relaxes the updating sequence of model parameters and embeddings across training batches. The evaluation shows that TRAININGCXL achieves 5.2x training performance improvement and 76% energy savings, compared to the modern PMEM-based recommendation systems.  ( 2 min )
    Compression of GPS Trajectories using Autoencoders. (arXiv:2301.07420v1 [cs.LG])
    The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data. Several compression methods have been developed in order to reduce the amount of storage needed while keeping the important information. In this paper, we present an lstm-autoencoder based approach in order to compress and reconstruct GPS trajectories, which is evaluated on both a gaming and real-world dataset. We consider various compression ratios and trajectory lengths. The performance is compared to other trajectory compression algorithms, i.e., Douglas-Peucker. Overall, the results indicate that our approach outperforms Douglas-Peucker significantly in terms of the discrete Fr\'echet distance and dynamic time warping. Furthermore, by reconstructing every point lossy, the proposed methodology offers multiple advantages over traditional methods.  ( 2 min )
    No-substitution k-means Clustering with Adversarial Order. (arXiv:2012.14512v2 [cs.DS] UPDATED)
    We investigate $k$-means clustering in the online no-substitution setting when the input arrives in \emph{arbitrary} order. In this setting, points arrive one after another, and the algorithm is required to instantly decide whether to take the current point as a center before observing the next point. Decisions are irrevocable. The goal is to minimize both the number of centers and the $k$-means cost. Previous works in this setting assume that the input's order is random, or that the input's aspect ratio is bounded. It is known that if the order is arbitrary and there is no assumption on the input, then any algorithm must take all points as centers. Moreover, assuming a bounded aspect ratio is too restrictive -- it does not include natural input generated from mixture models. We introduce a new complexity measure that quantifies the difficulty of clustering a dataset arriving in arbitrary order. We design a new random algorithm and prove that if applied on data with complexity $d$, the algorithm takes $O(d\log(n) k\log(k))$ centers and is an $O(k^3)$-approximation. We also prove that if the data is sampled from a ``natural" distribution, such as a mixture of $k$ Gaussians, then the new complexity measure is equal to $O(k^2\log(n))$. This implies that for data generated from those distributions, our new algorithm takes only $\text{poly}(k\log(n))$ centers and is a $\text{poly}(k)$-approximation. In terms of negative results, we prove that the number of centers needed to achieve an $\alpha$-approximation is at least $\Omega\left(\frac{d}{k\log(n\alpha)}\right)$.  ( 2 min )
    Quantum-inspired tensor network for Earth science. (arXiv:2301.07528v1 [physics.geo-ph])
    Deep Learning (DL) is one of many successful methodologies to extract informative patterns and insights from ever increasing noisy large-scale datasets (in our case, satellite images). However, DL models consist of a few thousand to millions of training parameters, and these training parameters require tremendous amount of electrical power for extracting informative patterns from noisy large-scale datasets (e.g., computationally expensive). Hence, we employ a quantum-inspired tensor network for compressing trainable parameters of physics-informed neural networks (PINNs) in Earth science. PINNs are DL models penalized by enforcing the law of physics; in particular, the law of physics is embedded in DL models. In addition, we apply tensor decomposition to HyperSpectral Images (HSIs) to improve their spectral resolution. A quantum-inspired tensor network is also the native formulation to efficiently represent and train quantum machine learning models on big datasets on GPU tensor cores. Furthermore, the key contribution of this paper is twofold: (I) we reduced a number of trainable parameters of PINNs by using a quantum-inspired tensor network, and (II) we improved the spectral resolution of remotely-sensed images by employing tensor decomposition. As a benchmark PDE, we solved Burger's equation. As practical satellite data, we employed HSIs of Indian Pine, USA and of Pavia University, Italy.  ( 2 min )
    Landscape Complexity for the Empirical Risk of Generalized Linear Models. (arXiv:1912.02143v5 [stat.ML] UPDATED)
    We present a method to obtain the average and the typical value of the number of critical points of the empirical risk landscape for generalized linear estimation problems and variants. This represents a substantial extension of previous applications of the Kac-Rice method since it allows to analyze the critical points of high dimensional non-Gaussian random functions. Under a technical hypothesis, we obtain a rigorous explicit variational formula for the annealed complexity, which is the logarithm of the average number of critical points at fixed value of the empirical risk. This result is simplified, and extended, using the non-rigorous Kac-Rice replicated method from theoretical physics. In this way we find an explicit variational formula for the quenched complexity, which is generally different from its annealed counterpart, and allows to obtain the number of critical points for typical instances up to exponential accuracy.  ( 2 min )
    LIMEADE: From AI Explanations to Advice Taking. (arXiv:2003.04315v5 [cs.IR] UPDATED)
    Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow an AI to take advice from humans in response to explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA$^2$Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, little attention has been given to advice methods for opaque models. This paper introduces LIMEADE, the first general framework that translates both positive and negative advice (expressed using high-level vocabulary such as that employed by post-hoc explanations) into an update to an arbitrary, underlying opaque model. We demonstrate the generality of our approach with case studies on seventy real-world models across two broad domains: image classification and text recommendation. We show our method improves accuracy compared to a rigorous baseline on the image classification domains. For the text modality, we apply our framework to a neural recommender system for scientific papers on a public website; our user study shows that our framework leads to significantly higher perceived user control, trust, and satisfaction.  ( 2 min )
    A Robust Classification Framework for Byzantine-Resilient Stochastic Gradient Descent. (arXiv:2301.07498v1 [cs.LG])
    This paper proposes a Robust Gradient Classification Framework (RGCF) for Byzantine fault tolerance in distributed stochastic gradient descent. The framework consists of a pattern recognition filter which we train to be able to classify individual gradients as Byzantine by using their direction alone. This filter is robust to an arbitrary number of Byzantine workers for convex as well as non-convex optimisation settings, which is a significant improvement on the prior work that is robust to Byzantine faults only when up to 50% of the workers are Byzantine. This solution does not require an estimate of the number of Byzantine workers; its running time is not dependent on the number of workers and can scale up to training instances with a large number of workers without a loss in performance. We validate our solution by training convolutional neural networks on the MNIST dataset in the presence of Byzantine workers.  ( 2 min )
    Autonomous Slalom Maneuver Based on Expert Drivers' Behavior Using Convolutional Neural Network. (arXiv:2301.07424v1 [cs.RO])
    Lane changing and obstacle avoidance are one of the most important tasks in automated cars. To date, many algorithms have been suggested that are generally based on path trajectory or reinforcement learning approaches. Although these methods have been efficient, they are not able to accurately imitate a smooth path traveled by an expert driver. In this paper, a method is presented to mimic drivers' behavior using a convolutional neural network (CNN). First, seven features are extracted from a dataset gathered from four expert drivers in a driving simulator. Then, these features are converted from 1D arrays to 2D arrays and injected into a CNN. The CNN model computes the desired steering wheel angle and sends it to an adaptive PD controller. Finally, the control unit applies proper torque to the steering wheel. Results show that the CNN model can mimic the drivers' behavior with an R2-squared of 0.83. Also, the performance of the presented method was evaluated in the driving simulator for 17 trials, which avoided all traffic cones successfully. In some trials, the presented method performed a smoother maneuver compared to the expert drivers.  ( 2 min )
    A Survey of Advanced Computer Vision Techniques for Sports. (arXiv:2301.07583v1 [cs.CV])
    Computer Vision developments are enabling significant advances in many fields, including sports. Many applications built on top of Computer Vision technologies, such as tracking data, are nowadays essential for every top-level analyst, coach, and even player. In this paper, we survey Computer Vision techniques that can help many sports-related studies gather vast amounts of data, such as Object Detection and Pose Estimation. We provide a use case for such data: building a model for shot speed estimation with pose data obtained using only Computer Vision models. Our model achieves a correlation of 67%. The possibility of estimating shot speeds enables much deeper studies about enabling the creation of new metrics and recommendation systems that will help athletes improve their performance, in any sport. The proposed methodology is easily replicable for many technical movements and is only limited by the availability of video data.  ( 2 min )
    Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification. (arXiv:2301.07609v1 [stat.ML])
    Data-driven approaches coupled with physical knowledge are powerful techniques to model systems. The goal of such models is to efficiently solve for the underlying field by combining measurements with known physical laws. As many systems contain unknown elements, such as missing parameters, noisy data, or incomplete physical laws, this is widely approached as an uncertainty quantification problem. The common techniques to handle all the variables typically depend on the numerical scheme used to approximate the posterior, and it is desirable to have a method which is independent of any such discretization. Information field theory (IFT) provides the tools necessary to perform statistics over fields that are not necessarily Gaussian. We extend IFT to physics-informed IFT (PIFT) by encoding the functional priors with information about the physical laws which describe the field. The posteriors derived from this PIFT remain independent of any numerical scheme and can capture multiple modes, allowing for the solution of problems which are ill-posed. We demonstrate our approach through an analytical example involving the Klein-Gordon equation. We then develop a variant of stochastic gradient Langevin dynamics to draw samples from the joint posterior over the field and model parameters. We apply our method to numerical examples with various degrees of model-form error and to inverse problems involving nonlinear differential equations. As an addendum, the method is equipped with a metric which allows the posterior to automatically quantify model-form uncertainty. Because of this, our numerical experiments show that the method remains robust to even an incorrect representation of the physics given sufficient data. We numerically demonstrate that the method correctly identifies when the physics cannot be trusted, in which case it automatically treats learning the field as a regression problem.  ( 2 min )
    Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning. (arXiv:2301.07316v1 [cs.CV])
    Current deep learning models often suffer from catastrophic forgetting of old knowledge when continually learning new knowledge. Existing strategies to alleviate this issue often fix the trade-off between keeping old knowledge (stability) and learning new knowledge (plasticity). However, the stability-plasticity trade-off during continual learning may need to be dynamically changed for better model performance. In this paper, we propose two novel ways to adaptively balance model stability and plasticity. The first one is to adaptively integrate multiple levels of old knowledge and transfer it to each block level in the new model. The second one uses prediction uncertainty of old knowledge to naturally tune the importance of learning new knowledge during model training. To our best knowledge, this is the first time to connect model prediction uncertainty and knowledge distillation for continual learning. In addition, this paper applies a modified CutMix particularly to augment the data for old knowledge, further alleviating the catastrophic forgetting issue. Extensive evaluations on the CIFAR100 and the ImageNet datasets confirmed the effectiveness of the proposed method for continual learning.  ( 2 min )
    AutoFraudNet: A Multimodal Network to Detect Fraud in the Auto Insurance Industry. (arXiv:2301.07526v1 [cs.LG])
    In the insurance industry detecting fraudulent claims is a critical task with a significant financial impact. A common strategy to identify fraudulent claims is looking for inconsistencies in the supporting evidence. However, this is a laborious and cognitively heavy task for human experts as insurance claims typically come with a plethora of data from different modalities (e.g. images, text and metadata). To overcome this challenge, the research community has focused on multimodal machine learning frameworks that can efficiently reason through multiple data sources. Despite recent advances in multimodal learning, these frameworks still suffer from (i) challenges of joint-training caused by the different characteristics of different modalities and (ii) overfitting tendencies due to high model complexity. In this work, we address these challenges by introducing a multimodal reasoning framework, AutoFraudNet (Automobile Insurance Fraud Detection Network), for detecting fraudulent auto-insurance claims. AutoFraudNet utilizes a cascaded slow fusion framework and state-of-the-art fusion block, BLOCK Tucker, to alleviate the challenges of joint-training. Furthermore, it incorporates a light-weight architectural design along with additional losses to prevent overfitting. Through extensive experiments conducted on a real-world dataset, we demonstrate: (i) the merits of multimodal approaches, when compared to unimodal and bimodal methods, and (ii) the effectiveness of AutoFraudNet in fusing various modalities to boost performance (over 3\% in PR AUC).  ( 2 min )
    Training Semantic Segmentation on Heterogeneous Datasets. (arXiv:2301.07634v1 [cs.CV])
    We explore semantic segmentation beyond the conventional, single-dataset homogeneous training and bring forward the problem of Heterogeneous Training of Semantic Segmentation (HTSS). HTSS involves simultaneous training on multiple heterogeneous datasets, i.e. datasets with conflicting label spaces and different (weak) annotation types from the perspective of semantic segmentation. The HTSS formulation exposes deep networks to a larger and previously unexplored aggregation of information that can potentially enhance semantic segmentation in three directions: i) performance: increased segmentation metrics on seen datasets, ii) generalization: improved segmentation metrics on unseen datasets, and iii) knowledgeability: increased number of recognizable semantic concepts. To research these benefits of HTSS, we propose a unified framework, that incorporates heterogeneous datasets in a single-network training pipeline following the established FCN standard. Our framework first curates heterogeneous datasets to bring them into a common format and then trains a single-backbone FCN on all of them simultaneously. To achieve this, it transforms weak annotations, which are incompatible with semantic segmentation, to per-pixel labels, and hierarchizes their label spaces into a universal taxonomy. The trained HTSS models demonstrate performance and generalization gains over a wide range of datasets and extend the inference label space entailing hundreds of semantic classes.  ( 2 min )
    Curvilinear object segmentation in medical images based on ODoS filter and deep learning network. (arXiv:2301.07475v1 [eess.IV])
    Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty for the complex segmentation task due to different issues like various image appearance, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on oriented derivative of stick (ODoS) filter and deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasis on developing deep architectures and ignore capturing the structural features of curvature objects, which may lead to unsatisfactory results. In consequence, a new approach that incorporates the ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. In which, the original image is considered as principal part to describe various image appearance and complex background illumination, the multi-step strategy is used to enhance contrast between curvilinear objects and their surrounding backgrounds, and the vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract varvious structural features for curvilinear object segmentation in medical images. The performance of the computational model was validated in experiments with publicly available DRIVE, STARE and CHASEDB1 datasets. Experimental results indicate that the presented model has yielded surprising results compared with some state-of-the-art methods.  ( 2 min )
    Model-free machine learning of conservation laws from data. (arXiv:2301.07503v1 [cs.LG])
    We present a machine learning based method for learning first integrals of systems of ordinary differential equations from given trajectory data. The method is model-free in that it does not require explicit knowledge of the underlying system of differential equations that generated the trajectories. As a by-product, once the first integrals have been learned, also the system of differential equations will be known. We illustrate our method by considering several classical problems from the mathematical sciences.  ( 2 min )
    Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions. (arXiv:2301.07496v1 [cs.LG])
    Schizophrenia (SCZ) is a brain disorder where different people experience different symptoms, such as hallucination, delusion, flat-talk, disorganized thinking, etc. In the long term, this can cause severe effects and diminish life expectancy by more than ten years. Therefore, early and accurate diagnosis of SCZ is prevalent, and modalities like structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and electroencephalogram (EEG) assist in witnessing the brain abnormalities of the patients. Moreover, for accurate diagnosis of SCZ, researchers have used machine learning (ML) algorithms for the past decade to distinguish the brain patterns of healthy and SCZ brains using MRI and fMRI images. This paper seeks to acquaint SCZ researchers with ML and to discuss its recent applications to the field of SCZ study. This paper comprehensively reviews state-of-the-art techniques such as ML classifiers, artificial neural network (ANN), deep learning (DL) models, methodological fundamentals, and applications with previous studies. The motivation of this paper is to benefit from finding the research gaps that may lead to the development of a new model for accurate SCZ diagnosis. The paper concludes with the research finding, followed by the future scope that directly contributes to new research directions.  ( 2 min )
    Local Learning with Neuron Groups. (arXiv:2301.07635v1 [cs.LG])
    Traditional deep network training methods optimize a monolithic objective function jointly for all the components. This can lead to various inefficiencies in terms of potential parallelization. Local learning is an approach to model-parallelism that removes the standard end-to-end learning setup and utilizes local objective functions to permit parallel learning amongst model components in a deep network. Recent works have demonstrated that variants of local learning can lead to efficient training of modern deep networks. However, in terms of how much computation can be distributed, these approaches are typically limited by the number of layers in a network. In this work we propose to study how local learning can be applied at the level of splitting layers or modules into sub-components, adding a notion of width-wise modularity to the existing depth-wise modularity associated with local learning. We investigate local-learning penalties that permit such models to be trained efficiently. Our experiments on the CIFAR-10, CIFAR-100, and Imagenet32 datasets demonstrate that introducing width-level modularity can lead to computational advantages over existing methods based on local learning and opens new opportunities for improved model-parallel distributed training. Code is available at: https://github.com/adeetyapatel12/GN-DGL.  ( 2 min )
    Human-Timescale Adaptation in an Open-Ended Task Space. (arXiv:2301.07608v1 [cs.LG])
    Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL). In this work, we demonstrate that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans. In a vast space of held-out environment dynamics, our adaptive agent (AdA) displays on-the-fly hypothesis-driven exploration, efficient exploitation of acquired knowledge, and can successfully be prompted with first-person demonstrations. Adaptation emerges from three ingredients: (1) meta-reinforcement learning across a vast, smooth and diverse task distribution, (2) a policy parameterised as a large-scale attention-based memory architecture, and (3) an effective automated curriculum that prioritises tasks at the frontier of an agent's capabilities. We demonstrate characteristic scaling laws with respect to network size, memory length, and richness of the training task distribution. We believe our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains.  ( 2 min )
    Synthcity: facilitating innovative use cases of synthetic data in different data modalities. (arXiv:2301.07573v1 [cs.LG])
    Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi-source data, composite data, and more. Synthcity provides the practitioners with a single access point to cutting edge research and tools in synthetic data. It also offers the community a playground for rapid experimentation and prototyping, a one-stop-shop for SOTA benchmarks, and an opportunity for extending research impact. The library can be accessed on GitHub (https://github.com/vanderschaarlab/synthcity) and pip (https://pypi.org/project/synthcity/). We warmly invite the community to join the development effort by providing feedback, reporting bugs, and contributing code.  ( 2 min )
    Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction. (arXiv:2301.07286v1 [eess.IV])
    Robot-guided catheter insertion has the potential to deliver urgent medical care in situations where medical personnel are unavailable. However, this technique requires accurate and reliable segmentation of anatomical landmarks in the body. For the ultrasound imaging modality, obtaining large amounts of training data for a segmentation model is time-consuming and expensive. This paper introduces RESUS (RESlicing of UltraSound Images), a weak supervision data augmentation technique for ultrasound images based on slicing reconstructed 3D volumes from tracked 2D images. This technique allows us to generate views which cannot be easily obtained in vivo due to physical constraints of ultrasound imaging, and use these augmented ultrasound images to train a semantic segmentation model. We demonstrate that RESUS achieves statistically significant improvement over training with non-augmented images and highlight qualitative improvements through vessel reconstruction.  ( 2 min )
    Learning a Formality-Aware Japanese Sentence Representation. (arXiv:2301.07209v1 [cs.CL])
    While the way intermediate representations are generated in encoder-decoder sequence-to-sequence models typically allow them to preserve the semantics of the input sentence, input features such as formality might be left out. On the other hand, downstream tasks such as translation would benefit from working with a sentence representation that preserves formality in addition to semantics, so as to generate sentences with the appropriate level of social formality -- the difference between speaking to a friend versus speaking with a supervisor. We propose a sequence-to-sequence method for learning a formality-aware representation for Japanese sentences, where sentence generation is conditioned on both the original representation of the input sentence, and a side constraint which guides the sentence representation towards preserving formality information. Additionally, we propose augmenting the sentence representation with a learned representation of formality which facilitates the extraction of formality in downstream tasks. We address the lack of formality-annotated parallel data by adapting previous works on procedural formality classification of Japanese sentences. Experimental results suggest that our techniques not only helps the decoder recover the formality of the input sentence, but also slightly improves the preservation of input sentence semantics.  ( 2 min )
    Efficient correlation-based discretization of continuous variables for annealing machines. (arXiv:2301.07244v1 [quant-ph])
    Annealing machines specialized for combinatorial optimization problems have been developed, and some companies offer services to use those machines. Such specialized machines can only handle binary variables, and their input format is the quadratic unconstrained binary optimization (QUBO) formulation. Therefore, discretization is necessary to solve problems with continuous variables. However, there is a severe constraint on the number of binary variables with such machines. Although the simple binary expansion in the previous research requires many binary variables, we need to reduce the number of such variables in the QUBO formulation due to the constraint. We propose a discretization method that involves using correlations of continuous variables. We numerically show that the proposed method reduces the number of necessary binary variables in the QUBO formulation without a significant loss in prediction accuracy.  ( 2 min )
    Discrete Latent Structure in Neural Networks. (arXiv:2301.07473v1 [cs.LG])
    Many types of data from fields including natural language processing, computer vision, and bioinformatics, are well represented by discrete, compositional structures such as trees, sequences, or matchings. Latent structure models are a powerful tool for learning to extract such representations, offering a way to incorporate structural bias, discover insight about the data, and interpret decisions. However, effective training is challenging, as neural networks are typically designed for continuous computation. This text explores three broad strategies for learning with discrete latent structure: continuous relaxation, surrogate gradients, and probabilistic estimation. Our presentation relies on consistent notations for a wide range of models. As such, we reveal many new connections between latent structure learning strategies, showing how most consist of the same small set of fundamental building blocks, but use them differently, leading to substantially different applicability and properties.  ( 2 min )
    DDPEN: Trajectory Optimisation With Sub Goal Generation Model. (arXiv:2301.07433v1 [cs.RO])
    Differential dynamic programming (DDP) is a widely used and powerful trajectory optimization technique, however, due to its internal structure, it is not exempt from local minima. In this paper, we present Differential Dynamic Programming with Escape Network (DDPEN) - a novel approach to avoid DDP local minima by utilising an additional term used in the optimization criteria pointing towards the direction where robot should move in order to escape local minima. In order to produce the aforementioned directions, we propose to utilize a deep model that takes as an input the map of the environment in the form of a costmap together with the desired goal position. The Model produces possible future directions that will lead to the goal, avoiding local minima which is possible to run in real time conditions. The model is trained on a synthetic dataset and overall the system is evaluated at the Gazebo simulator. In this work we show that our proposed method allows avoiding local minima of trajectory optimization algorithm and successfully execute a trajectory 278 m long with various convex and nonconvex obstacles.  ( 2 min )
    DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training. (arXiv:2301.07421v1 [cs.LG])
    We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms. Building upon the concept of self-imitation learning (SIL), we introduce an imitation buffer to store beneficial trajectories generated by the policy determined by their return. A discriminator network is trained concurrently to the policy to distinguish between trajectories generated by the current policy and beneficial trajectories generated by previous policies. The discriminator's verdict is used to construct a reward signal for optimizing the policy. By interpolating prior experience, DIRECT is able to act as a surrogate, steering policy optimization towards more valuable regions of the reward landscape thus learning an optimal policy. Our results show that DIRECT outperforms state-of-the-art algorithms in sparse- and shifting-reward environments being able to provide a surrogate reward to the policy and direct the optimization towards valuable areas.  ( 2 min )
    Image Embedding for Denoising Generative Models. (arXiv:2301.07485v1 [cs.CV])
    Denoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent generative quality, and the solid probabilistic foundation. In this article, we address the problem of {\em embedding} an image into the latent space of Denoising Diffusion Models, that is finding a suitable ``noisy'' image whose denoising results in the original image. We particularly focus on Denoising Diffusion Implicit Models due to the deterministic nature of their reverse diffusion process. As a side result of our investigation, we gain a deeper insight into the structure of the latent space of diffusion models, opening interesting perspectives on its exploration, the definition of semantic trajectories, and the manipulation/conditioning of encodings for editing purposes. A particularly interesting property highlighted by our research, which is also characteristic of this class of generative models, is the independence of the latent representation from the networks implementing the reverse diffusion process. In other words, a common seed passed to different networks (each trained on the same dataset), eventually results in identical images.  ( 2 min )
    Optimistic Dynamic Regret Bounds. (arXiv:2301.07530v1 [cs.LG])
    Online Learning (OL) algorithms have originally been developed to guarantee good performances when comparing their output to the best fixed strategy. The question of performance with respect to dynamic strategies remains an active research topic. We develop in this work dynamic adaptations of classical OL algorithms based on the use of experts' advice and the notion of optimism. We also propose a constructivist method to generate those advices and eventually provide both theoretical and experimental guarantees for our procedures.  ( 2 min )
    PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav. (arXiv:2301.07302v1 [cs.LG])
    We study ObjectGoal Navigation - where a virtual robot situated in a new environment is asked to navigate to an object. Prior work has shown that imitation learning (IL) on a dataset of human demonstrations achieves promising results. However, this has limitations $-$ 1) IL policies generalize poorly to new states, since the training mimics actions not their consequences, and 2) collecting demonstrations is expensive. On the other hand, reinforcement learning (RL) is trivially scalable, but requires careful reward engineering to achieve desirable behavior. We present a two-stage learning scheme for IL pretraining on human demonstrations followed by RL-finetuning. This leads to a PIRLNav policy that advances the state-of-the-art on ObjectNav from $60.0\%$ success rate to $65.0\%$ ($+5.0\%$ absolute). Using this IL$\rightarrow$RL training recipe, we present a rigorous empirical analysis of design choices. First, we investigate whether human demonstrations can be replaced with `free' (automatically generated) sources of demonstrations, e.g. shortest paths (SP) or task-agnostic frontier exploration (FE) trajectories. We find that IL$\rightarrow$RL on human demonstrations outperforms IL$\rightarrow$RL on SP and FE trajectories, even when controlled for the same IL-pretraining success on TRAIN, and even on a subset of VAL episodes where IL-pretraining success favors the SP or FE policies. Next, we study how RL-finetuning performance scales with the size of the IL pretraining dataset. We find that as we increase the size of the IL-pretraining dataset and get to high IL accuracies, the improvements from RL-finetuning are smaller, and that $90\%$ of the performance of our best IL$\rightarrow$RL policy can be achieved with less than half the number of IL demonstrations. Finally, we analyze failure modes of our ObjectNav policies, and present guidelines for further improving them.  ( 2 min )
    Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction. (arXiv:2301.07568v1 [q-bio.BM])
    The goal of Protein Structure Prediction (PSP) problem is to predict a protein's 3D structure (confirmation) from its amino acid sequence. The problem has been a 'holy grail' of science since the Noble prize-winning work of Anfinsen demonstrated that protein conformation was determined by sequence. A recent and important step towards this goal was the development of AlphaFold2, currently the best PSP method. AlphaFold2 is probably the highest profile application of AI to science. Both AlphaFold2 and RoseTTAFold (another impressive PSP method) have been published and placed in the public domain (code & models). Stacking is a form of ensemble machine learning ML in which multiple baseline models are first learnt, then a meta-model is learnt using the outputs of the baseline level model to form a model that outperforms the base models. Stacking has been successful in many applications. We developed the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold. ARStack significantly outperforms AlphaFold2. We rigorously demonstrate this using two sets of non-homologous proteins, and a test set of protein structures published after that of AlphaFold2 and RoseTTAFold. As more high quality prediction methods are published it is likely that ensemble methods will increasingly outperform any single method.  ( 2 min )
    Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness. (arXiv:2301.07487v1 [cs.LG])
    Learning from raw high dimensional data via interaction with a given environment has been effectively achieved through the utilization of deep neural networks. Yet the observed degradation in policy performance caused by imperceptible worst-case policy dependent translations along high sensitivity directions (i.e. adversarial perturbations) raises concerns on the robustness of deep reinforcement learning policies. In our paper, we show that these high sensitivity directions do not lie only along particular worst-case directions, but rather are more abundant in the deep neural policy landscape and can be found via more natural means in a black-box setting. Furthermore, we show that vanilla training techniques intriguingly result in learning more robust policies compared to the policies learnt via the state-of-the-art adversarial training techniques. We believe our work lays out intriguing properties of the deep reinforcement learning policy manifold and our results can help to build robust and generalizable deep reinforcement learning policies.  ( 2 min )
    Learning Deformation Trajectories of Boltzmann Densities. (arXiv:2301.07388v1 [stat.ML])
    We introduce a training objective for continuous normalizing flows that can be used in the absence of samples but in the presence of an energy function. Our method relies on either a prescribed or a learnt interpolation $f_t$ of energy functions between the target energy $f_1$ and the energy function of a generalized Gaussian $f_0(x) = (|x|/\sigma)^p$. This then induces an interpolation of Boltzmann densities $p_t \propto e^{-f_t}$ and we aim to find a time-dependent vector field $V_t$ that transports samples along this family of densities. Concretely, this condition can be translated to a PDE between $V_t$ and $f_t$ and we minimize the amount by which this PDE fails to hold. We compare this objective to the reverse KL-divergence on Gaussian mixtures and on the $\phi^4$ lattice field theory on a circle.  ( 2 min )
    Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy. (arXiv:2301.07474v1 [cs.CR])
    In this article, we propose the Artificial Intelligence Security Taxonomy to systematize the knowledge of threats, vulnerabilities, and security controls of ML-based systems. We first classify the damage caused by attacks against ML-based systems, define ML-specific security, and discuss its characteristics. Next, we enumerate all relevant assets and stakeholders and provide a general taxonomy for ML-specific threats. Then, we collect a wide range of security controls against ML-specific threats through an extensive review of recent literature. Finally, we classify the vulnerabilities and controls of an ML-based system in terms of each vulnerable asset in the system's entire lifecycle.  ( 2 min )
    PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection. (arXiv:2301.07301v1 [cs.CV])
    In autonomous driving, 3D object detection based on multi-modal data has become an indispensable approach when facing complex environments around the vehicle. During multi-modal detection, LiDAR and camera are simultaneously applied for capturing and modeling. However, due to the intrinsic discrepancies between the LiDAR point and camera image, the fusion of the data for object detection encounters a series of problems. Most multi-modal detection methods perform even worse than LiDAR-only methods. In this investigation, we propose a method named PTA-Det to improve the performance of multi-modal detection. Accompanied by PTA-Det, a Pseudo Point Cloud Generation Network is proposed, which can convert image information including texture and semantic features by pseudo points. Thereafter, through a transformer-based Point Fusion Transition (PFT) module, the features of LiDAR points and pseudo points from image can be deeply fused under a unified point-based representation. The combination of these modules can conquer the major obstacle in feature fusion across modalities and realizes a complementary and discriminative representation for proposal generation. Extensive experiments on the KITTI dataset show the PTA-Det achieves a competitive result and support its effectiveness.  ( 2 min )
    Causal Falsification of Digital Twins. (arXiv:2301.07210v1 [stat.ME])
    Digital twins hold substantial promise in many applications, but rigorous procedures for assessing their accuracy are essential for their widespread deployment in safety-critical settings. By formulating this task within the framework of causal inference, we show it is not possible to certify that a twin is "correct" using real-world observational data unless potentially tenuous assumptions are made about the data-generating process. To avoid these assumptions, we propose an assessment strategy that instead aims to find cases where the twin is not correct, and present a general-purpose statistical procedure for doing so that may be used across a wide variety of applications and twin models. Our approach yields reliable and actionable information about the twin under only the assumption of an i.i.d. dataset of real-world observations, and in particular remains sound even in the presence of arbitrary unmeasured confounding. We demonstrate the effectiveness of our methodology via a large-scale case study involving sepsis modelling within the Pulse Physiology Engine, which we assess using the MIMIC-III dataset of ICU patients.  ( 2 min )
    Improve Noise Tolerance of Robust Loss via Noise-Awareness. (arXiv:2301.07306v1 [cs.LG])
    Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current robust losses, however, inevitably involve hyperparameters to be tuned for different datasets with noisy labels, manually or heuristically through cross validation, which makes them fairly hard to be generally applied in practice. Existing robust loss methods usually assume that all training samples share common hyperparameters, which are independent of instances. This limits the ability of these methods on distinguishing individual noise properties of different samples, making them hardly adapt to different noise structures. To address above issues, we propose to assemble robust loss with instance-dependent hyperparameters to improve their noise-tolerance with theoretical guarantee. To achieve setting such instance-dependent hyperparameters for robust loss, we propose a meta-learning method capable of adaptively learning a hyperparameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster). Specifically, through mutual amelioration between hyperparameter prediction function and classifier parameters in our method, both of them can be simultaneously finely ameliorated and coordinated to attain solutions with good generalization capability. Four kinds of SOTA robust losses are attempted to be integrated with our algorithm, and experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and generalization performance. Meanwhile, the explicit parameterized structure makes the meta-learned prediction function capable of being readily transferrable and plug-and-play to unseen datasets with noisy labels. Specifically, we transfer our meta-learned NARL-Adjuster to unseen tasks, including several real noisy datasets, and achieve better performance compared with conventional hyperparameter tuning strategy.  ( 2 min )
    Tailor: Altering Skip Connections for Resource-Efficient Inference. (arXiv:2301.07247v1 [cs.CV])
    Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and off-chip memory utilization and bandwidth requirements. In this paper, we show that skip connections can be optimized for hardware when tackled with a hardware-software codesign approach. We argue that while a network's skip connections are needed for the network to learn, they can later be removed or shortened to provide a more hardware efficient implementation with minimal to no accuracy loss. We introduce Tailor, a codesign tool whose hardware-aware training algorithm gradually removes or shortens a fully trained network's skip connections to lower their hardware cost. The optimized hardware designs improve resource utilization by up to 34% for BRAMs, 13% for FFs, and 16% for LUTs.  ( 2 min )
    Detecting and Ranking Causal Anomalies in End-to-End Complex System. (arXiv:2301.07281v1 [cs.LG])
    With the rapid development of technology, the automated monitoring systems of large-scale factories are becoming more and more important. By collecting a large amount of machine sensor data, we can have many ways to find anomalies. We believe that the real core value of an automated monitoring system is to identify and track the cause of the problem. The most famous method for finding causal anomalies is RCA, but there are many problems that cannot be ignored. They used the AutoRegressive eXogenous (ARX) model to create a time-invariant correlation network as a machine profile, and then use this profile to track the causal anomalies by means of a method called fault propagation. There are two major problems in describing the behavior of a machine by using the correlation network established by ARX: (1) It does not take into account the diversity of states (2) It does not separately consider the correlations with different time-lag. Based on these problems, we propose a framework called Ranking Causal Anomalies in End-to-End System (RCAE2E), which completely solves the problems mentioned above. In the experimental part, we use synthetic data and real-world large-scale photoelectric factory data to verify the correctness and existence of our method hypothesis.  ( 2 min )
    Towards Models that Can See and Read. (arXiv:2301.07389v1 [cs.CV])
    Visual Question Answering (VQA) and Image Captioning (CAP), which are among the most popular vision-language tasks, have analogous scene-text versions that require reasoning from the text in the image. Despite the obvious resemblance between them, the two are treated independently, yielding task-specific methods that can either see or read, but not both. In this work, we conduct an in-depth analysis of this phenomenon and propose UniTNT, a Unified Text-Non-Text approach, which grants existing multimodal architectures scene-text understanding capabilities. Specifically, we treat scene-text information as an additional modality, fusing it with any pretrained encoder-decoder-based architecture via designated modules. Thorough experiments reveal that UniTNT leads to the first single model that successfully handles both task types. Moreover, we show that scene-text understanding capabilities can boost vision-language models' performance on VQA and CAP by up to 3.49% and 0.7 CIDEr, respectively.  ( 2 min )
    Relativistic Digital Twin: Bringing the IoT to the Future. (arXiv:2301.07390v1 [cs.NI])
    Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e. the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario.  ( 2 min )
    Complexity Analysis of a Countable-armed Bandit Problem. (arXiv:2301.07243v1 [cs.LG])
    We consider a stochastic multi-armed bandit (MAB) problem motivated by ``large'' action spaces, and endowed with a population of arms containing exactly $K$ arm-types, each characterized by a distinct mean reward. The decision maker is oblivious to the statistical properties of reward distributions as well as the population-level distribution of different arm-types, and is precluded also from observing the type of an arm after play. We study the classical problem of minimizing the expected cumulative regret over a horizon of play $n$, and propose algorithms that achieve a rate-optimal finite-time instance-dependent regret of $\mathcal{O}\left( \log n \right)$. We also show that the instance-independent (minimax) regret is $\tilde{\mathcal{O}}\left( \sqrt{n} \right)$ when $K=2$. While the order of regret and complexity of the problem suggests a great degree of similarity to the classical MAB problem, properties of the performance bounds and salient aspects of algorithm design are quite distinct from the latter, as are the key primitives that determine complexity along with the analysis tools needed to study them.  ( 2 min )
    Label Inference Attack against Split Learning under Regression Setting. (arXiv:2301.07284v1 [cs.CR])
    As a crucial building block in vertical Federated Learning (vFL), Split Learning (SL) has demonstrated its practice in the two-party model training collaboration, where one party holds the features of data samples and another party holds the corresponding labels. Such method is claimed to be private considering the shared information is only the embedding vectors and gradients instead of private raw data and labels. However, some recent works have shown that the private labels could be leaked by the gradients. These existing attack only works under the classification setting where the private labels are discrete. In this work, we step further to study the leakage in the scenario of the regression model, where the private labels are continuous numbers (instead of discrete labels in classification). This makes previous attacks harder to infer the continuous labels due to the unbounded output range. To address the limitation, we propose a novel learning-based attack that integrates gradient information and extra learning regularization objectives in aspects of model training properties, which can infer the labels under regression settings effectively. The comprehensive experiments on various datasets and models have demonstrated the effectiveness of our proposed attack. We hope our work can pave the way for future analyses that make the vFL framework more secure.  ( 2 min )
    Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining. (arXiv:2301.07295v1 [cs.CL])
    In recent years, neural models learned through self-supervised pretraining on large scale multilingual text or speech data have exhibited promising results for underresourced languages, especially when a relatively large amount of data from related language(s) is available. While the technology has a potential for facilitating tasks carried out in language documentation projects, such as speech transcription, pretraining a multilingual model from scratch for every new language would be highly impractical. We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language, focusing on actual fieldwork data from a critically endangered tongue: Ainu. Specifically, we (i) examine the feasibility of leveraging data from similar languages also in fine-tuning; (ii) verify whether the model's performance can be improved by further pretraining on target language data. Our results show that continued pretraining is the most effective method to adapt a wav2vec 2.0 model for a new language and leads to considerable reduction in error rates. Furthermore, we find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance when there is very little labeled data in the target language.  ( 2 min )
    A variational autoencoder-based nonnegative matrix factorisation model for deep dictionary learning. (arXiv:2301.07272v1 [cs.LG])
    Construction of dictionaries using nonnegative matrix factorisation (NMF) has extensive applications in signal processing and machine learning. With the advances in deep learning, training compact and robust dictionaries using deep neural networks, i.e., dictionaries of deep features, has been proposed. In this study, we propose a probabilistic generative model which employs a variational autoencoder (VAE) to perform nonnegative dictionary learning. In contrast to the existing VAE models, we cast the model under a statistical framework with latent variables obeying a Gamma distribution and design a new loss function to guarantee the nonnegative dictionaries. We adopt an acceptance-rejection sampling reparameterization trick to update the latent variables iteratively. We apply the dictionaries learned from VAE-NMF to two signal processing tasks, i.e., enhancement of speech and extraction of muscle synergies. Experimental results demonstrate that VAE-NMF performs better in learning the latent nonnegative dictionaries in comparison with state-of-the-art methods.  ( 2 min )
    Tracking Brand-Associated Polarity-Bearing Topics in User Reviews. (arXiv:2301.07183v1 [cs.IR])
    Monitoring online customer reviews is important for business organisations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organised in temporally-ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.  ( 2 min )
    Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data. (arXiv:2301.07206v1 [stat.ML])
    Relating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features. When high-dimensional problems arise, dimension reduction techniques can be used. Most notable are projections (e.g. Partial Least Squares or PLS ) or variable selections (e.g. lasso). Sparse partial least squares combine both strategies, by blending variable selection into PLS. The variant presented in this paper, Dual-sPLS, generalizes the classical PLS1 algorithm. It provides balance between accurate prediction and efficient interpretation. It is based on penalizations inspired by classical regression methods (lasso, group lasso, least squares, ridge) and uses the dual norm notion. The resulting sparsity is enforced by an intuitive shrinking ratio parameter. Dual-sPLS favorably compares to similar regression methods, on simulated and real chemical data. Code is provided as an open-source package in R: \url{https://CRAN.R-project.org/package=dual.spls}.  ( 2 min )
    Scaffold-Based Multi-Objective Drug Candidate Optimization. (arXiv:2301.07175v1 [q-bio.BM])
    Multiparameter optimization (MPO) provides a means to assess and balance several variables based on their importance to the overall objective. However, using MPO methods in therapeutic discovery is challenging due to the number of cheminformatics properties required to find an optimal solution. High throughput virtual screening to identify hit candidates produces a large amount of data with conflicting properties. For instance, toxicity and binding affinity can contradict each other and cause improbable levels of toxicity that can lead to adverse effects. Instead of using the exhaustive method of treating each property, multiple properties can be combined into a single MPO score, with weights assigned for each property. This desirability score also lends itself well to ML applications that can use the score in the loss function. In this work, we will discuss scaffold focused graph-based Markov chain monte carlo framework built to generate molecules with optimal properties. This framework trains itself on-the-fly with the MPO score of each iteration of molecules, and is able to work on a greater number of properties and sample the chemical space around a starting scaffold. Results are compared to the chemical Transformer model molGCT to judge performance between graph and natural language processing approaches.  ( 2 min )
    Artificial Neuronal Ensembles with Learned Context Dependent Gating. (arXiv:2301.07187v1 [cs.LG])
    Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural networks suffer from catastrophic forgetting, where the network's performance rapidly deteriorates as tasks are learned sequentially. By contrast, sequential learning is possible for a range of biological organisms. We introduce Learned Context Dependent Gating (LXDG), a method to flexibly allocate and recall `artificial neuronal ensembles', using a particular network structure and a new set of regularization terms. Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. The gates are outputs of networks themselves, trained with a sigmoid output activation. The regularization terms we have introduced correspond to properties exhibited by biological neuronal ensembles. The first term penalizes low gate sparsity, ensuring that only a specified fraction of the network is used. The second term ensures that previously learned gates are recalled when the network is presented with input from previously learned tasks. Finally, there is a regularization term responsible for ensuring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones. We demonstrate the ability of this method to alleviate catastrophic forgetting on continual learning benchmarks. When the new regularization terms are included in the model along with Elastic Weight Consolidation (EWC) it achieves better performance on the benchmark `permuted MNIST' than with EWC alone. The benchmark `rotated MNIST' demonstrates how similar tasks recruit similar neurons to the artificial neuronal ensemble.  ( 2 min )
    Revisiting mass-radius relationships for exoplanet populations: a machine learning insight. (arXiv:2301.07143v1 [astro-ph.EP])
    The growing number of exoplanet discoveries and advances in machine learning techniques allow us to find, explore, and understand characteristics of these new worlds beyond our Solar System. We analyze the dataset of 762 confirmed exoplanets and eight Solar System planets using efficient machine-learning approaches to characterize their fundamental quantities. By adopting different unsupervised clustering algorithms, the data are divided into two main classes: planets with $\log R_{p}\leq0.91R_{\oplus}$ and $\log M_{p}\leq1.72M_{\oplus}$ as class 1 and those with $\log R_{p}>0.91R_{\oplus}$ and $\log M_{p}>1.72M_{\oplus}$ as class 2. Various regression models are used to reveal correlations between physical parameters and evaluate their performance. We find that planetary mass, orbital period, and stellar mass play preponderant roles in predicting exoplanet radius. The validation metrics (RMSE, MAE, and $R^{2}$) suggest that the Support Vector Regression has, by and large, better performance than other models and is a promising model for obtaining planetary radius. Not only do we improve the prediction accuracy in logarithmic space, but also we derive parametric equations using the M5P and Markov Chain Monte Carlo methods. Planets of class 1 are shown to be consistent with a positive linear mass-radius relation, while for planets of class 2, the planetary radius represents a strong correlation with their host stars' masses.  ( 2 min )
    Heterogeneous Multi-Robot Reinforcement Learning. (arXiv:2301.07137v1 [cs.RO])
    Cooperative multi-robot tasks can benefit from heterogeneity in the robots' physical and behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL) frameworks lack the ability to explicitly accommodate policy heterogeneity, and typically constrain agents to share neural network parameters. This enforced homogeneity limits application in cases where the tasks benefit from heterogeneous behaviors. In this paper, we crystallize the role of heterogeneity in MARL policies. Towards this end, we introduce Heterogeneous Graph Neural Network Proximal Policy Optimization (HetGPPO), a paradigm for training heterogeneous MARL policies that leverages a Graph Neural Network for differentiable inter-agent communication. HetGPPO allows communicating agents to learn heterogeneous behaviors while enabling fully decentralized training in partially observable environments. We complement this with a taxonomical overview that exposes more heterogeneity classes than previously identified. To motivate the need for our model, we present a characterization of techniques that homogeneous models can leverage to emulate heterogeneous behavior, and show how this "apparent heterogeneity" is brittle in real-world conditions. Through simulations and real-world experiments, we show that: (i) when homogeneous methods fail due to strong heterogeneous requirements, HetGPPO succeeds, and, (ii) when homogeneous methods are able to learn apparently heterogeneous behaviors, HetGPPO achieves higher resilience to both training and deployment noise.  ( 2 min )
    Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset. (arXiv:2301.07107v1 [cs.LG])
    Objective: Peritoneal Dialysis (PD) is one of the most widely used life-supporting therapies for patients with End-Stage Renal Disease (ESRD). Predicting mortality risk and identifying modifiable risk factors based on the Electronic Medical Records (EMR) collected along with the follow-up visits are of great importance for personalized medicine and early intervention. Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare. Method and Materials: Our proposed model consists of a multi-channel feature extraction module and an adaptive feature importance recalibration module. AICare explicitly identifies the key features that strongly indicate the outcome prediction for each patient to build the health status embedding individually. This study has collected 13,091 clinical follow-up visits and demographic data of 656 PD patients. To verify the application universality, this study has also collected 4,789 visits of 1,363 hemodialysis dialysis (HD) as an additional experiment dataset to test the prediction performance, which will be discussed in the Appendix. Results: 1) Experiment results show that AICare achieves 81.6%/74.3% AUROC and 47.2%/32.5% AUPRC for the 1-year mortality prediction task on PD/HD dataset respectively, which outperforms the state-of-the-art comparative deep learning models. 2) This study first provides a comprehensive elucidation of the relationship between the causes of mortality in patients with PD and clinical features based on an end-to-end deep learning model. 3) This study first reveals the pattern of variation in the importance of each feature in the mortality prediction based on built-in interpretability. 4) We develop a practical AI-Doctor interaction system to visualize the trajectory of patients' health status and risk indicators.  ( 3 min )
    Genetic Imitation Learning by Reward Extrapolation. (arXiv:2301.07182v1 [cs.NE])
    Imitation learning demonstrates remarkable performance in various domains. However, imitation learning is also constrained by many prerequisites. The research community has done intensive research to alleviate these constraints, such as adding the stochastic policy to avoid unseen states, eliminating the need for action labels, and learning from the suboptimal demonstrations. Inspired by the natural reproduction process, we proposed a method called GenIL that integrates the Genetic Algorithm with imitation learning. The involvement of the Genetic Algorithm improves the data efficiency by reproducing trajectories with various returns and assists the model in estimating more accurate and compact reward function parameters. We tested GenIL in both Atari and Mujoco domains, and the result shows that it successfully outperforms the previous extrapolation methods over extrapolation accuracy, robustness, and overall policy performance when input data is limited.  ( 2 min )
    A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles. (arXiv:2301.07156v1 [cs.LG])
    In this work, we address the problem of long-distance navigation for battery electric vehicles (BEVs), where one or more charging sessions are required to reach the intended destination. We consider the availability and performance of the charging stations to be unknown and stochastic, and develop a combinatorial semi-bandit framework for exploring the road network to learn the parameters of the queue time and charging power distributions. Within this framework, we first outline a pre-processing for the road network graph to handle the constrained combinatorial optimization problem in an efficient way. Then, for the pre-processed graph, we use a Bayesian approach to model the stochastic edge weights, utilizing conjugate priors for the one-parameter exponential and two-parameter gamma distributions, the latter of which is novel to multi-armed bandit literature. Finally, we apply combinatorial versions of Thompson Sampling, BayesUCB and Epsilon-greedy to the problem. We demonstrate the performance of our framework on long-distance navigation problem instances in country-sized road networks, with simulation experiments in Norway, Sweden and Finland.  ( 2 min )
    Large Deviations for Classification Performance Analysis of Machine Learning Systems. (arXiv:2301.07104v1 [cs.LG])
    We study the performance of machine learning binary classification techniques in terms of error probabilities. The statistical test is based on the Data-Driven Decision Function (D3F), learned in the training phase, i.e., what is thresholded before the final binary decision is made. Based on large deviations theory, we show that under appropriate conditions the classification error probabilities vanish exponentially, as $\sim \exp\left(-n\,I + o(n) \right)$, where $I$ is the error rate and $n$ is the number of observations available for testing. We also propose two different approximations for the error probability curves, one based on a refined asymptotic formula (often referred to as exact asymptotics), and another one based on the central limit theorem. The theoretical findings are finally tested using the popular MNIST dataset.  ( 2 min )
    Continuous Trajectory Generation Based on Two-Stage GAN. (arXiv:2301.07103v1 [cs.LG])
    Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geographic privacy protect. Although many previous works have studied the problem of trajectory generation, the continuity of the generated trajectories has been neglected, which makes these methods useless for practical urban simulation scenarios. To solve this problem, we propose a novel two-stage generative adversarial framework to generate the continuous trajectory on the road network, namely TS-TrajGen, which efficiently integrates prior domain knowledge of human mobility with model-free learning paradigm. Specifically, we build the generator under the human mobility hypothesis of the A* algorithm to learn the human mobility behavior. For the discriminator, we combine the sequential reward with the mobility yaw reward to enhance the effectiveness of the generator. Finally, we propose a novel two-stage generation process to overcome the weak point of the existing stochastic generation process. Extensive experiments on two real-world datasets and two case studies demonstrate that our framework yields significant improvements over the state-of-the-art methods.  ( 2 min )
    On Using Deep Learning Proxies as Forward Models in Deep Learning Problems. (arXiv:2301.07102v1 [cs.LG])
    Physics-based optimization problems are generally very time-consuming, especially due to the computational complexity associated with the forward model. Recent works have demonstrated that physics-modelling can be approximated with neural networks. However, there is always a certain degree of error associated with this learning, and we study this aspect in this paper. We demonstrate through experiments on popular mathematical benchmarks, that neural network approximations (NN-proxies) of such functions when plugged into the optimization framework, can lead to erroneous results. In particular, we study the behavior of particle swarm optimization and genetic algorithm methods and analyze their stability when coupled with NN-proxies. The correctness of the approximate model depends on the extent of sampling conducted in the parameter space, and through numerical experiments, we demonstrate that caution needs to be taken when constructing this landscape with neural networks. Further, the NN-proxies are hard to train for higher dimensional functions, and we present our insights for 4D and 10D problems. The error is higher for such cases, and we demonstrate that it is sensitive to the choice of the sampling scheme used to build the NN-proxy. The code is available at https://github.com/Fa-ti-ma/NN-proxy-in-optimization.  ( 2 min )
    The moral authority of ChatGPT. (arXiv:2301.07098v1 [cs.CY])
    ChatGPT is not only fun to chat with, but it also searches information, answers questions, and gives advice. With consistent moral advice, it might improve the moral judgment and decisions of users, who often hold contradictory moral beliefs. Unfortunately, ChatGPT turns out highly inconsistent as a moral advisor. Nonetheless, it influences users' moral judgment, we find in an experiment, even if they know they are advised by a chatting bot, and they underestimate how much they are influenced. Thus, ChatGPT threatens to corrupt rather than improves users' judgment. These findings raise the question of how to ensure the responsible use of ChatGPT and similar AI. Transparency is often touted but seems ineffective. We propose training to improve digital literacy.  ( 2 min )
    Distributed LSTM-Learning from Differentially Private Label Proportions. (arXiv:2301.07101v1 [cs.LG])
    Data privacy and decentralised data collection has become more and more popular in recent years. In order to solve issues with privacy, communication bandwidth and learning from spatio-temporal data, we will propose two efficient models which use Differential Privacy and decentralized LSTM-Learning: One, in which a Long Short Term Memory (LSTM) model is learned for extracting local temporal node constraints and feeding them into a Dense-Layer (LabelProportionToLocal). The other approach extends the first one by fetching histogram data from the neighbors and joining the information with the LSTM output (LabelProportionToDense). For evaluation two popular datasets are used: Pems-Bay and METR-LA. Additionally, we provide an own dataset, which is based on LuST. The evaluation will show the tradeoff between performance and data privacy.  ( 2 min )
    EENet: Learning to Early Exit for Adaptive Inference. (arXiv:2301.07099v1 [cs.LG])
    Budgeted adaptive inference with early exits is an emerging technique to improve the computational efficiency of deep neural networks (DNNs) for edge AI applications with limited resources at test time. This method leverages the fact that different test data samples may not require the same amount of computation for a correct prediction. By allowing early exiting from full layers of DNN inference for some test examples, we can reduce latency and improve throughput of edge inference while preserving performance. Although there have been numerous studies on designing specialized DNN architectures for training early-exit enabled DNN models, most of the existing work employ hand-tuned or manual rule-based early exit policies. In this study, we introduce a novel multi-exit DNN inference framework, coined as EENet, which leverages multi-objective learning to optimize the early exit policy for a trained multi-exit DNN under a given inference budget. This paper makes two novel contributions. First, we introduce the concept of early exit utility scores by combining diverse confidence measures with class-wise prediction scores to better estimate the correctness of test-time predictions at a given exit. Second, we train a lightweight, budget-driven, multi-objective neural network over validation predictions to learn the exit assignment scheduling for query examples at test time. The EENet early exit scheduler optimizes both the distribution of test samples to different exits and the selection of the exit utility thresholds such that the given inference budget is satisfied while the performance metric is maximized. Extensive experiments are conducted on five benchmarks, including three image datasets (CIFAR-10, CIFAR-100, ImageNet) and two NLP datasets (SST-2, AgNews). The results demonstrate the performance improvements of EENet compared to existing representative early exit techniques.  ( 2 min )
  • Open

    Data thinning for convolution-closed distributions. (arXiv:2301.07276v1 [stat.ME])
    We propose data thinning, a new approach for splitting an observation into two or more independent parts that sum to the original observation, and that follow the same distribution as the original observation, up to a (known) scaling of a parameter. This proposal is very general, and can be applied to any observation drawn from a "convolution closed" distribution, a class that includes the Gaussian, Poisson, negative binomial, Gamma, and binomial distributions, among others. It is similar in spirit to -- but distinct from, and more easily applicable than -- a recent proposal known as data fission. Data thinning has a number of applications to model selection, evaluation, and inference. For instance, cross-validation via data thinning provides an attractive alternative to the "usual" approach of cross-validation via sample splitting, especially in unsupervised settings in which the latter is not applicable. In simulations and in an application to single-cell RNA-sequencing data, we show that data thinning can be used to validate the results of unsupervised learning approaches, such as k-means clustering and principal components analysis.  ( 2 min )
    Optimal Sub-sampling to Boost Power of Kernel Sequential Change-point Detection. (arXiv:2210.15060v2 [stat.ME] UPDATED)
    We present a novel scheme to boost detection power for kernel maximum mean discrepancy based sequential change-point detection procedures. Our proposed scheme features an optimal sub-sampling of the history data before the detection procedure, in order to tackle the power loss incurred by the random sub-sample from the enormous history data. We apply our proposed scheme to both Scan $B$ and Kernel Cumulative Sum (CUSUM) procedures, and improved performance is observed from extensive numerical experiments.  ( 2 min )
    A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles. (arXiv:2301.07156v1 [cs.LG])
    In this work, we address the problem of long-distance navigation for battery electric vehicles (BEVs), where one or more charging sessions are required to reach the intended destination. We consider the availability and performance of the charging stations to be unknown and stochastic, and develop a combinatorial semi-bandit framework for exploring the road network to learn the parameters of the queue time and charging power distributions. Within this framework, we first outline a pre-processing for the road network graph to handle the constrained combinatorial optimization problem in an efficient way. Then, for the pre-processed graph, we use a Bayesian approach to model the stochastic edge weights, utilizing conjugate priors for the one-parameter exponential and two-parameter gamma distributions, the latter of which is novel to multi-armed bandit literature. Finally, we apply combinatorial versions of Thompson Sampling, BayesUCB and Epsilon-greedy to the problem. We demonstrate the performance of our framework on long-distance navigation problem instances in country-sized road networks, with simulation experiments in Norway, Sweden and Finland.  ( 2 min )
    Learning Deformation Trajectories of Boltzmann Densities. (arXiv:2301.07388v1 [stat.ML])
    We introduce a training objective for continuous normalizing flows that can be used in the absence of samples but in the presence of an energy function. Our method relies on either a prescribed or a learnt interpolation $f_t$ of energy functions between the target energy $f_1$ and the energy function of a generalized Gaussian $f_0(x) = (|x|/\sigma)^p$. This then induces an interpolation of Boltzmann densities $p_t \propto e^{-f_t}$ and we aim to find a time-dependent vector field $V_t$ that transports samples along this family of densities. Concretely, this condition can be translated to a PDE between $V_t$ and $f_t$ and we minimize the amount by which this PDE fails to hold. We compare this objective to the reverse KL-divergence on Gaussian mixtures and on the $\phi^4$ lattice field theory on a circle.  ( 2 min )
    Reliable amortized variational inference with physics-based latent distribution correction. (arXiv:2207.11640v3 [stat.ML] UPDATED)
    Bayesian inference for high-dimensional inverse problems is computationally costly and requires selecting a suitable prior distribution. Amortized variational inference addresses these challenges via a neural network that approximates the posterior distribution not only for one instance of data, but a distribution of data pertaining to a specific inverse problem. During inference, the neural network -- in our case a conditional normalizing flow -- provides posterior samples at virtually no cost. However, the accuracy of amortized variational inference relies on the availability of high-fidelity training data, which seldom exists in geophysical inverse problems due to the Earth's heterogeneity. In addition, the network is prone to errors if evaluated over out-of-distribution data. As such, we propose to increase the resilience of amortized variational inference in the presence of moderate data distribution shifts. We achieve this via a correction to the latent distribution that improves the posterior distribution approximation for the data at hand. The correction involves relaxing the standard Gaussian assumption on the latent distribution and parameterizing it via a Gaussian distribution with an unknown mean and (diagonal) covariance. These unknowns are then estimated by minimizing the Kullback-Leibler divergence between the corrected and the (physics-based) true posterior distributions. While generic and applicable to other inverse problems, by means of a linearized seismic imaging example, we show that our correction step improves the robustness of amortized variational inference with respect to changes in the number of seismic sources, noise variance, and shifts in the prior distribution. This approach provides a seismic image with limited artifacts and an assessment of its uncertainty at approximately the same cost as five reverse-time migrations.  ( 2 min )
    A Nonsmooth Dynamical Systems Perspective on Accelerated Extensions of ADMM. (arXiv:1808.04048v7 [math.OC] UPDATED)
    Recently, there has been great interest in connections between continuous-time dynamical systems and optimization methods, notably in the context of accelerated methods for smooth and unconstrained problems. In this paper we extend this perspective to nonsmooth and constrained problems by obtaining differential inclusions associated to novel accelerated variants of the alternating direction method of multipliers (ADMM). Through a Lyapunov analysis, we derive rates of convergence for these dynamical systems in different settings that illustrate an interesting tradeoff between decaying versus constant damping strategies. We also obtain modified equations capturing fine-grained details of these methods, which have improved stability and preserve the leading order convergence rates. An extension to general nonlinear equality and inequality constraints in connection with singular perturbation theory is provided.  ( 2 min )
    Optimistic Dynamic Regret Bounds. (arXiv:2301.07530v1 [cs.LG])
    Online Learning (OL) algorithms have originally been developed to guarantee good performances when comparing their output to the best fixed strategy. The question of performance with respect to dynamic strategies remains an active research topic. We develop in this work dynamic adaptations of classical OL algorithms based on the use of experts' advice and the notion of optimism. We also propose a constructivist method to generate those advices and eventually provide both theoretical and experimental guarantees for our procedures.  ( 2 min )
    Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification. (arXiv:2301.05869v1 [cs.LG] CROSS LISTED)
    It is desirable for statistical models to detect signals of interest independently of their position. If the data is generated by some smooth process, this additional structure should be taken into account. We introduce a new class of neural networks that are shift invariant and preserve smoothness of the data: functional neural networks (FNNs). For this, we use methods from functional data analysis (FDA) to extend multi-layer perceptrons and convolutional neural networks to functional data. We propose different model architectures, show that the models outperform a benchmark model from FDA in terms of accuracy and successfully use FNNs to classify electroencephalography (EEG) data.  ( 2 min )
    Complexity Analysis of a Countable-armed Bandit Problem. (arXiv:2301.07243v1 [cs.LG])
    We consider a stochastic multi-armed bandit (MAB) problem motivated by ``large'' action spaces, and endowed with a population of arms containing exactly $K$ arm-types, each characterized by a distinct mean reward. The decision maker is oblivious to the statistical properties of reward distributions as well as the population-level distribution of different arm-types, and is precluded also from observing the type of an arm after play. We study the classical problem of minimizing the expected cumulative regret over a horizon of play $n$, and propose algorithms that achieve a rate-optimal finite-time instance-dependent regret of $\mathcal{O}\left( \log n \right)$. We also show that the instance-independent (minimax) regret is $\tilde{\mathcal{O}}\left( \sqrt{n} \right)$ when $K=2$. While the order of regret and complexity of the problem suggests a great degree of similarity to the classical MAB problem, properties of the performance bounds and salient aspects of algorithm design are quite distinct from the latter, as are the key primitives that determine complexity along with the analysis tools needed to study them.  ( 2 min )
    Improving Federated Learning Personalization via Model Agnostic Meta Learning. (arXiv:1909.12488v2 [cs.LG] UPDATED)
    Federated Learning (FL) refers to learning a high quality global model based on decentralized data storage, without ever copying the raw data. A natural scenario arises with data created on mobile phones by the activity of their users. Given the typical data heterogeneity in such situations, it is natural to ask how can the global model be personalized for every such device, individually. In this work, we point out that the setting of Model Agnostic Meta Learning (MAML), where one optimizes for a fast, gradient-based, few-shot adaptation to a heterogeneous distribution of tasks, has a number of similarities with the objective of personalization for FL. We present FL as a natural source of practical applications for MAML algorithms, and make the following observations. 1) The popular FL algorithm, Federated Averaging, can be interpreted as a meta learning algorithm. 2) Careful fine-tuning can yield a global model with higher accuracy, which is at the same time easier to personalize. However, solely optimizing for the global model accuracy yields a weaker personalization result. 3) A model trained using a standard datacenter optimization method is much harder to personalize, compared to one trained using Federated Averaging, supporting the first claim. These results raise new questions for FL, MAML, and broader ML research.  ( 2 min )
    What relations are reliably embeddable in Euclidean space?. (arXiv:1903.05347v3 [cs.LG] UPDATED)
    We consider the problem of embedding a relation, represented as a directed graph, into Euclidean space. For three types of embeddings motivated by the recent literature on knowledge graphs, we obtain characterizations of which relations they are able to capture, as well as bounds on the minimal dimensionality and precision needed.  ( 2 min )
    Global Contrastive Batch Sampling via Optimization on Sample Permutations. (arXiv:2210.12874v3 [cs.LG] UPDATED)
    Contrastive Learning has recently achieved state-of-the-art performance in a wide range of tasks. Many contrastive learning approaches use mined hard negatives to make batches more informative during training but these approaches are inefficient as they increase epoch length proportional to the number of mined negatives and require frequent updates of nearest neighbor indices or mining from recent batches. In this work, we provide an alternative to hard negative mining, Global Contrastive Batch Sampling (GCBS), an efficient approximation to the batch assignment problem that upper bounds the gap between the global and training losses, $\mathcal{L}^{Global} - \mathcal{L}^{Train}$, in contrastive learning settings. Through experimentation we find GCBS improves state-of-the-art performance in sentence embedding and code-search tasks. Additionally, GCBS is easy to implement as it requires only a few additional lines of code, does not maintain external data structures such as nearest neighbor indices, is more computationally efficient than the most minimal hard negative mining approaches, and makes no changes to the model being trained.  ( 2 min )
    Concentration inequalities for leave-one-out cross validation. (arXiv:2211.02478v2 [math.ST] UPDATED)
    In this article we prove that estimator stability is enough to show that leave-one-out cross validation is a sound procedure, by providing concentration bounds in a general framework. In particular, we provide concentration bounds beyond Lipschitz continuity assumptions on the loss or on the estimator. In order to obtain our results, we rely on random variables with distribution satisfying the logarithmic Sobolev inequality, providing us a relatively rich class of distributions. We illustrate our method by considering several interesting examples, including linear regression, kernel density estimation, and stabilized / truncated estimators such as stabilized kernel regression.  ( 2 min )
    Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness. (arXiv:2301.07487v1 [cs.LG])
    Learning from raw high dimensional data via interaction with a given environment has been effectively achieved through the utilization of deep neural networks. Yet the observed degradation in policy performance caused by imperceptible worst-case policy dependent translations along high sensitivity directions (i.e. adversarial perturbations) raises concerns on the robustness of deep reinforcement learning policies. In our paper, we show that these high sensitivity directions do not lie only along particular worst-case directions, but rather are more abundant in the deep neural policy landscape and can be found via more natural means in a black-box setting. Furthermore, we show that vanilla training techniques intriguingly result in learning more robust policies compared to the policies learnt via the state-of-the-art adversarial training techniques. We believe our work lays out intriguing properties of the deep reinforcement learning policy manifold and our results can help to build robust and generalizable deep reinforcement learning policies.  ( 2 min )
    PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes. (arXiv:2301.01514v1 [eess.SP] CROSS LISTED)
    Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separated by low-pass filtering. We combine the generalized quasi-norm ratio SOOT/SPOQ sparse penalties $\ell_p/\ell_q$ with the BEADS ternary assisted source separation algorithm. This results in a both convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. It outperforms comparable methods, when applied to typically peaked analytical chemistry signals. Reproducible code is provided.  ( 2 min )
    Using Topological Data Analysis to classify Encrypted Bits. (arXiv:2301.07393v1 [cs.CR])
    We present a way to apply topological data analysis for classifying encrypted bits into distinct classes. Persistent homology is applied to generate topological features of a point cloud obtained from sets of encryptions. We see that this machine learning pipeline is able to classify our data successfully where classical models of machine learning fail to perform the task. We also see that this pipeline works as a dimensionality reduction method making this approach to classify encrypted data a realistic method to classify the given encryptioned bits.  ( 2 min )
    Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification. (arXiv:2301.07609v1 [stat.ML])
    Data-driven approaches coupled with physical knowledge are powerful techniques to model systems. The goal of such models is to efficiently solve for the underlying field by combining measurements with known physical laws. As many systems contain unknown elements, such as missing parameters, noisy data, or incomplete physical laws, this is widely approached as an uncertainty quantification problem. The common techniques to handle all the variables typically depend on the numerical scheme used to approximate the posterior, and it is desirable to have a method which is independent of any such discretization. Information field theory (IFT) provides the tools necessary to perform statistics over fields that are not necessarily Gaussian. We extend IFT to physics-informed IFT (PIFT) by encoding the functional priors with information about the physical laws which describe the field. The posteriors derived from this PIFT remain independent of any numerical scheme and can capture multiple modes, allowing for the solution of problems which are ill-posed. We demonstrate our approach through an analytical example involving the Klein-Gordon equation. We then develop a variant of stochastic gradient Langevin dynamics to draw samples from the joint posterior over the field and model parameters. We apply our method to numerical examples with various degrees of model-form error and to inverse problems involving nonlinear differential equations. As an addendum, the method is equipped with a metric which allows the posterior to automatically quantify model-form uncertainty. Because of this, our numerical experiments show that the method remains robust to even an incorrect representation of the physics given sufficient data. We numerically demonstrate that the method correctly identifies when the physics cannot be trusted, in which case it automatically treats learning the field as a regression problem.  ( 2 min )
    Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data. (arXiv:2301.07206v1 [stat.ML])
    Relating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features. When high-dimensional problems arise, dimension reduction techniques can be used. Most notable are projections (e.g. Partial Least Squares or PLS ) or variable selections (e.g. lasso). Sparse partial least squares combine both strategies, by blending variable selection into PLS. The variant presented in this paper, Dual-sPLS, generalizes the classical PLS1 algorithm. It provides balance between accurate prediction and efficient interpretation. It is based on penalizations inspired by classical regression methods (lasso, group lasso, least squares, ridge) and uses the dual norm notion. The resulting sparsity is enforced by an intuitive shrinking ratio parameter. Dual-sPLS favorably compares to similar regression methods, on simulated and real chemical data. Code is provided as an open-source package in R: \url{https://CRAN.R-project.org/package=dual.spls}.  ( 2 min )
    Discrete Latent Structure in Neural Networks. (arXiv:2301.07473v1 [cs.LG])
    Many types of data from fields including natural language processing, computer vision, and bioinformatics, are well represented by discrete, compositional structures such as trees, sequences, or matchings. Latent structure models are a powerful tool for learning to extract such representations, offering a way to incorporate structural bias, discover insight about the data, and interpret decisions. However, effective training is challenging, as neural networks are typically designed for continuous computation. This text explores three broad strategies for learning with discrete latent structure: continuous relaxation, surrogate gradients, and probabilistic estimation. Our presentation relies on consistent notations for a wide range of models. As such, we reveal many new connections between latent structure learning strategies, showing how most consist of the same small set of fundamental building blocks, but use them differently, leading to substantially different applicability and properties.  ( 2 min )
    Sample Complexity of Adversarially Robust Linear Classification on Separated Data. (arXiv:2012.10794v3 [cs.LG] UPDATED)
    We consider the sample complexity of learning with adversarial robustness. Most prior theoretical results for this problem have considered a setting where different classes in the data are close together or overlapping. Motivated by some real applications, we consider, in contrast, the well-separated case where there exists a classifier with perfect accuracy and robustness, and show that the sample complexity narrates an entirely different story. Specifically, for linear classifiers, we show a large class of well-separated distributions where the expected robust loss of any algorithm is at least $\Omega(\frac{d}{n})$, whereas the max margin algorithm has expected standard loss $O(\frac{1}{n})$. This shows a gap in the standard and robust losses that cannot be obtained via prior techniques. Additionally, we present an algorithm that, given an instance where the robustness radius is much smaller than the gap between the classes, gives a solution with expected robust loss is $O(\frac{1}{n})$. This shows that for very well-separated data, convergence rates of $O(\frac{1}{n})$ are achievable, which is not the case otherwise. Our results apply to robustness measured in any $\ell_p$ norm with $p > 1$ (including $p = \infty$).  ( 2 min )
    Electronic excited states in deep variational Monte Carlo. (arXiv:2203.09472v3 [physics.chem-ph] UPDATED)
    Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications. One ab initio method for solving the Schr\"odinger equation that scales favorably for large systems is variational quantum Monte Carlo (QMC). The recently introduced deep QMC approach uses ansatzes represented by deep neural networks and generates nearly exact ground-state solutions for molecules containing up to a few dozen electrons, with the potential to scale to much larger systems where other highly accurate methods are not feasible. In this paper, we extend one such ansatz (PauliNet) to compute electronic excited states. We demonstrate our method on various small atoms and molecules and consistently achieve high accuracy for low-lying states. To highlight the method's potential, we compute the first excited state of the much larger benzene molecule, as well as the conical intersection of ethylene, with PauliNet matching results of more expensive high-level methods.  ( 2 min )
    LIMEADE: From AI Explanations to Advice Taking. (arXiv:2003.04315v5 [cs.IR] UPDATED)
    Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow an AI to take advice from humans in response to explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA$^2$Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, little attention has been given to advice methods for opaque models. This paper introduces LIMEADE, the first general framework that translates both positive and negative advice (expressed using high-level vocabulary such as that employed by post-hoc explanations) into an update to an arbitrary, underlying opaque model. We demonstrate the generality of our approach with case studies on seventy real-world models across two broad domains: image classification and text recommendation. We show our method improves accuracy compared to a rigorous baseline on the image classification domains. For the text modality, we apply our framework to a neural recommender system for scientific papers on a public website; our user study shows that our framework leads to significantly higher perceived user control, trust, and satisfaction.  ( 2 min )
    Landscape Complexity for the Empirical Risk of Generalized Linear Models. (arXiv:1912.02143v5 [stat.ML] UPDATED)
    We present a method to obtain the average and the typical value of the number of critical points of the empirical risk landscape for generalized linear estimation problems and variants. This represents a substantial extension of previous applications of the Kac-Rice method since it allows to analyze the critical points of high dimensional non-Gaussian random functions. Under a technical hypothesis, we obtain a rigorous explicit variational formula for the annealed complexity, which is the logarithm of the average number of critical points at fixed value of the empirical risk. This result is simplified, and extended, using the non-rigorous Kac-Rice replicated method from theoretical physics. In this way we find an explicit variational formula for the quenched complexity, which is generally different from its annealed counterpart, and allows to obtain the number of critical points for typical instances up to exponential accuracy.  ( 2 min )
    Strong inductive biases provably prevent harmless interpolation. (arXiv:2301.07605v1 [stat.ML])
    Classical wisdom suggests that estimators should avoid fitting noise to achieve good generalization. In contrast, modern overparameterized models can yield small test error despite interpolating noise -- a phenomenon often called "benign overfitting" or "harmless interpolation". This paper argues that the degree to which interpolation is harmless hinges upon the strength of an estimator's inductive bias, i.e., how heavily the estimator favors solutions with a certain structure: while strong inductive biases prevent harmless interpolation, weak inductive biases can even require fitting noise to generalize well. Our main theoretical result establishes tight non-asymptotic bounds for high-dimensional kernel regression that reflect this phenomenon for convolutional kernels, where the filter size regulates the strength of the inductive bias. We further provide empirical evidence of the same behavior for deep neural networks with varying filter sizes and rotational invariance.  ( 2 min )
    Non-IID Quantum Federated Learning with One-shot Communication Complexity. (arXiv:2209.00768v2 [quant-ph] UPDATED)
    Federated learning refers to the task of machine learning based on decentralized data from multiple clients with secured data privacy. Recent studies show that quantum algorithms can be exploited to boost its performance. However, when the clients' data are not independent and identically distributed (IID), the performance of conventional federated algorithms is known to deteriorate. In this work, we explore the non-IID issue in quantum federated learning with both theoretical and numerical analysis. We further prove that a global quantum channel can be exactly decomposed into local channels trained by each client with the help of local density estimators. This observation leads to a general framework for quantum federated learning on non-IID data with one-shot communication complexity. Numerical simulations show that the proposed algorithm outperforms the conventional ones significantly under non-IID settings.  ( 2 min )
    An Analysis of Loss Functions for Binary Classification and Regression. (arXiv:2301.07638v1 [stat.ML])
    This paper explores connections between margin-based loss functions and consistency in binary classification and regression applications. It is shown that a large class of margin-based loss functions for binary classification/regression result in estimating scores equivalent to log-likelihood scores weighted by an even function. A simple characterization for conformable (consistent) loss functions is given, which allows for straightforward comparison of different losses, including exponential loss, logistic loss, and others. The characterization is used to construct a new Huber-type loss function for the logistic model. A simple relation between the margin and standardized logistic regression residuals is derived, demonstrating that all margin-based loss can be viewed as loss functions of squared standardized logistic regression residuals. The relation provides new, straightforward interpretations for exponential and logistic loss, and aids in understanding why exponential loss is sensitive to outliers. In particular, it is shown that minimizing empirical exponential loss is equivalent to minimizing the sum of squared standardized logistic regression residuals. The relation also provides new insight into the AdaBoost algorithm.  ( 2 min )
    Large Deviations for Classification Performance Analysis of Machine Learning Systems. (arXiv:2301.07104v1 [cs.LG])
    We study the performance of machine learning binary classification techniques in terms of error probabilities. The statistical test is based on the Data-Driven Decision Function (D3F), learned in the training phase, i.e., what is thresholded before the final binary decision is made. Based on large deviations theory, we show that under appropriate conditions the classification error probabilities vanish exponentially, as $\sim \exp\left(-n\,I + o(n) \right)$, where $I$ is the error rate and $n$ is the number of observations available for testing. We also propose two different approximations for the error probability curves, one based on a refined asymptotic formula (often referred to as exact asymptotics), and another one based on the central limit theorem. The theoretical findings are finally tested using the popular MNIST dataset.  ( 2 min )

  • Open

    [D] ICLR 2023 results.
    Hi, Making a post for anything to be discussed related to ICLR 2023 results ​ One question I had: Is the exact time of result announcement fixed? submitted by /u/East-Beginning9987 [link] [comments]  ( 41 min )
    [Discussion] I'm Getting 50FPS With 4 Billion Parameters, Is That Good? - Compute Shader Implementation
    So I like to DIY a lot. I coded a neural network to run parallel in a compute shader with TanH activation, and the performance was much better than I expected. I Tested with a 3090 with many layers of 20,000 neurons until I reached 4 billion total parameters which ran around 50FPS when looped every frame. Is this above average performance for a GPU implementations? I haven't really tested out any other GPU implementations, so I was wondering if anyone here knows. submitted by /u/TheRPGGamerMan [link] [comments]  ( 42 min )
    I'm working on a project and need a open source chatbot that I can run locally and train to talk like a specific character, does anyone know one? [P]
    Title, I am trying to get a chatbot to act like Megumin, similar to Character AI, but open source and can be run on a local machine. Thank you! submitted by /u/otakuhacker123 [link] [comments]  ( 42 min )
    [D][P] Best Speech-to-Text Model for domain-specific data - Open Source vs. Paid Services
    I originally posted this here on r/learnmachinelearning but reposting here as it may be a more appropriate subreddit and/or may have a different perspective. I want a tool to programmatically generate transcripts from sermons. I have access to hundreds of sermon transcripts (and 100x more very similar in domain data) but less than a 40 transcripts with audio (~30 hours). I want the lowest WER (Word Error Rate) possible and can budge 100 hours for this project in 2023. Train my own acoustic model with the best open source offering OpenAI's Whisper seems to be the best available today? How much supervised data (e.g. hours of sermons with perfect transcripts) would I need to develop a model that would be more accurate than Google/AWS for my specific domain? Can I take a model already trained and "tune" it by augmenting the data I have? Use the best cloud speech-to-text API that I can provide in-domain data to to tune it AWS Transcribe and Google Speech to Text seem to be big players I've gone with AWS Transcribe since it can be tuned more easily with custom domain data (just upload text files) than Google's (which requires building phrase dictionaries with weights). Is there anything out there that's better for my use case? submitted by /u/Knecht_Christi [link] [comments]  ( 43 min )
    [D] Pre-trained Models for Domain-Specific (i.e. Stylistic) Feature Extraction
    Most or all of the style transfer models are used to extract the domain-independent (robust) feature from an artpiece so as to apply it to different styles. But I need the opposite: I need a pretrained model that can extract the domain-specific (i.e. stylistic) feature from an artpiece. Are there any publicly available ones I can use? It doesn't matter whether it's a Github repository, a Huggingface API, or something else. Thank you! submitted by /u/No_Zookeepergame8794 [link] [comments]  ( 42 min )
    [P] Labeling tools are great, but what about quality checks?
    Modern datasets contain hundreds of thousands to millions of labels that must be kept accurate. In practice, some errors in the dataset average out and can be ignored – systematic biases transfer to the model. After quick initial wins in areas where abundant data is readily available, deep learning needs to become more data efficient to help solve difficult business problems. MLfix is a new open-source tool that combines novel unsupervised machine-learning pipelines with a new user interface concept that, together, help annotators and machine-learning engineers identify and filter out label errors. https://www.collabora.com/news-and-blog/blog/2023/01/17/labeling-tools-are-great-but-what-about-quality-checks/ submitted by /u/mfilion [link] [comments]  ( 42 min )
    [D] is it time to investigate retrieval language models?
    With ChatGPT going mainstream and the general push to make products out of LM, a problem remain about the cost of running such models. To me, it seems counterproductive to put both language modelling and knowledge inside the model weights. Is it time to shift to retrieval LM like Retro to keep the cost down while offering the same products? It would possibly allow Google or others to offer a free assistant service, using embeddings similarity search to retrieve results from the Internet so the model itself could possibly even run on edge devices? What are your thoughts about that subject? submitted by /u/hapliniste [link] [comments]  ( 46 min )
    [P] Code super clean multi-modal PyTorch models and easily serve them through FastAPI, using DocArray
    Hi all! I'd like to share an open source project that I am currently working on together with a few colleagues: DocArray! If you've ever trained models that deal with different data types (images, text, video, audio, ...) then you know how much of a hassle it can be to keep track of all of your tensors, what shapes they have, and what data they are meant to represent. That's what we're trying to change with DocArray, a Python library for representing, sending, and storing multi-modal data! The core idea of DocArray is that you define Documents that represent your data. For example, one Document could hold the file path to an image, its image tensor, and and image embedding that your model creates. A different Document could do the same thing for some Text, and a third Document might co…  ( 44 min )
    [P] Tired of generating synthetic corgis❓🐶 Check out Synthcity, a framework for synthetic tabular data
    🌟 Synthcity isa library for generating and benchmarking synthetic tabular data. https://github.com/vanderschaarlab/synthcity ​ 🚀 Synthcity includes a wide range of algorithms for various use cases, such as: - tabular data(CTGAN, TVAE, Bayesian Networks etc) - survival analysis(SurvivalGAN etc). - time series(Fourier Flows, TimeGAN, etc.). - privacy-focused(DP-GAN, PATEGAN, AdsGAN, DECAF). - domain adaptation(RadialGAN). ​ 🔍 Synthcity supports benchmarking multiple algorithms, testing data quality, downstream performance, statistical fidelity, and privacy metrics. ​ 🌀 Give it a try: - Library: https://github.com/vanderschaarlab/synthcity - Tutorial: https://colab.research.google.com/drive/1Vr2PJswgfFYBkJCm3hhVkuH-9dXnHeYV?usp=sharing - Docs: https://synthcity.readthedocs.io/ submitted by /u/ManagementBig2995 [link] [comments]  ( 42 min )
    [P] Need some recs on an NLP project
    Hello, for my job, I have to extract job responsibilities from job ads. I'm thinking of approaching it as a span extraction problem. Where I'm gonna label the job responsibility span manually for around 1000 samples. And use supervised learning. Is there any better way to approach this problem? Is there any pretrained model I can use to fine tune? Any suggestion will be appreciated. Thanks! submitted by /u/Salekeen01 [link] [comments]  ( 43 min )
    [R] Human-Timescale Adaptation in an Open-Ended Task Space - (AdA) - DeepMind 2023 - Can adapt to open-ended novel embodied 3D problems as quickly as humans!
    Paper: https://arxiv.org/abs/2301.07608 Youtube: https://www.youtube.com/watch?v=U93bUQ1roiw Please watch the Video the explanations are better than me giving you 3-5 Pictures! Abstract: Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL). In this work, we demonstrate that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans. In a vast space of held-out environment dynamics, our adaptive agent (AdA) displays on-the-fly hypothesis-driven exploration, efficient exploitation of acquired knowledge, and can successfully be prompted with first-person demonstrations. Adaptation emerges from three ingredients: (1) meta-reinforcement learning across a vast, smooth and diverse task distribution, (2) a policy parameterised as a large-scale attention-based memory architecture, and (3) an effective automated curriculum that prioritises tasks at the frontier of an agent's capabilities. We demonstrate characteristic scaling laws with respect to network size, memory length, and richness of the training task distribution. We believe our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains. https://preview.redd.it/is3pyl1p70da1.jpg?width=1424&format=pjpg&auto=webp&s=8d102af4202711be7e01619577f109b6598d6ed5 submitted by /u/Singularian2501 [link] [comments]  ( 44 min )
    [D] Question about using diffusion to denoise images
    Hi all, I am trying to see if I can use DDPM (Denoising Diffusion Probabilistic Model) to denoise images using a supervised learning approach. However, I've learned that DDPM is only for unconditional image generation. Has anyone had experience using conditional DDPM and could help me out with some conceptual questions? Here's what I'm trying to understand: Say I have a pair of noisy and clean ground truth images. Should I take my clean image and gradually corrupt it by adding gaussian noise in the forward diffusion (FD) process? Could I get the network to learn the reverse diffusion process by giving it the noisy input, the FD noisy image, and positional embeddings? I was planning on concatenating the noisy input with the FD noisy image. During training, the network learns to predict noise at t-1 given the image at t conditioned on the input noisy source image. Here is an image showing you what I mean. Any thoughts or suggestions would be greatly appreciated. DDPM for image denoising submitted by /u/CurrentlyJoblessFML [link] [comments]  ( 44 min )
    [D] What is the name of this NLP technique?
    Lets say I have a dataset of real estate listings. I have a column of text that describes the listing, and another column that shows the number of rooms for example. In most of the cases, the number of rooms is shown in both columns, in the description text and also in the dedicated column. But for some observations, the number of rooms is in the description text but not in the column "number of rooms". So I have missing data. I could try to fill the missing data with by applying regex in the description text, but the number of possibilities seems to big. Is there a machine learning technique in NLP that allows me to do that, since it most of the observations the data is present in both column, so is "naturally labelled"? If there is, what is the name of these techniques? I would like to search about it but I don't know the proper keywords to google. submitted by /u/Kebet-Mendez [link] [comments]  ( 43 min )
    [D] Inner workings of the chatgpt memory
    All the examples from langchain and on huggingface create memory by pasting the entire history in every prompt. This seems to violate the max input prompt length pretty quickly. And it’s expensive. Does chatgpt use something revolutionary? It forgets everything when you create a new session so it ‘feels’ it’s using the convo as memory as well. But then the question; how do they get past prompt limits? Chunking doesn’t help as it still doesn’t get context in that case between prompts. Maybe they ask the same question with different chunks many times and then ask for a final result? Apologies if this was answered somewhere, I cannot find it at all and all examples use the same kind of history memory. submitted by /u/terserterseness [link] [comments]  ( 45 min )
    [D] very short video generation
    Hi, my indie game devs asked me if I could build a model that generates cool movements for their characters. 1) I wanted to start by generating characters and scene. Should I go for stable diffusion or for a GAN ? I do need a prompt 2) do you know any model that can generate short video clips (2 seconds) and that could potentially generate character movement ? Thank you so much ! submitted by /u/Frizzoux [link] [comments]  ( 42 min )
    [D] ML Researchers/Engineers in Industry: Why don't companies use open source models more often?
    In my experience at big tech, I've never seen any company use open-source ML models in production. Why is this the case? Curious, because there seems to be some insanely cool research going on these days. On the other hand, if you have seen this used, what kind of repos have you guys seen? submitted by /u/tennismlandguitar [link] [comments]  ( 46 min )
    [Discussion] Storing hundreds of ML models - what do you use?
    I am currently using the Google Cloud Model Registry and I want to learn what you use for archiving your machine learning models. What are the other options for developers who have to store hundreds of models? https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-model-registry submitted by /u/May-is-spring [link] [comments]  ( 42 min )
    [D] Best LLM for Question/Answering with personality?
    Hi, I am looking at fine tuning an open source LLM to answer questions as a specific character from chat on discord. I'm trying to decide which one to test between KoboldAI, GPTJ, Neo, Flan-T5, etc. Has anyone tries these LLMs and knows which would be best for this use case? The use case is to have a character that answers questions from discord chat in the form a specific character, with a personality and can be mean for example, very similar to https://beta.character.ai/ Does anyone want to guess what they use based on their experience or the closest LLM to replicate it? if this helps sometimes the model will go off on tangential stories which makes me think maybe it was trained on a novel or story dataset originally. submitted by /u/TernaryJimbo [link] [comments]  ( 42 min )
  • Open

    YouTube Video scripted entirely by ChatGPT???
    submitted by /u/McFIyyy [link] [comments]  ( 40 min )
    Inventory Management?
    I read a study that showed AI is being deployed into inventory management at a whopping 44% of total usage, though I can't quantify this, I am interested to know how to combine AI with my ERP, static data, a database etc. Does anyone here have experience with AI inventory management for MoQ, forecasting etc that they recommend? I want to do some homework submitted by /u/smudgepost [link] [comments]  ( 40 min )
    WhatsApp ChatGPT
    Hello i wanna ask you guys about this AI whatapp tool https://chattycat.ju.mp/ is it safe i saw it in a tweet and i was wondering if its the same as chat gpt and is it safe to give your name number and email " original tweet " https://twitter.com/TansuYegen/status/1616138232894492672 ​ WhatsApp gpt submitted by /u/Jnxe [link] [comments]  ( 40 min )
    Two guys in London working in AI looking for volunteers to join our team in educating the public on AI
    We’re 2 Brits who work in AI. We believe AI is likely to have a huge and mostly positive impact on society but that not many people realise this or understand how it will impact everyday life. There is a lack of places online right now clearly explaining the changes AI will bring, i.e., how will AI change the experience of shopping in stores in the next 10 years or how will AI change video games in the next 10 years. We are somewhat well positioned to collate the current views on likely future changes across most areas and are in the process of starting a website and perhaps video channel which will cover how AI is likely to impact people over the next 10 years in different areas of life (movies, sports, bars, banking, schools, hospitals etc). We are looking for people to help us research, write and make videos on this cause – which we think is important to help ensure that voters don’t misunderstand AI. Alex – researches, writes, and records the audio Seb - does the video and audio editing We thought we’d put the word out and ask if anyone else would like to volunteer to help create content too. No special skills needed. Getting involved would be as easy as PMing me, hearing about how we’ve done things so far and then saying what you might be interested in helping with. Maybe thinking about ideas for topics or getting involved in research and/or article writing. We are UTC-0 but open to all. submitted by /u/TheOptimisticRogue [link] [comments]  ( 41 min )
    The Next Step (I believe)
    We have already seen the capabilities of AI sourcing from the Internet to create, learn, and exceed our expectations. I want to be quick with my description so I don't loose anybody. The next step I believe will be AI catered to us individually. What I mean is an app that only cares for one person, that being you. It will comb over (with permission hopefully) everything you ever put online. Also track writting, Grammer, interests (internet of things information), about you. Then if you allow it, can go over your medical or financial history, even psychology. It will feel like a chatbot but be a middleman towards seeking healthcare, mental care, and may other things including niché interests and possible career routes. I don't propose this to invite any fear or anxiety of a sci-fi narrative, but to objectively observe the trends with AI and my belief where it is going. submitted by /u/DropDeddBlue [link] [comments]  ( 41 min )
    2 months to make ai video last summer: “The technology was evolving so fast that I worried my video would feel outdated by the time it would be ready.”
    submitted by /u/defensiveFruit [link] [comments]  ( 40 min )
    Announcing a major update to Perplexity Ask: the world’s first conversational search engine! Now, you can read answers with up-to-date sources and ask follow-up questions to dig deeper. In other words, you can chat with your search engine!
    submitted by /u/rafs2006 [link] [comments]  ( 40 min )
    is the data we have today enough to create AGI?
    let's say hypothetically we were only able to work with digital data we have collected up until today to try and create AGI, would it be possible? submitted by /u/Science_is_Greatness [link] [comments]  ( 42 min )
    Labeling tools are great, but what about quality checks?
    Modern datasets contain hundreds of thousands to millions of labels that must be kept accurate. In practice, some errors in the dataset average out and can be ignored – systematic biases transfer to the model. After quick initial wins in areas where abundant data is readily available, deep learning needs to become more data efficient to help solve difficult business problems. MLfix is a new open-source tool that combines novel unsupervised machine-learning pipelines with a new user interface concept that, together, help annotators and machine-learning engineers identify and filter out label errors. https://www.collabora.com/news-and-blog/blog/2023/01/17/labeling-tools-are-great-but-what-about-quality-checks/ submitted by /u/mfilion [link] [comments]  ( 40 min )
    Google Research And DeepMind Create AI Medical Chatbot That Can Generate Safe And Helpful Answers!
    submitted by /u/liquidocelotYT [link] [comments]  ( 40 min )
    Software to read "Once upon a time in Hollywood" book in character voices from film.
    I want an audiobook version of the book "Once upon a time in Hollywood" by Tarantino with the actors voices from the movie, using the voices from the movie as sample. How do I do that? submitted by /u/Art3mis_eros [link] [comments]  ( 40 min )
    Professor Initiates Integration of ChatGPT in Classroom
    submitted by /u/lambolifeofficial [link] [comments]  ( 40 min )
    Neural Network 'Hallucinating' While Training On Dog Images
    submitted by /u/TheRPGGamerMan [link] [comments]  ( 41 min )
    Wi-Fi Could Help Identify When You’re Struggling to Breathe
    submitted by /u/goronmask [link] [comments]  ( 40 min )
    Summarizing Text using In-database NLP through the Integration of Hugging Face with MindsDB
    submitted by /u/Klutzy_Accountant113 [link] [comments]  ( 40 min )
    Farmers Spend $5Billion a Year on Antibiotics For Their Animals using a Blanket Approach. Medicate all to Prevent Infection. AI Models are Being Used to Identify & Medicate Only Animals That are Actually Sick
    submitted by /u/HODLTID [link] [comments]  ( 40 min )
    I got frustrated with the time and effort required to code and maintain custom web scrapers, so I built an LLM-powered tool that can comprehend any website structure and extract the desired data in the preferred format.
    submitted by /u/madredditscientist [link] [comments]  ( 41 min )
    Generative AI Technology Is Discovering Completely New Drugs
    submitted by /u/bukowski3000 [link] [comments]  ( 40 min )
    Join us this Friday 6 pm EST for a fascinating discussion about the societal impact of large language models (LLMs) like ChatGPT
    submitted by /u/OnlyProggingForFun [link] [comments]  ( 40 min )
    Exclusive: The $2 Per Hour Workers Who Made ChatGPT Safer
    submitted by /u/ohmsalad [link] [comments]  ( 40 min )
    Hey guys! I made some of the cartoon characters to look like villains. Can you guess which cartoon they are from?
    submitted by /u/_aimnftri [link] [comments]  ( 40 min )
    Made with AI
    submitted by /u/NorthTs [link] [comments]  ( 40 min )
  • Open

    Probability problem with Pratt prime proofs
    In the process of creating a Pratt certificate to prove that a number n is prime, you have to find a number a that seems kinda arbitrary. As we discussed here, a number n is prime if there exists a number a such that an-1 = 1 mod n and a(n-1)/p ≠ 1 mod n […] Probability problem with Pratt prime proofs first appeared on John D. Cook.  ( 5 min )
    Factoring b^n + 1
    The previous post illustrated a technique for finding factors of number of the form bn – 1. This post will look at an analogous, though slightly less general, technique for numbers of the form bn + 1. There is a theorem that says that if m divides n then bm + 1 divides bn + […] Factoring b^n + 1 first appeared on John D. Cook.  ( 5 min )
    Factoring b^n – 1
    Suppose you want to factor a number of the form bn – 1. There is a theorem that says that if m divides n then bm – 1 divides bn – 1. Let’s use this theorem to try to factor J = 22023 – 1, a 609-digit number. Factoring such a large number would be more difficult if it didn’t have […] Factoring b^n – 1 first appeared on John D. Cook.  ( 6 min )
  • Open

    AI’s Leg Up: Startup Accelerates Robotics Simulation for $8 Trillion Food Market
    Robots are finally getting a grip. Developers have been striving to close the gap on robotic gripping for the past several years, pursuing applications for multibillion-dollar industries. Securely gripping and transferring fast-moving items on conveyor belts holds vast promise for businesses. Soft Robotics, a Bedford, Mass., startup, is harnessing NVIDIA Isaac Sim to help close Read article >  ( 6 min )
    The Ultimate Upgrade: GeForce RTX 4080 SuperPOD Rollout Begins Today
    The Ultimate upgrade begins today: GeForce NOW RTX 4080 SuperPODs are now rolling out, bringing a new level of high-performance gaming to the cloud. Ultimate members will start to see RTX 4080 performance in their region soon, and experience titles like  Warhammer 40,000: Darktide, Cyberpunk 2077, The Witcher 3: Wild Hunt and more at ultimate Read article >  ( 5 min )
  • Open

    Human-Timescale Adaptation in an Open-Ended Task Space - (AdA) - DeepMind 2023 - Can adapt to open-ended novel embodied 3D problems as quickly as humans!
    Paper: https://arxiv.org/abs/2301.07608 Youtube: https://www.youtube.com/watch?v=U93bUQ1roiw Please watch the Video the explanations are better than me giving you 3-5 Pictures! Abstract: Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL). In this work, we demonstrate that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans. In a vast space of held-out environment dynamics, our adaptive agent (AdA) displays on-the-fly hypothesis-driven exploration, efficient exploitation of acquired knowledge, and can successfully be prompted with first-person demonstrations. Adaptation emerges from three ingredients: (1) meta-reinforcement learning across a vast, smooth and diverse task distribution, (2) a policy parameterised as a large-scale attention-based memory architecture, and (3) an effective automated curriculum that prioritises tasks at the frontier of an agent's capabilities. We demonstrate characteristic scaling laws with respect to network size, memory length, and richness of the training task distribution. We believe our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains. https://preview.redd.it/rcta0vvt80da1.jpg?width=1424&format=pjpg&auto=webp&s=da7cc5745a21969b1687b7cf2a8c590dcac72ae0 submitted by /u/Singularian2501 [link] [comments]  ( 41 min )
    On the legal status of downloading and using ATARI 2600 ROMs
    Hi there, I have searched online quite broadly, and could not find an answer anywhere on the following questions: Is it legal to download ATARI ROMs, e.g., the ones in https://github.com/Farama-Foundation/AutoROM? Is it legal to use those ROMs? (Imagine I acquired them in a different way than downloading, like physical shipping on USB drive) If both are illegal, what's the legal situation around using them for reinforcement learning research? What I know from reading: Downloading ROMs is a gray area. It is supposedly illegal, but if the copyright holder knows about a use of their ROMs and doesn't do anything, there exist and "implied license" allowing their use. Source: here. Providing a means to download these ROMs is not illegal, it is allowed. Source: here. ​ Does anyone have more info or legal experience with this? ​ Thanks submitted by /u/Conscious_Heron_9133 [link] [comments]  ( 42 min )
    trying to make a single actuated link stay upright with dqn. The maximum score you can get from the game is 20,000, and the highest score I am getting is 200.
    submitted by /u/blackgentrifier [link] [comments]  ( 40 min )
  • Open

    Equivariant Networks for Crystal Structures. (arXiv:2211.15420v2 [cond-mat.mtrl-sci] UPDATED)
    Supervised learning with deep models has tremendous potential for applications in materials science. Recently, graph neural networks have been used in this context, drawing direct inspiration from models for molecules. However, materials are typically much more structured than molecules, which is a feature that these models do not leverage. In this work, we introduce a class of models that are equivariant with respect to crystalline symmetry groups. We do this by defining a generalization of the message passing operations that can be used with more general permutation groups, or that can alternatively be seen as defining an expressive convolution operation on the crystal graph. Empirically, these models achieve competitive results with state-of-the-art on property prediction tasks.
    Multi-Task Imitation Learning for Linear Dynamical Systems. (arXiv:2212.00186v2 [cs.LG] UPDATED)
    We study representation learning for efficient imitation learning over linear systems. In particular, we consider a setting where learning is split into two phases: (a) a pre-training step where a shared $k$-dimensional representation is learned from $H$ source policies, and (b) a target policy fine-tuning step where the learned representation is used to parameterize the policy class. We find that the imitation gap over trajectories generated by the learned target policy is bounded by $\tilde{O}\left( \frac{k n_x}{HN_{\mathrm{shared}}} + \frac{k n_u}{N_{\mathrm{target}}}\right)$, where $n_x > k$ is the state dimension, $n_u$ is the input dimension, $N_{\mathrm{shared}}$ denotes the total amount of data collected for each policy during representation learning, and $N_{\mathrm{target}}$ is the amount of target task data. This result formalizes the intuition that aggregating data across related tasks to learn a representation can significantly improve the sample efficiency of learning a target task. The trends suggested by this bound are corroborated in simulation.
    FedALA: Adaptive Local Aggregation for Personalized Federated Learning. (arXiv:2212.01197v2 [cs.LG] UPDATED)
    A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy.
    FeSAC: Federated Learning-Based Soft Actor-Critic Traffic Offloading in Space-Air-Ground Integrated Network. (arXiv:2212.02075v2 [cs.NI] UPDATED)
    With the increase of intelligent devices leading to increasing demand for traffic, traffic offloading has become a challenging problem. The space-air-ground integrated network (SAGIN) is a superior network architecture to solve this problem. The existing research on SAGIN traffic offloading only considers the single-layer satellite network in the space network. To further expand the resource pool of traffic offloading in SAGIN, we extend the single-layer satellite network into a double-layer satellite network composed of low-orbit satellites (LEO) and high-orbit satellites (GEO). And re-model a four-layer SAGIN architecture consisting of the ground network, the air network, LEO and GEO. Furthermore, we propose a novel Federated Soft Actor-Critic (FeSAC) traffic offloading method with positive environmental exploration to accommodate this dynamic and complex four-layer SAGIN architecture. The FeSAC method uses federated learning to train traffic offloading nodes and then aggregate the training results to obtain the best traffic offloading strategy. The simulation results show that under the four-layer SAGIN, our proposed method can better adapt to the network environment changes by nodes mobility and is better than the existing traffic offloading methods in throughput, packet loss, and transmission delay.
    Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework. (arXiv:2212.01519v2 [cs.LG] UPDATED)
    As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery still. In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm. While this explains the inherent robustness of federated ADMM, the vanilla version of it lacks the ability to be adaptive to the degree of client heterogeneity. Besides, the global model at the server under client sampling is biased which slows down the practical convergence. To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. In addition, FedVRA unifies several representative FL algorithms in the sense that they are either special instances of FedVRA or are close to it. Extensions of FedVRA to semi/un-supervised learning are also presented. Experiments based on (semi-)supervised image classification tasks demonstrate superiority of FedVRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling.
    Geometry-Complete Perceptron Networks for 3D Molecular Graphs. (arXiv:2211.02504v2 [cs.LG] UPDATED)
    The field of geometric deep learning has had a profound impact on the development of innovative and powerful graph neural network architectures. Disciplines such as computer vision and computational biology have benefited significantly from such methodological advances, which has led to breakthroughs in scientific domains such as protein structure prediction and design. In this work, we introduce GCPNet, a new geometry-complete, SE(3)-equivariant graph neural network designed for 3D molecular graph representation learning. We demonstrate the state-of-the-art utility and expressiveness of our method on six independent datasets designed for three distinct geometric tasks: protein-ligand binding affinity prediction, protein structure ranking, and Newtonian many-body systems modeling. Our results suggest that GCPNet is a powerful, general method for capturing complex geometric and physical interactions within 3D molecular graphs for downstream prediction tasks. The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.
    Unbalanced Optimal Transport, from Theory to Numerics. (arXiv:2211.08775v2 [stat.ML] UPDATED)
    Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need for a large number of samples in high dimension and the difficulty to handle data in distinct spaces. In this review, we detail several recently proposed approaches to mitigate these issues. We insist in particular on unbalanced OT, which compares arbitrary positive measures, not restricted to probability distributions (i.e. their total mass can vary). This generalization of OT makes it robust to outliers and missing data. The second workhorse of modern computational OT is entropic regularization, which leads to scalable algorithms while lowering the sample complexity in high dimension. The last point presented in this review is the Gromov-Wasserstein (GW) distance, which extends OT to cope with distributions belonging to different metric spaces. The main motivation for this review is to explain how unbalanced OT, entropic regularization and GW can work hand-in-hand to turn OT into efficient geometric loss functions for data sciences.
    Algorithmic progress in computer vision. (arXiv:2212.05153v3 [cs.CV] UPDATED)
    We investigate algorithmic progress in image classification on ImageNet, perhaps the most well-known test bed for computer vision. We estimate a model, informed by work on neural scaling laws, and infer a decomposition of progress into the scaling of compute, data, and algorithms. Using Shapley values to attribute performance improvements, we find that algorithmic improvements have been roughly as important as the scaling of compute for progress computer vision. Our estimates indicate that algorithmic innovations mostly take the form of compute-augmenting algorithmic advances (which enable researchers to get better performance from less compute), not data-augmenting algorithmic advances. We find that compute-augmenting algorithmic advances are made at a pace more than twice as fast as the rate usually associated with Moore's law. In particular, we estimate that compute-augmenting innovations halve compute requirements every nine months (95\% confidence interval: 4 to 25 months).
    Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. (arXiv:2211.14645v2 [math.OC] UPDATED)
    We propose a globally-accelerated, first-order method for the optimization of smooth and (strongly or not) geodesically-convex functions in a wide class of Hadamard manifolds. We achieve the same convergence rates as Nesterov's accelerated gradient descent, up to a multiplicative geometric penalty and log factors. Crucially, we can enforce our method to stay within a compact set we define. Prior fully accelerated works \emph{resort to assuming} that the iterates of their algorithms stay in some pre-specified compact set, except for two previous methods of limited applicability. For our manifolds, this solves the open question in [KY22] about obtaining global general acceleration without iterates assumptively staying in the feasible set. In our solution, we design an accelerated Riemannian inexact proximal point algorithm, which is a result that was unknown even with exact access to the proximal operator, and is of independent interest. For smooth functions, we show we can implement the prox step inexactly with first-order methods in Riemannian balls of certain diameter that is enough for global accelerated optimization.
    The Benefits of Model-Based Generalization in Reinforcement Learning. (arXiv:2211.02222v2 [cs.LG] UPDATED)
    Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be considered a simple kind of model, which has proved extremely effective at improving the stability and efficiency of deep RL. In principle, a learned parametric model could improve on ER by generalizing from real experience to augment the dataset with additional plausible experience. However, owing to the many design choices involved in empirically successful algorithms, it can be very hard to establish where the benefits are actually coming from. Here, we provide theoretical and empirical insight into when, and how, we can expect data generated by a learned model to be useful. First, we provide a general theorem motivating how learning a model as an intermediate step can narrow down the set of possible value functions more than learning a value function directly from data using the Bellman equation. Second, we provide an illustrative example showing empirically how a similar effect occurs in a more concrete setting with neural network function approximation. Finally, we provide extensive experiments showing the benefit of model-based learning for online RL in environments with combinatorial complexity, but factored structure that allows a learned model to generalize. In these experiments, we take care to control for other factors in order to isolate, insofar as possible, the benefit of using experience generated by a learned model relative to ER alone.
    Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks. (arXiv:2210.13014v2 [cs.LG] UPDATED)
    We study a new paradigm of knowledge transfer that aims at encoding graph topological information into graph neural networks (GNNs) by distilling knowledge from a teacher GNN model trained on a complete graph to a student GNN model operating on a smaller or sparser graph. To this end, we revisit the connection between thermodynamics and the behavior of GNN, based on which we propose Neural Heat Kernel (NHK) to encapsulate the geometric property of the underlying manifold concerning the architecture of GNNs. A fundamental and principled solution is derived by aligning NHKs on teacher and student models, dubbed as Geometric Knowledge Distillation. We develop non- and parametric instantiations and demonstrate their efficacy in various experimental settings for knowledge distillation regarding different types of privileged topological information and teacher-student schemes.
    SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning. (arXiv:2211.12509v2 [cs.LG] UPDATED)
    Recent years have witnessed remarkable advances in spatiotemporal predictive learning, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. Although impressive, the system complexity of mainstream methods is increasing as well, which may hinder the convenient applications. This paper proposes SimVP, a simple spatiotemporal predictive baseline model that is completely built upon convolutional networks without recurrent architectures and trained by common mean squared error loss in an end-to-end fashion. Without introducing any extra tricks and strategies, SimVP can achieve superior performance on various benchmark datasets. To further improve the performance, we derive variants with the gated spatiotemporal attention translator from SimVP that can achieve better performance. We demonstrate that SimVP has strong generalization and extensibility on real-world datasets through extensive experiments. The significant reduction in training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to benefit the spatiotemporal predictive learning community.
    torchode: A Parallel ODE Solver for PyTorch. (arXiv:2210.12375v2 [cs.LG] UPDATED)
    We introduce an ODE solver for the PyTorch ecosystem that can solve multiple ODEs in parallel independently from each other while achieving significant performance gains. Our implementation tracks each ODE's progress separately and is carefully optimized for GPUs and compatibility with PyTorch's JIT compiler. Its design lets researchers easily augment any aspect of the solver and collect and analyze internal solver statistics. In our experiments, our implementation is up to 4.3 times faster per step than other ODE solvers and it is robust against within-batch interactions that lead other solvers to take up to 4 times as many steps. Code available at https://github.com/martenlienen/torchode
    Black-box Coreset Variational Inference. (arXiv:2211.02377v2 [stat.ML] UPDATED)
    Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly accelerating subsequent downstream tasks. Existing variational coreset constructions rely on either selecting subsets of the observed datapoints, or jointly performing approximate inference and optimizing pseudodata in the observed space akin to inducing points methods in Gaussian Processes. So far, both approaches are limited by complexities in evaluating their objectives for general purpose models, and require generating samples from a typically intractable posterior over the coreset throughout inference and testing. In this work, we present a black-box variational inference framework for coresets that overcomes these constraints and enables principled application of variational coresets to intractable models, such as Bayesian neural networks. We apply our techniques to supervised learning problems, and compare them with existing approaches in the literature for data summarization and inference.
    Synthetic Dataset Generation for Privacy-Preserving Machine Learning. (arXiv:2210.03205v4 [cs.CR] UPDATED)
    Machine Learning (ML) has achieved enormous success in solving a variety of problems in computer vision, speech recognition, object detection, to name a few. The principal reason for this success is the availability of huge datasets for training deep neural networks (DNNs). However, datasets cannot be publicly released if they contain sensitive information such as medical records, and data privacy becomes a major concern. Encryption methods could be a possible solution, however their deployment on ML applications seriously impacts classification accuracy and results in substantial computational overhead. Alternatively, obfuscation techniques could be used, but maintaining a good trade-off between visual privacy and accuracy is challenging. In this paper, we propose a method to generate secure synthetic datasets from the original private datasets. Given a network with Batch Normalization (BN) layers pretrained on the original dataset, we first record the class-wise BN layer statistics. Next, we generate the synthetic dataset by optimizing random noise such that the synthetic data match the layer-wise statistical distribution of original images. We evaluate our method on image classification datasets (CIFAR10, ImageNet) and show that synthetic data can be used in place of the original CIFAR10/ImageNet data for training networks from scratch, producing comparable classification performance. Further, to analyze visual privacy provided by our method, we use Image Quality Metrics and show high degree of visual dissimilarity between the original and synthetic images. Moreover, we show that our proposed method preserves data-privacy under various privacy-leakage attacks including Gradient Matching Attack, Model Memorization Attack, and GAN-based Attack.
    Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time. (arXiv:2211.14238v2 [cs.LG] UPDATED)
    Distribution shift occurs when the test distribution differs from the training distribution, and it can considerably degrade performance of machine learning models deployed in the real world. Temporal shifts -- distribution shifts arising from the passage of time -- often occur gradually and have the additional structure of timestamp metadata. By leveraging timestamp metadata, models can potentially learn from trends in past distribution shifts and extrapolate into the future. While recent works have studied distribution shifts, temporal shifts remain underexplored. To address this gap, we curate Wild-Time, a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. We use two evaluation strategies: evaluation with a fixed time split (Eval-Fix) and evaluation with a data stream (Eval-Stream). Eval-Fix, our primary evaluation strategy, aims to provide a simple evaluation protocol, while Eval-Stream is more realistic for certain real-world applications. Under both evaluation strategies, we observe an average performance drop of 20% from in-distribution to out-of-distribution data. Existing methods are unable to close this gap. Code is available at https://wild-time.github.io/.
    Local Bayesian optimization via maximizing probability of descent. (arXiv:2210.11662v2 [cs.LG] UPDATED)
    Local optimization presents a promising approach to expensive, high-dimensional black-box optimization by sidestepping the need to globally explore the search space. For objective functions whose gradient cannot be evaluated directly, Bayesian optimization offers one solution -- we construct a probabilistic model of the objective, design a policy to learn about the gradient at the current location, and use the resulting information to navigate the objective landscape. Previous work has realized this scheme by minimizing the variance in the estimate of the gradient, then moving in the direction of the expected gradient. In this paper, we re-examine and refine this approach. We demonstrate that, surprisingly, the expected value of the gradient is not always the direction maximizing the probability of descent, and in fact, these directions may be nearly orthogonal. This observation then inspires an elegant optimization scheme seeking to maximize the probability of descent while moving in the direction of most-probable descent. Experiments on both synthetic and real-world objectives show that our method outperforms previous realizations of this optimization scheme and is competitive against other, significantly more complicated baselines.
    Betting the system: Using lineups to predict football scores. (arXiv:2210.06327v3 [cs.LG] UPDATED)
    This paper aims to reduce randomness in football by analysing the role of lineups in final scores using machine learning prediction models we have developed. Football clubs invest millions of dollars on lineups and knowing how individual statistics translate to better outcomes can optimise investments. Moreover, sports betting is growing exponentially and being able to predict the future is profitable and desirable. We use machine learning models and historical player data from English Premier League (2020-2022) to predict scores and to understand how individual performance can improve the outcome of a match. We compared different prediction techniques to maximise the possibility of finding useful models. We created heuristic and machine learning models predicting football scores to compare different techniques. We used different sets of features and shown goalkeepers stats are more important than attackers stats to predict goals scored. We applied a broad evaluation process to assess the efficacy of the models in real world applications. We managed to predict correctly all relegated teams after forecast 100 consecutive matches. We show that Support Vector Regression outperformed other techniques predicting final scores and that lineups do not improve predictions. Finally, our model was profitable (42% return) when emulating a betting system using real world odds data.
    Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment. (arXiv:2210.13005v2 [cs.LG] UPDATED)
    The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment. In practice, the next-event prediction models are trained with sequential data collected at one time and need to generalize to newly arrived sequences in remote future, which requires models to handle temporal distribution shift from training to testing. In this paper, we first take a data-generating perspective to reveal a negative result that existing approaches with maximum likelihood estimation would fail for distribution shift due to the latent context confounder, i.e., the common cause for the historical events and the next event. Then we devise a new learning objective based on backdoor adjustment and further harness variational inference to make it tractable for sequence learning problems. On top of that, we propose a framework with hierarchical branching structures for learning context-specific representations. Comprehensive experiments on diverse tasks (e.g., sequential recommendation) demonstrate the effectiveness, applicability and scalability of our method with various off-the-shelf models as backbones.
    An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games. (arXiv:2211.01280v2 [math.OC] UPDATED)
    We consider the problem of computing mixed Nash equilibria of two-player zero-sum games with continuous sets of pure strategies and with first-order access to the payoff function. This problem arises for example in game-theory-inspired machine learning applications, such as distributionally-robust learning. In those applications, the strategy sets are high-dimensional and thus methods based on discretisation cannot tractably return high-accuracy solutions. In this paper, we introduce and analyze a particle-based method that enjoys guaranteed local convergence for this problem. This method consists in parametrizing the mixed strategies as atomic measures and applying proximal point updates to both the atoms' weights and positions. It can be interpreted as a time-implicit discretization of the "interacting" Wasserstein-Fisher-Rao gradient flow. We prove that, under non-degeneracy assumptions, this method converges at an exponential rate to the exact mixed Nash equilibrium from any initialization satisfying a natural notion of closeness to optimality. We illustrate our results with numerical experiments and discuss applications to max-margin and distributionally-robust classification using two-layer neural networks, where our method has a natural interpretation as a simultaneous training of the network's weights and of the adversarial distribution.
    Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input. (arXiv:2209.08752v2 [cs.RO] UPDATED)
    Great success has been achieved in the 6-DoF grasp learning from the point cloud input, yet the computational cost due to the point set orderlessness remains a concern. Alternatively, we explore the grasp generation from the RGB-D input in this paper. The proposed solution, Keypoint-GraspNet, detects the projection of the gripper keypoints in the image space and then recover the SE(3) poses with a PnP algorithm. A synthetic dataset based on the primitive shape and the grasp family is constructed to examine our idea. Metric-based evaluation reveals that our method outperforms the baselines in terms of the grasp proposal accuracy, diversity, and the time cost. Finally, robot experiments show high success rate, demonstrating the potential of the idea in the real-world applications.
    Deep Counterfactual Estimation with Categorical Background Variables. (arXiv:2210.05811v4 [cs.LG] UPDATED)
    Referred to as the third rung of the causal inference ladder, counterfactual queries typically ask the "What if ?" question retrospectively. The standard approach to estimate counterfactuals resides in using a structural equation model that accurately reflects the underlying data generating process. However, such models are seldom available in practice and one usually wishes to infer them from observational data alone. Unfortunately, the correct structural equation model is in general not identifiable from the observed factual distribution. Nevertheless, in this work, we show that under the assumption that the main latent contributors to the treatment responses are categorical, the counterfactuals can be still reliably predicted. Building upon this assumption, we introduce CounterFactual Query Prediction (CFQP), a novel method to infer counterfactuals from continuous observations when the background variables are categorical. We show that our method significantly outperforms previously available deep-learning-based counterfactual methods, both theoretically and empirically on time series and image data. Our code is available at https://github.com/edebrouwer/cfqp.
    Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming. (arXiv:2210.01515v3 [cs.LG] UPDATED)
    Internet based businesses and products (e.g. e-commerce, music streaming) are becoming more and more sophisticated every day with a lot of focus on improving customer satisfaction. A core way they achieve this is by providing customers with an easy access to their products by structuring them in catalogues using navigation bars and providing recommendations. We refer to these catalogues as product concepts, e.g. product categories on e-commerce websites, public playlists on music streaming platforms. These product concepts typically contain products that are linked with each other through some common features (e.g. a playlist of songs by the same artist). How they are defined in the backend of the system can be different for different products. In this work, we represent product concepts using database queries and tackle two learning problems. First, given sets of products that all belong to the same unknown product concept, we learn a database query that is a representation of this product concept. Second, we learn product concepts and their corresponding queries when the given sets of products are associated with multiple product concepts. To achieve these goals, we propose two approaches that combine the concepts of PU learning with Decision Trees and Clustering. Our experiments demonstrate, via a simulated setup for a music streaming service, that our approach is effective in solving these problems.
    Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions. (arXiv:2210.07236v3 [cs.LG] UPDATED)
    A deep neural network using rectified linear units represents a continuous piecewise linear (CPWL) function and vice versa. Recent results in the literature estimated that the number of neurons needed to exactly represent any CPWL function grows exponentially with the number of pieces or exponentially in terms of the factorial of the number of distinct linear components. Moreover, such growth is amplified linearly with the input dimension. These existing results seem to indicate that the cost of representing a CPWL function is expensive. In this paper, we propose much tighter bounds and establish a polynomial time algorithm to find a network satisfying these bounds for any given CPWL function. We prove that the number of hidden neurons required to exactly represent any CPWL function is at most a quadratic function of the number of pieces. In contrast to all previous results, this upper bound is invariant to the input dimension. Besides the number of pieces, we also study the number of distinct linear components in CPWL functions. When such a number is also given, we prove that the quadratic complexity turns into bilinear, which implies a lower neural complexity because the number of distinct linear components is always not greater than the minimum number of pieces in a CPWL function. When the number of pieces is unknown, we prove that, in terms of the number of distinct linear components, the neural complexities of any CPWL function are at most polynomial growth for low-dimensional inputs and factorial growth for the worst-case scenario, which are significantly better than existing results in the literature.
    Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction. (arXiv:2208.05119v5 [cs.LG] UPDATED)
    Molecular Representation Learning is essential to solving many drug discovery and computational chemistry problems. It is a challenging problem due to the complex structure of molecules and the vast chemical space. Graph representations of molecules are more expressive than traditional representations, such as molecular fingerprints. Therefore, they can improve the performance of machine learning models. We propose SeMole, a method that augments the Junction Tree Variational Autoencoders, a state-of-the-art generative model for molecular graphs, with semi-supervised learning. SeMole aims to improve the accuracy of molecular property prediction when having limited labeled data by exploiting unlabeled data. We enforce that the model generates molecular graphs conditioned on target properties by incorporating the property into the latent representation. We propose an additional pre-training phase to improve the training process for our semi-supervised generative model. We perform an experimental evaluation on the ZINC dataset using three different molecular properties and demonstrate the benefits of semi-supervision.
    Neural Observer with Lyapunov Stability Guarantee for Uncertain Nonlinear Systems. (arXiv:2208.13006v2 [math.OC] UPDATED)
    In this paper, we propose a novel nonlinear observer based on neural networks, called neural observer, for observation tasks of linear time-invariant (LTI) systems and uncertain nonlinear systems. In particular, the neural observer designed for uncertain systems is inspired by the active disturbance rejection control, which can measure the uncertainty in real-time. The stability analysis (e.g., exponential convergence rate) of LTI and uncertain nonlinear systems (involving neural observers) are presented and guaranteed, where it is shown that the observation problems can be solved only using the linear matrix inequalities (LMIs). Also, it is revealed that the observability and controllability of the system matrices are required to demonstrate the existence of solutions of LMIs. Finally, the effectiveness of neural observers is verified on three simulation cases, including the X-29A aircraft model, the nonlinear pendulum, and the four-wheel steering vehicle.
    Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis. (arXiv:2210.13336v2 [eess.IV] UPDATED)
    Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).
    Learning Probabilistic Models from Generator Latent Spaces with Hat EBM. (arXiv:2210.16486v2 [cs.CV] UPDATED)
    This work proposes a method for using any generator network as the foundation of an Energy-Based Model (EBM). Our formulation posits that observed images are the sum of unobserved latent variables passed through the generator network and a residual random variable that spans the gap between the generator output and the image manifold. One can then define an EBM that includes the generator as part of its forward pass, which we call the Hat EBM. The model can be trained without inferring the latent variables of the observed data or calculating the generator Jacobian determinant. This enables explicit probabilistic modeling of the output distribution of any type of generator network. Experiments show strong performance of the proposed method on (1) unconditional ImageNet synthesis at 128x128 resolution, (2) refining the output of existing generators, and (3) learning EBMs that incorporate non-probabilistic generators. Code and pretrained models to reproduce our results are available at https://github.com/point0bar1/hat-ebm.
    Competition, Alignment, and Equilibria in Digital Marketplaces. (arXiv:2208.14423v2 [cs.GT] UPDATED)
    Competition between traditional platforms is known to improve user utility by aligning the platform's actions with user preferences. But to what extent is alignment exhibited in data-driven marketplaces? To study this question from a theoretical perspective, we introduce a duopoly market where platform actions are bandit algorithms and the two platforms compete for user participation. A salient feature of this market is that the quality of recommendations depends on both the bandit algorithm and the amount of data provided by interactions from users. This interdependency between the algorithm performance and the actions of users complicates the structure of market equilibria and their quality in terms of user utility. Our main finding is that competition in this market does not perfectly align market outcomes with user utility. Interestingly, market outcomes exhibit misalignment not only when the platforms have separate data repositories, but also when the platforms have a shared data repository. Nonetheless, the data sharing assumptions impact what mechanism drives misalignment and also affect the specific form of misalignment (e.g. the quality of the best-case and worst-case market outcomes). More broadly, our work illustrates that competition in digital marketplaces has subtle consequences for user utility that merit further investigation.
    Pattern Attention Transformer with Doughnut Kernel. (arXiv:2211.16961v2 [cs.CV] UPDATED)
    We present in this paper a new architecture, the Pattern Attention Transformer (PAT), that is composed of the new doughnut kernel. Compared with tokens in the NLP field, Transformer in computer vision has the problem of handling the high resolution of pixels in images. In ViT, an image is cut into square-shaped patches. As the follow-up of ViT, Swin Transformer proposes an additional step of shifting to decrease the existence of fixed boundaries, which also incurs 'two connected Swin Transformer blocks' as the minimum unit of the model. Inheriting the patch/window idea, our doughnut kernel enhances the design of patches further. It replaces the line-cut boundaries with two types of areas: sensor and updating, which is based on the comprehension of self-attention (named QKVA grid). The doughnut kernel also brings a new topic about the shape of kernels beyond square. To verify its performance on image classification, PAT is designed with Transformer blocks of regular octagon shape doughnut kernels. Its architecture is lighter: the minimum pattern attention layer is only one for each stage. Under similar complexity of computation, its performances on ImageNet 1K reach higher throughput (+10\%) and surpass Swin Transformer (+0.1 acc1).
    Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features. (arXiv:2206.12815v2 [eess.IV] UPDATED)
    Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to come up with the framework for early detection, classification and diagnosis. Artificial intelligence research community in coordination with medical practitioners are developing such frameworks to automate the task of detection. With the surge in research activities coupled with availability of large datasets and enhanced computational powers, it expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for classification of breast cancer using mammograms is proposed. The proposed framework combines robust features extracted from novel Convolutional Neural Network (CNN) features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on CBIS-DDSM dataset exceed state of the art.
    Quantized Training of Gradient Boosting Decision Trees. (arXiv:2207.09682v2 [cs.LG] UPDATED)
    Recent years have witnessed significant success in Gradient Boosting Decision Trees (GBDT) for a wide range of machine learning applications. Generally, a consensus about GBDT's training algorithms is gradients and statistics are computed based on high-precision floating points. In this paper, we investigate an essentially important question which has been largely ignored by the previous literature: how many bits are needed for representing gradients in training GBDT? To solve this mystery, we propose to quantize all the high-precision gradients in a very simple yet effective way in the GBDT's training algorithm. Surprisingly, both our theoretical analysis and empirical studies show that the necessary precisions of gradients without hurting any performance can be quite low, e.g., 2 or 3 bits. With low-precision gradients, most arithmetic operations in GBDT training can be replaced by integer operations of 8, 16, or 32 bits. Promisingly, these findings may pave the way for much more efficient training of GBDT from several aspects: (1) speeding up the computation of gradient statistics in histograms; (2) compressing the communication cost of high-precision statistical information during distributed training; (3) the inspiration of utilization and development of hardware architectures which well support low-precision computation for GBDT training. Benchmarked on CPUs, GPUs, and distributed clusters, we observe up to 2$\times$ speedup of our simple quantization strategy compared with SOTA GBDT systems on extensive datasets, demonstrating the effectiveness and potential of the low-precision training of GBDT. The code will be released to the official repository of LightGBM.
    Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery. (arXiv:2209.02564v2 [cs.CV] UPDATED)
    Images in aerial datasets are very large in resolution, and each frame contains many dense and small objects. State-of-the-art detection methods fail to capture small objects, local features, and region proposals for densely overlapped objects in aerial imagery due to the high variation of object sizes in satellite imagery with respect to the image size and high variation of content. Aerial imagery content varies greatly within the dataset due to the large change in lighting conditions, and the type of ground imagery captures from high altitudes. The variation is even higher between different datasets as object sizes, class distributions, image acquisition, and weather conditions can vary even more drastically. Thus, Domain Adaptation (DA) has been introduced as a band-aid to alleviate the degradation of object identification in previously unseen datasets. In this paper, we propose a small object detection pipeline that improves the feature extraction process by spatial pyramid pooling, cross-stage partial networks, heat-map-based region proposal network, and objects localization and identification through a novel image difficulty score that adapts the overall focal loss measure based on the image difficulty. Next, we propose novel contrastive learning with progressive domain adaptation to produce domain-invariant features across aerial datasets using local and global features. Effective analysis and illustration of different performance metrics and challenges show that our proposed method is comparable to the current State-of-Art models and creates a first-ever Domain Adaptation benchmark for the object detection task in highly imbalanced satellite datasets with large domain gaps and dominant small objects.
    Sequence Model Imitation Learning with Unobserved Contexts. (arXiv:2208.02225v3 [cs.LG] UPDATED)
    We consider imitation learning problems where the learner's ability to mimic the expert increases throughout the course of an episode as more information is revealed. One example of this is when the expert has access to privileged information: while the learner might not be able to accurately reproduce expert behavior early on in an episode, by considering the entire history of states and actions, they might be able to eventually identify the hidden context and act as the expert would. We prove that on-policy imitation learning algorithms (with or without access to a queryable expert) are better equipped to handle these sorts of asymptotically realizable problems than off-policy methods. This is because on-policy algorithms provably learn to recover from their initially suboptimal actions, while off-policy methods treat their suboptimal past actions as though they came from the expert. This often manifests as a latching behavior: a naive repetition of past actions. We conduct experiments in a toy bandit domain that show that there exist sharp phase transitions of whether off-policy approaches are able to match expert performance asymptotically, in contrast to the uniformly good performance of on-policy approaches. We demonstrate that on several continuous control tasks, on-policy approaches are able to use history to identify the context while off-policy approaches actually perform worse when given access to history.
    ViNL: Visual Navigation and Locomotion Over Obstacles. (arXiv:2210.14791v2 [cs.RO] UPDATED)
    We present Visual Navigation and Locomotion over obstacles (ViNL), which enables a quadrupedal robot to navigate unseen apartments while stepping over small obstacles that lie in its path (e.g., shoes, toys, cables), similar to how humans and pets lift their feet over objects as they walk. ViNL consists of: (1) a visual navigation policy that outputs linear and angular velocity commands that guides the robot to a goal coordinate in unfamiliar indoor environments; and (2) a visual locomotion policy that controls the robot's joints to avoid stepping on obstacles while following provided velocity commands. Both the policies are entirely "model-free", i.e. sensors-to-actions neural networks trained end-to-end. The two are trained independently in two entirely different simulators and then seamlessly co-deployed by feeding the velocity commands from the navigator to the locomotor, entirely "zero-shot" (without any co-training). While prior works have developed learning methods for visual navigation or visual locomotion, to the best of our knowledge, this is the first fully learned approach that leverages vision to accomplish both (1) intelligent navigation in new environments, and (2) intelligent visual locomotion that aims to traverse cluttered environments without disrupting obstacles. On the task of navigation to distant goals in unknown environments, ViNL using just egocentric vision significantly outperforms prior work on robust locomotion using privileged terrain maps (+32.8% success and -4.42 collisions per meter). Additionally, we ablate our locomotion policy to show that each aspect of our approach helps reduce obstacle collisions. Videos and code at this http URL
    Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment. (arXiv:2208.08726v2 [eess.SP] UPDATED)
    A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. However, existing fast graph sampling schemes are designed and tested only for positive graphs describing positive correlations. In this paper, we show that for datasets with strong inherent anti-correlations, a suitable graph contains both positive and negative edge weights. In response, we propose a linear-time signed graph sampling method centered on the concept of balanced signed graphs. Specifically, given an empirical covariance data matrix $\bar{\bf{C}}$, we first learn a sparse inverse matrix (graph Laplacian) $\mathcal{L}$ corresponding to a signed graph $\mathcal{G}$. We define the eigenvectors of Laplacian $\mathcal{L}_B$ for a balanced signed graph $\mathcal{G}_B$ -- approximating $\mathcal{G}$ via edge weight augmentation -- as graph frequency components. Next, we choose samples to minimize the low-pass filter reconstruction error in two steps. We first align all Gershgorin disc left-ends of Laplacian $\mathcal{L}_B$ at smallest eigenvalue $\lambda_{\min}(\mathcal{L}_B)$ via similarity transform $\mathcal{L}_p = \S \mathcal{L}_B \S^{-1}$, leveraging a recent linear algebra theorem called Gershgorin disc perfect alignment (GDPA). We then perform sampling on $\mathcal{L}_p$ using a previous fast Gershgorin disc alignment sampling (GDAS) scheme. Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.
    Linear Convergence of ISTA and FISTA. (arXiv:2212.06319v2 [math.OC] UPDATED)
    In this paper, we revisit the class of iterative shrinkage-thresholding algorithms (ISTA) for solving the linear inverse problem with sparse representation, which arises in signal and image processing. It is shown in the numerical experiment to deblur an image that the convergence behavior in the logarithmic-scale ordinate tends to be linear instead of logarithmic, approximating to be flat. Making meticulous observations, we find that the previous assumption for the smooth part to be convex weakens the least-square model. Specifically, assuming the smooth part to be strongly convex is more reasonable for the least-square model, even though the image matrix is probably ill-conditioned. Furthermore, we improve the pivotal inequality tighter for composite optimization with the smooth part to be strongly convex instead of general convex, which is first found in [Li et al., 2022]. Based on this pivotal inequality, we generalize the linear convergence to composite optimization in both the objective value and the squared proximal subgradient norm. Meanwhile, we set a simple ill-conditioned matrix which is easy to compute the singular values instead of the original blur matrix. The new numerical experiment shows the proximal generalization of Nesterov's accelerated gradient descent (NAG) for the strongly convex function has a faster linear convergence rate than ISTA. Based on the tighter pivotal inequality, we also generalize the faster linear convergence rate to composite optimization, in both the objective value and the squared proximal subgradient norm, by taking advantage of the well-constructed Lyapunov function with a slight modification and the phase-space representation based on the high-resolution differential equation framework from the implicit-velocity scheme.
    Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. (arXiv:2209.03695v3 [cs.LG] UPDATED)
    A fundamental property of deep learning normalization techniques, such as batch normalization, is making the pre-normalization parameters scale invariant. The intrinsic domain of such parameters is the unit sphere, and therefore their gradient optimization dynamics can be represented via spherical optimization with varying effective learning rate (ELR), which was studied previously. However, the varying ELR may obscure certain characteristics of the intrinsic loss landscape structure. In this work, we investigate the properties of training scale-invariant neural networks directly on the sphere using a fixed ELR. We discover three regimes of such training depending on the ELR value: convergence, chaotic equilibrium, and divergence. We study these regimes in detail both on a theoretical examination of a toy example and on a thorough empirical analysis of real scale-invariant deep learning models. Each regime has unique features and reflects specific properties of the intrinsic loss landscape, some of which have strong parallels with previous research on both regular and scale-invariant neural networks training. Finally, we demonstrate how the discovered regimes are reflected in conventional training of normalized networks and how they can be leveraged to achieve better optima.
    Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis. (arXiv:2208.07448v4 [cs.LG] UPDATED)
    Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detection of seizure events and brain regions in which seizure happens or initiates. However, all existing machine learning-based algorithms for seizure analysis require access to the labeled seizure data while acquiring labeled data is very labor intensive, expensive, as well as clinicians dependent given the subjective nature of the visual qualitative interpretation of EEG signals. In this paper, we propose to detect seizure channels and clips in a self-supervised manner where no access to the seizure data is needed. The proposed method considers local structural and contextual information embedded in EEG graphs by employing positive and negative sub-graphs. We train our method through minimizing contrastive and generative losses. The employ of local EEG sub-graphs makes the algorithm an appropriate choice when accessing to the all EEG channels is impossible due to complications such as skull fractures. We conduct an extensive set of experiments on the largest seizure dataset and demonstrate that our proposed framework outperforms the state-of-the-art methods in the EEG-based seizure study. The proposed method is the only study that requires no access to the seizure data in its training phase, yet establishes a new state-of-the-art to the field, and outperforms all related supervised methods.
    Dilated Neighborhood Attention Transformer. (arXiv:2209.15001v3 [cs.CV] UPDATED)
    Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have also gained significant attention, thanks to their performance and easy integration into existing frameworks. These models typically employ localized attention mechanisms, such as the sliding-window Neighborhood Attention (NA) or Swin Transformer's Shifted Window Self Attention. While effective at reducing self attention's quadratic complexity, local attention weakens two of the most desirable properties of self attention: long range inter-dependency modeling, and global receptive field. In this paper, we introduce Dilated Neighborhood Attention (DiNA), a natural, flexible and efficient extension to NA that can capture more global context and expand receptive fields exponentially at no additional cost. NA's local attention and DiNA's sparse global attention complement each other, and therefore we introduce Dilated Neighborhood Attention Transformer (DiNAT), a new hierarchical vision transformer built upon both. DiNAT variants enjoy significant improvements over strong baselines such as NAT, Swin, and ConvNeXt. Our large model is faster and ahead of its Swin counterpart by 1.6% box AP in COCO object detection, 1.4% mask AP in COCO instance segmentation, and 1.4% mIoU in ADE20K semantic segmentation. Paired with new frameworks, our large variant is the new state of the art panoptic segmentation model on COCO (58.5 PQ) and ADE20K (49.4 PQ), and instance segmentation model on Cityscapes (45.1 AP) and ADE20K (35.4 AP) (no extra data). It also matches the state of the art specialized semantic segmentation models on ADE20K (58.1 mIoU), and ranks second on Cityscapes (84.5 mIoU) (no extra data).
    Tightening Discretization-based MILP Models for the Pooling Problem using Upper Bounds on Bilinear Terms. (arXiv:2207.03699v2 [math.OC] UPDATED)
    Discretization-based methods have been proposed for solving nonconvex optimization problems with bilinear terms such as the pooling problem. These methods convert the original nonconvex optimization problems into mixed-integer linear programs (MILPs). In this paper we study tightening methods for these MILP models for the pooling problem, and derive valid constraints using upper bounds on bilinear terms. Computational results demonstrate the effectiveness of our methods in terms of reducing solution time.
    Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks. (arXiv:2207.03952v2 [cs.RO] UPDATED)
    Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favorable to learning. While recent advances have been made in applying modern learning techniques to muscle-actuated systems both in simulation as well as in robotics, so far, no detailed analysis has been performed to show the benefits of muscles when learning from scratch. Our study closes this gap and showcases the potential of muscle actuators for core robotics challenges in terms of data-efficiency, hyperparameter sensitivity, and robustness.
    Adapting to Online Label Shift with Provable Guarantees. (arXiv:2207.02121v3 [cs.LG] UPDATED)
    The standard supervised learning paradigm works effectively when training data shares the same distribution as the upcoming testing samples. However, this stationary assumption is often violated in real-world applications, especially when testing data appear in an online fashion. In this paper, we formulate and investigate the problem of \emph{online label shift} (OLaS): the learner trains an initial model from the labeled offline data and then deploys it to an unlabeled online environment where the underlying label distribution changes over time but the label-conditional density does not. The non-stationarity nature and the lack of supervision make the problem challenging to be tackled. To address the difficulty, we construct a new unbiased risk estimator that utilizes the unlabeled data, which exhibits many benign properties albeit with potential non-convexity. Building upon that, we propose novel online ensemble algorithms to deal with the non-stationarity of the environments. Our approach enjoys optimal \emph{dynamic regret}, indicating that the performance is competitive with a clairvoyant who knows the online environments in hindsight and then chooses the best decision for each round. The obtained dynamic regret bound scales with the intensity and pattern of label distribution shift, hence exhibiting the adaptivity in the OLaS problem. Extensive experiments are conducted to validate the effectiveness and support our theoretical findings.
    Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. (arXiv:2207.08799v3 [cs.LG] UPDATED)
    There is mounting evidence of emergent phenomena in the capabilities of deep learning methods as we scale up datasets, model sizes, and training times. While there are some accounts of how these resources modulate statistical capacity, far less is known about their effect on the computational problem of model training. This work conducts such an exploration through the lens of learning a $k$-sparse parity of $n$ bits, a canonical discrete search problem which is statistically easy but computationally hard. Empirically, we find that a variety of neural networks successfully learn sparse parities, with discontinuous phase transitions in the training curves. On small instances, learning abruptly occurs at approximately $n^{O(k)}$ iterations; this nearly matches SQ lower bounds, despite the apparent lack of a sparse prior. Our theoretical analysis shows that these observations are not explained by a Langevin-like mechanism, whereby SGD "stumbles in the dark" until it finds the hidden set of features (a natural algorithm which also runs in $n^{O(k)}$ time). Instead, we show that SGD gradually amplifies the sparse solution via a Fourier gap in the population gradient, making continual progress that is invisible to loss and error metrics.
    OpenXAI: Towards a Transparent Evaluation of Model Explanations. (arXiv:2206.11104v3 [cs.LG] UPDATED)
    While several types of post hoc explanation methods (e.g., feature attribution methods) have been proposed in recent literature, there is little to no work on systematically benchmarking these methods in an efficient and transparent manner. Here, we introduce OpenXAI, a comprehensive and extensible open source framework for evaluating and benchmarking post hoc explanation methods. OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, (ii) open-source implementations of twenty-two quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, and (iii) the first ever public XAI leaderboards to benchmark explanations. OpenXAI is easily extensible, as users can readily evaluate custom explanation methods and incorporate them into our leaderboards. Overall, OpenXAI provides an automated end-to-end pipeline that not only simplifies and standardizes the evaluation of post hoc explanation methods, but also promotes transparency and reproducibility in benchmarking these methods. OpenXAI datasets and data loaders, implementations of state-of-the-art explanation methods and evaluation metrics, as well as leaderboards are publicly available at https://open-xai.github.io/.
    Primal Dual Alternating Proximal Gradient Algorithms for Nonsmooth Nonconvex Minimax Problems with Coupled Linear Constraints. (arXiv:2212.04672v2 [math.OC] UPDATED)
    Nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose a primal dual alternating proximal gradient (PDAPG) algorithm and a primal dual proximal gradient (PDPG-L) algorithm for solving nonsmooth nonconvex-(strongly) concave and nonconvex-linear minimax problems with coupled linear constraints, respectively. The iteration complexity of the two algorithms are proved to be $\mathcal{O}\left( \varepsilon ^{-2} \right)$ (resp. $\mathcal{O}\left( \varepsilon ^{-4} \right)$) under nonconvex-strongly concave (resp. nonconvex-concave) setting and $\mathcal{O}\left( \varepsilon ^{-3} \right)$ under nonconvex-linear setting to reach an $\varepsilon$-stationary point, respectively. To our knowledge, they are the first two algorithms with iteration complexity guarantee for solving the nonconvex minimax problems with coupled linear constraints.
    Learning Deep Input-Output Stable Dynamics. (arXiv:2206.13093v3 [math.DS] UPDATED)
    Learning stable dynamics from observed time-series data is an essential problem in robotics, physical modeling, and systems biology. Many of these dynamics are represented as an inputs-output system to communicate with the external environment. In this study, we focus on input-output stable systems, exhibiting robustness against unexpected stimuli and noise. We propose a method to learn nonlinear systems guaranteeing the input-output stability. Our proposed method utilizes the differentiable projection onto the space satisfying the Hamilton-Jacobi inequality to realize the input-output stability. The problem of finding this projection can be formulated as a quadratic constraint quadratic programming problem, and we derive the particular solution analytically. Also, we apply our method to a toy bistable model and the task of training a benchmark generated from a glucose-insulin simulator. The results show that the nonlinear system with neural networks by our method achieves the input-output stability, unlike naive neural networks. Our code is available at https://github.com/clinfo/DeepIOStability.
    Evaluating Explainability for Graph Neural Networks. (arXiv:2208.09339v2 [cs.LG] UPDATED)
    As post hoc explanations are increasingly used to understand the behavior of graph neural networks (GNNs), it becomes crucial to evaluate the quality and reliability of GNN explanations. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no or unreliable ground-truth explanations for a given task. Here, we introduce a synthetic graph data generator, ShapeGGen, which can generate a variety of benchmark datasets (e.g., varying graph sizes, degree distributions, homophilic vs. heterophilic graphs) accompanied by ground-truth explanations. Further, the flexibility to generate diverse synthetic datasets and corresponding ground-truth explanations allows us to mimic the data generated by various real-world applications. We include ShapeGGen and several real-world graph datasets into an open-source graph explainability library, GraphXAI. In addition to synthetic and real-world graph datasets with ground-truth explanations, GraphXAI provides data loaders, data processing functions, visualizers, GNN model implementations, and evaluation metrics to benchmark the performance of GNN explainability methods.
    On the effectiveness of persistent homology. (arXiv:2206.10551v3 [math.AT] UPDATED)
    Persistent homology (PH) is one of the most popular methods in Topological Data Analysis. Even though PH has been used in many different types of applications, the reasons behind its success remain elusive; in particular, it is not known for which classes of problems it is most effective, or to what extent it can detect geometric or topological features. The goal of this work is to identify some types of problems where PH performs well or even better than other methods in data analysis. We consider three fundamental shape analysis tasks: the detection of the number of holes, curvature and convexity from 2D and 3D point clouds sampled from shapes. Experiments demonstrate that PH is successful in these tasks, outperforming several baselines, including PointNet, an architecture inspired precisely by the properties of point clouds. In addition, we observe that PH remains effective for limited computational resources and limited training data, as well as out-of-distribution test data, including various data transformations and noise. For convexity detection, we provide a theoretical guarantee that PH is effective for this task in $\mathbb{R}^d$, and demonstrate the detection of a convexity measure on the FLAVIA data set of plant leaf images. Due to the crucial role of shape classification in understanding mathematical and physical structures and objects, and in many applications, the findings of this work will provide some knowledge about the types of problems that are appropriate for PH, so that it can - to borrow the words from Wigner 1960 - ``remain valid in future research, and extend, to our pleasure", but to our lesser bafflement, to a variety of applications.
    Continual Prune-and-Select: Class-incremental learning with specialized subnetworks. (arXiv:2208.04952v2 [cs.LG] UPDATED)
    The human brain is capable of learning tasks sequentially mostly without forgetting. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning one task after another. We address this challenge considering a class-incremental learning scenario where the DNN sees test data without knowing the task from which this data originates. During training, Continual-Prune-and-Select (CP&S) finds a subnetwork within the DNN that is responsible for solving a given task. Then, during inference, CP&S selects the correct subnetwork to make predictions for that task. A new task is learned by training available neuronal connections of the DNN (previously untrained) to create a new subnetwork by pruning, which can include previously trained connections belonging to other subnetwork(s) because it does not update shared connections. This enables to eliminate catastrophic forgetting by creating specialized regions in the DNN that do not conflict with each other while still allowing knowledge transfer across them. The CP&S strategy is implemented with different subnetwork selection strategies, revealing superior performance to state-of-the-art continual learning methods tested on various datasets (CIFAR-100, CUB-200-2011, ImageNet-100 and ImageNet-1000). In particular, CP&S is capable of sequentially learning 10 tasks from ImageNet-1000 keeping an accuracy around 94% with negligible forgetting, a first-of-its-kind result in class-incremental learning. To the best of the authors' knowledge, this represents an improvement in accuracy above 10% when compared to the best alternative method.
    AutoML Two-Sample Test. (arXiv:2206.08843v3 [cs.LG] UPDATED)
    Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of many sophisticated test procedures going beyond the standard supervised learning frameworks, whose usage can require specialized knowledge about two-sample testing. We use a simple test that takes the mean discrepancy of a witness function as the test statistic and prove that minimizing a squared loss leads to a witness with optimal testing power. This allows us to leverage recent advancements in AutoML. Without any user input about the problems at hand, and using the same method for all our experiments, our AutoML two-sample test achieves competitive performance on a diverse distribution shift benchmark as well as on challenging two-sample testing problems. We provide an implementation of the AutoML two-sample test in the Python package autotst.
    Towards Interpreting Vulnerability of Multi-Instance Learning via Customized and Universal Adversarial Perturbations. (arXiv:2211.17071v2 [cs.CV] UPDATED)
    Multiple-Instance Learning (MIL) is a recent machine learning paradigm which is immensely useful in various real-life applications, like image analysis, video anomaly detection, text classification, etc. It is well known that most of the existing machine learning classifiers are highly vulnerable to adversarial perturbations. Since MIL is a weakly supervised learning, where information is available for a set of instances, called bag and not for every instances, adversarial perturbations can be fatal. In this paper, we have proposed two adversarial perturbation methods to analyze the effect of adversarial perturbations to interpret the vulnerability of MIL methods. Out of the two algorithms, one can be customized for every bag, and the other is a universal one, which can affect all bags in a given data set and thus has some generalizability. Through simulations, we have also shown the effectiveness of the proposed algorithms to fool the state-of-the-art (SOTA) MIL methods. Finally, we have discussed through experiments, about taking care of these kind of adversarial perturbations through a simple strategy. Source codes are available at https://github.com/InkiInki/MI-UAP.
    NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs. (arXiv:2206.04910v3 [cs.LG] UPDATED)
    The graph Transformer emerges as a new architecture and has shown superior performance on various graph mining tasks. In this work, we observe that existing graph Transformers treat nodes as independent tokens and construct a single long sequence composed of all node tokens so as to train the Transformer model, causing it hard to scale to large graphs due to the quadratical complexity on the number of nodes for the self-attention computation. To this end, we propose a Neighborhood Aggregation Graph Transformer (NAGphormer) that treats each node as a sequence containing a series of tokens constructed by our proposed Hop2Token module. For each node, Hop2Token aggregates the neighborhood features from different hops into different representations and thereby produces a sequence of token vectors as one input. In this way, NAGphormer could be trained in a mini-batch manner and thus could scale to large graphs. Moreover, we mathematically show that as compared to a category of advanced Graph Neural Networks (GNNs), the decoupled Graph Convolutional Network, NAGphormer could learn more informative node representations from the multi-hop neighborhoods. Extensive experiments on benchmark datasets from small to large are conducted to demonstrate that NAGphormer consistently outperforms existing graph Transformers and mainstream GNNs.
    Improved Algorithms for Neural Active Learning. (arXiv:2210.00423v3 [cs.LG] UPDATED)
    We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work. Then, the proposed algorithm leverages the powerful representation of NNs for both exploitation and exploration, has the query decision-maker tailored for $k$-class classification problems with the performance guarantee, utilizes the full feedback, and updates parameters in a more practical and efficient manner. These careful designs lead to an instance-dependent regret upper bound, roughly improving by a multiplicative factor $O(\log T)$ and removing the curse of input dimensionality. Furthermore, we show that the algorithm can achieve the same performance as the Bayes-optimal classifier in the long run under the hard-margin setting in classification problems. In the end, we use extensive experiments to evaluate the proposed algorithm and SOTA baselines, to show the improved empirical performance.
    A Search-Based Testing Approach for Deep Reinforcement Learning Agents. (arXiv:2206.07813v2 [cs.SE] UPDATED)
    Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challenges when deployed in safety-critical environments since they often exhibit erroneous behaviors that can lead to potentially critical errors. One way to assess the safety of DRL agents is to test them to detect possible faults leading to critical failures during their execution. This raises the question of how we can efficiently test DRL policies to ensure their correctness and adherence to safety requirements. Most existing works on testing DRL agents use adversarial attacks that perturb states or actions of the agent. However, such attacks often lead to unrealistic states of the environment. Their main goal is to test the robustness of DRL agents rather than testing the compliance of agents' policies with respect to requirements. Due to the huge state space of DRL environments, the high cost of test execution, and the black-box nature of DRL algorithms, the exhaustive testing of DRL agents is impossible. In this paper, we propose a Search-based Testing Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL agent by effectively searching for failing executions of the agent within a limited testing budget. We use machine learning models and a dedicated genetic algorithm to narrow the search towards faulty episodes. We apply STARLA on Deep-Q-Learning agents which are widely used as benchmarks and show that it significantly outperforms Random Testing by detecting more faults related to the agent's policy. We also investigate how to extract rules that characterize faulty episodes of the DRL agent using our search results. Such rules can be used to understand the conditions under which the agent fails and thus assess its deployment risks.
    Long Range Graph Benchmark. (arXiv:2206.08164v3 [cs.LG] UPDATED)
    Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given task on graphs. Recently, there has been an increasing interest in development of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI. However, MP-GNNs that simply rely on 1-hop message passing often fare better in several existing graph benchmarks when combined with positional feature representations, among other innovations, hence limiting the perceived utility and ranking of Transformer-like architectures. Here, we present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets: PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func and Peptides-struct that arguably require LRI reasoning to achieve strong performance in a given task. We benchmark both baseline GNNs and Graph Transformer networks to verify that the models which capture long-range dependencies perform significantly better on these tasks. Therefore, these datasets are suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.
    Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. (arXiv:2209.00735v2 [cs.LG] UPDATED)
    Neural networks (NNs) struggle to efficiently solve certain problems, such as learning parities, even when there are simple learning algorithms for those problems. Can NNs discover learning algorithms on their own? We exhibit a NN architecture that, in polynomial time, learns as well as any efficient learning algorithm describable by a constant-sized program. For example, on parity problems, the NN learns as well as Gaussian elimination, an efficient algorithm that can be succinctly described. Our architecture combines both recurrent weight sharing between layers and convolutional weight sharing to reduce the number of parameters down to a constant, even though the network itself may have trillions of nodes. While in practice the constants in our analysis are too large to be directly meaningful, our work suggests that the synergy of Recurrent and Convolutional NNs (RCNNs) may be more natural and powerful than either alone, particularly for concisely parameterizing discrete algorithms.
    RenyiCL: Contrastive Representation Learning with Skew Renyi Divergence. (arXiv:2208.06270v2 [stat.ML] UPDATED)
    Contrastive representation learning seeks to acquire useful representations by estimating the shared information between multiple views of data. Here, the choice of data augmentation is sensitive to the quality of learned representations: as harder the data augmentations are applied, the views share more task-relevant information, but also task-irrelevant one that can hinder the generalization capability of representation. Motivated by this, we present a new robust contrastive learning scheme, coined R\'enyiCL, which can effectively manage harder augmentations by utilizing R\'enyi divergence. Our method is built upon the variational lower bound of R\'enyi divergence, but a na\"ive usage of a variational method is impractical due to the large variance. To tackle this challenge, we propose a novel contrastive objective that conducts variational estimation of a skew R\'enyi divergence and provide a theoretical guarantee on how variational estimation of skew divergence leads to stable training. We show that R\'enyi contrastive learning objectives perform innate hard negative sampling and easy positive sampling simultaneously so that it can selectively learn useful features and ignore nuisance features. Through experiments on ImageNet, we show that R\'enyi contrastive learning with stronger augmentations outperforms other self-supervised methods without extra regularization or computational overhead. Moreover, we also validate our method on other domains such as graph and tabular, showing empirical gain over other contrastive methods.
    The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization. (arXiv:2205.14546v2 [cs.LG] UPDATED)
    Machine learning models often generalize poorly to out-of-distribution (OOD) data as a result of relying on features that are spuriously correlated with the label during training. Recently, the technique of Invariant Risk Minimization (IRM) was proposed to learn predictors that only use invariant features by conserving the feature-conditioned label expectation $\mathbb{E}_e[y|f(x)]$ across environments. However, more recent studies have demonstrated that IRM-v1, a practical version of IRM, can fail in various settings. Here, we identify a fundamental flaw of IRM formulation that causes the failure. We then introduce a complementary notion of invariance, MRI, based on conserving the label-conditioned feature expectation $\mathbb{E}_e[f(x)|y]$, which is free of this flaw. Further, we introduce a simplified, practical version of the MRI formulation called MRI-v1. We prove that for general linear problems, MRI-v1 guarantees invariant predictors given sufficient number of environments. We also empirically demonstrate that MRI-v1 strongly out-performs IRM-v1 and consistently achieves near-optimal OOD generalization in image-based nonlinear problems.
    Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization. (arXiv:2208.07798v3 [cs.LG] UPDATED)
    Learning invariant (causal) features for out-of-distribution (OOD) generalization has attracted extensive attention recently, and among the proposals invariant risk minimization (IRM) is a notable solution. In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems remain. By introducing the information bottleneck (IB) principle into the learning of IRM, IB-IRM approach has demonstrated its power to solve these challenges. In this paper, we further improve IB-IRM from two aspects. First, we show that the key assumption of support overlap of invariant features used in IB-IRM is strong for the guarantee of OOD generalization and it is still possible to achieve the optimal solution without this assumption. Second, we illustrate two failure modes that IB-IRM (and IRM) could fail for learning the invariant features, and to address such failures, we propose a \textit{Counterfactual Supervision-based Information Bottleneck (CSIB)} learning algorithm that provably recovers the invariant features. By requiring counterfactual inference, CSIB works even when accessing data from a single environment. Empirical experiments on several datasets verify our theoretical results.
    Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards. (arXiv:2206.01293v2 [cs.LG] UPDATED)
    Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms. This paper investigates the problem of how an advertiser can learn to optimize the bidding sequence in an online manner \emph{without} knowing the incrementality parameters in advance. We formulate the offline version of this problem as a specially structured episodic Markov Decision Process (MDP) and then, for its online learning counterpart, propose a novel reinforcement learning (RL) algorithm with regret at most $\widetilde{O}(H^2\sqrt{T})$, which depends on the number of rounds $H$ and number of episodes $T$, but does not depend on the number of actions (i.e., possible bids). A fundamental difference between our learning problem from standard RL problems is that the realized reward feedback from conversion incrementality is \emph{mixed} and \emph{delayed}. To handle this difficulty we propose and analyze a novel pairwise moment-matching algorithm to learn the conversion incrementality, which we believe is of independent of interest.
    SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments. (arXiv:2206.08851v2 [cs.LG] UPDATED)
    Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds. Modernized factories have advanced process control algorithms such as model predictive control (MPC). However, there is little exploration of applying deep reinforcement learning to control manufacturing plants. One of the reasons is the lack of high fidelity simulations and standard APIs for benchmarking. To bridge this gap, we develop an easy-to-use library that includes five high-fidelity simulation environments: BeerFMTEnv, ReactorEnv, AtropineEnv, PenSimEnv and mAbEnv, which cover a wide range of manufacturing processes. We build these environments on published dynamics models. Furthermore, we benchmark online and offline, model-based and model-free reinforcement learning algorithms for comparisons of follow-up research.
    Recipe for a General, Powerful, Scalable Graph Transformer. (arXiv:2205.12454v4 [cs.LG] UPDATED)
    We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being $\textit{local}$, $\textit{global}$ or $\textit{relative}$. The prior GTs are constrained to small graphs with a few hundred nodes, here we propose the first architecture with a complexity linear in the number of nodes and edges $O(N+E)$ by decoupling the local real-edge aggregation from the fully-connected Transformer. We argue that this decoupling does not negatively affect the expressivity, with our architecture being a universal function approximator on graphs. Our GPS recipe consists of choosing 3 main ingredients: (i) positional/structural encoding, (ii) local message-passing mechanism, and (iii) global attention mechanism. We provide a modular framework $\textit{GraphGPS}$ that supports multiple types of encodings and that provides efficiency and scalability both in small and large graphs. We test our architecture on 16 benchmarks and show highly competitive results in all of them, show-casing the empirical benefits gained by the modularity and the combination of different strategies.
    Investigation of a Machine learning methodology for the SKA pulsar search pipeline. (arXiv:2209.04430v3 [astro-ph.IM] UPDATED)
    The SKA pulsar search pipeline will be used for real time detection of pulsars. Modern radio telescopes such as SKA will be generating petabytes of data in their full scale of operation. Hence experience-based and data-driven algorithms become indispensable for applications such as candidate detection. Here we describe our findings from testing a state of the art object detection algorithm called Mask R-CNN to detect candidate signatures in the SKA pulsar search pipeline. We have trained the Mask R-CNN model to detect candidate images. A custom annotation tool was developed to mark the regions of interest in large datasets efficiently. We have successfully demonstrated this algorithm by detecting candidate signatures on a simulation dataset. The paper presents details of this work with a highlight on the future prospects.
    Tighter Regret Analysis and Optimization of Online Federated Learning. (arXiv:2205.06491v3 [cs.LG] UPDATED)
    In federated learning (FL), it is commonly assumed that all data are placed at clients in the beginning of machine learning (ML) optimization (i.e., offline learning). However, in many real-world applications, it is expected to proceed in an online fashion. To this end, online FL (OFL) has been introduced, which aims at learning a sequence of global models from decentralized streaming data such that the so-called cumulative regret is minimized. Combining online gradient descent and model averaging, in this framework, FedOGD is constructed as the counterpart of FedSGD in FL. While it can enjoy an optimal sublinear regret, FedOGD suffers from heavy communication costs. In this paper, we present a communication-efficient method (named OFedIQ) by means of intermittent transmission (enabled by client subsampling and periodic transmission) and quantization. For the first time, we derive the regret bound that captures the impact of data-heterogeneity and the communication-efficient techniques. Through this, we efficiently optimize the parameters of OFedIQ such as sampling rate, transmission period, and quantization levels. Also, it is proved that the optimized OFedIQ can asymptotically achieve the performance of FedOGD while reducing the communication costs by 99%. Via experiments with real datasets, we demonstrate the effectiveness of the optimized OFedIQ.
    Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables. (arXiv:2206.00706v2 [stat.ML] UPDATED)
    We present a new concentration of measure inequality for sums of independent bounded random variables, which we name a split-kl inequality. The inequality is particularly well-suited for ternary random variables, which naturally show up in a variety of problems, including analysis of excess losses in classification, analysis of weighted majority votes, and learning with abstention. We demonstrate that for ternary random variables the inequality is simultaneously competitive with the kl inequality, the Empirical Bernstein inequality, and the Unexpected Bernstein inequality, and in certain regimes outperforms all of them. It resolves an open question by Tolstikhin and Seldin [2013] and Mhammedi et al. [2019] on how to match simultaneously the combinatorial power of the kl inequality when the distribution happens to be close to binary and the power of Bernstein inequalities to exploit low variance when the probability mass is concentrated on the middle value. We also derive a PAC-Bayes-split-kl inequality and compare it with the PAC-Bayes-kl, PAC-Bayes-Empirical-Bennett, and PAC-Bayes-Unexpected-Bernstein inequalities in an analysis of excess losses and in an analysis of a weighted majority vote for several UCI datasets. Last but not least, our study provides the first direct comparison of the Empirical Bernstein and Unexpected Bernstein inequalities and their PAC-Bayes extensions.
    Bugs in Machine Learning-based Systems: A Faultload Benchmark. (arXiv:2206.12311v2 [cs.SE] UPDATED)
    The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and tools aiming at improving the quality of ML components and integrating them into the ML-based system safely. Although most of these tools use bugs' lifecycle, there is no standard benchmark of bugs to assess their performance, compare them and discuss their advantages and weaknesses. In this study, we firstly investigate the reproducibility and verifiability of the bugs in ML-based systems and show the most important factors in each one. Then, we explore the challenges of generating a benchmark of bugs in ML-based software systems and provide a bug benchmark namely defect4ML that satisfies all criteria of standard benchmark, i.e. relevance, reproducibility, fairness, verifiability, and usability. This faultload benchmark contains 100 bugs reported by ML developers in GitHub and Stack Overflow, using two of the most popular ML frameworks: TensorFlow and Keras. defect4ML also addresses important challenges in Software Reliability Engineering of ML-based software systems, like: 1) fast changes in frameworks, by providing various bugs for different versions of frameworks, 2) code portability, by delivering similar bugs in different ML frameworks, 3) bug reproducibility, by providing fully reproducible bugs with complete information about required dependencies and data, and 4) lack of detailed information on bugs, by presenting links to the bugs' origins. defect4ML can be of interest to ML-based systems practitioners and researchers to assess their testing tools and techniques.
    Minimax Optimal Online Imitation Learning via Replay Estimation. (arXiv:2205.15397v5 [cs.LG] UPDATED)
    Online imitation learning is the problem of how best to mimic expert demonstrations, given access to the environment or an accurate simulator. Prior work has shown that in the infinite sample regime, exact moment matching achieves value equivalence to the expert policy. However, in the finite sample regime, even if one has no optimization error, empirical variance can lead to a performance gap that scales with $H^2 / N$ for behavioral cloning and $H / \sqrt{N}$ for online moment matching, where $H$ is the horizon and $N$ is the size of the expert dataset. We introduce the technique of replay estimation to reduce this empirical variance: by repeatedly executing cached expert actions in a stochastic simulator, we compute a smoother expert visitation distribution estimate to match. In the presence of general function approximation, we prove a meta theorem reducing the performance gap of our approach to the parameter estimation error for offline classification (i.e. learning the expert policy). In the tabular setting or with linear function approximation, our meta theorem shows that the performance gap incurred by our approach achieves the optimal $\widetilde{O} \left( \min({H^{3/2}} / {N}, {H} / {\sqrt{N}} \right)$ dependency, under significantly weaker assumptions compared to prior work. We implement multiple instantiations of our approach on several continuous control tasks and find that we are able to significantly improve policy performance across a variety of dataset sizes.
    The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning. (arXiv:2205.06226v3 [cs.LG] UPDATED)
    Recently the surprising discovery of the Bootstrap Your Own Latent (BYOL) method by Grill et al. shows the negative term in contrastive loss can be removed if we add the so-called prediction head to the network. This initiated the research of non-contrastive self-supervised learning. It is mysterious why even when there exist trivial collapsed global optimal solutions, neural networks trained by (stochastic) gradient descent can still learn competitive representations. This phenomenon is a typical example of implicit bias in deep learning and remains little understood. In this work, we present our empirical and theoretical discoveries on non-contrastive self-supervised learning. Empirically, we find that when the prediction head is initialized as an identity matrix with only its off-diagonal entries being trainable, the network can learn competitive representations even though the trivial optima still exist in the training objective. Theoretically, we present a framework to understand the behavior of the trainable, but identity-initialized prediction head. Under a simple setting, we characterized the substitution effect and acceleration effect of the prediction head. The substitution effect happens when learning the stronger features in some neurons can substitute for learning these features in other neurons through updating the prediction head. And the acceleration effect happens when the substituted features can accelerate the learning of other weaker features to prevent them from being ignored. These two effects enable the neural networks to learn all the features rather than focus only on learning the stronger features, which is likely the cause of the dimensional collapse phenomenon. To the best of our knowledge, this is also the first end-to-end optimization guarantee for non-contrastive methods using nonlinear neural networks with a trainable prediction head and normalization.
    TaSIL: Taylor Series Imitation Learning. (arXiv:2205.14812v2 [cs.LG] UPDATED)
    We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control. TaSIL penalizes deviations in the higher-order Taylor series terms between the learned and expert policies. We show that experts satisfying a notion of $\textit{incremental input-to-state stability}$ are easy to learn, in the sense that a small TaSIL-augmented imitation loss over expert trajectories guarantees a small imitation loss over trajectories generated by the learned policy. We provide sample-complexity bounds for TaSIL that scale as $\tilde{\mathcal{O}}(1/n)$ in the realizable setting, for $n$ the number of expert demonstrations. Finally, we demonstrate experimentally the relationship between the robustness of the expert policy and the order of Taylor expansion required in TaSIL, and compare standard Behavior Cloning, DART, and DAgger with TaSIL-loss-augmented variants. In all cases, we show significant improvement over baselines across a variety of MuJoCo tasks.
    Bayesian Active Learning with Fully Bayesian Gaussian Processes. (arXiv:2205.10186v3 [cs.LG] UPDATED)
    The bias-variance trade-off is a well-known problem in machine learning that only gets more pronounced the less available data there is. In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can cause inefficient and non-optimal querying, leading to unnecessary data labeling. In this paper, we focus on active learning with Gaussian Processes (GPs). For the GP, the bias-variance trade-off is made by optimization of the two hyperparameters: the length scale and noise-term. Considering that the optimal mode of the joint posterior of the hyperparameters is equivalent to the optimal bias-variance trade-off, we approximate this joint posterior and utilize it to design two new acquisition functions. The first one is a Bayesian variant of Query-by-Committee (B-QBC), and the second is an extension that explicitly minimizes the predictive variance through a Query by Mixture of Gaussian Processes (QB-MGP) formulation. Across six simulators, we empirically show that B-QBC, on average, achieves the best marginal likelihood, whereas QB-MGP achieves the best predictive performance. We show that incorporating the bias-variance trade-off in the acquisition functions mitigates unnecessary and expensive data labeling.
    Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited. (arXiv:2205.10914v3 [cs.LG] UPDATED)
    Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism. We give a unified view on both classes of graph kernels. We study walk-based node refinement methods and formally relate them to several widely-used techniques, including Morgan's algorithm for molecule canonization and the Weisfeiler-Leman test. We define corresponding walk-based kernels on nodes that allow fine-grained parameterized neighborhood comparison, reach Weisfeiler-Leman expressiveness, and are computed using the kernel trick. From this we show that classical random walk kernels with only minor modifications regarding definition and computation are as expressive as the widely-used Weisfeiler-Leman subtree kernel but support non-strict neighborhood comparison. We verify experimentally that walk-based kernels reach or even surpass the accuracy of Weisfeiler-Leman kernels in real-world classification tasks.
    Joint Entropy Search for Maximally-Informed Bayesian Optimization. (arXiv:2206.04771v5 [cs.LG] UPDATED)
    Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Search and Predictive Entropy Search both consider the entropy over the optimum in the input space, while the recent Max-value Entropy Search considers the entropy over the optimal value in the output space. We propose Joint Entropy Search (JES), a novel information-theoretic acquisition function that considers an entirely new quantity, namely the entropy over the joint optimal probability density over both input and output space. To incorporate this information, we consider the reduction in entropy from conditioning on fantasized optimal input/output pairs. The resulting approach primarily relies on standard GP machinery and removes complex approximations typically associated with information-theoretic methods. With minimal computational overhead, JES shows superior decision-making, and yields state-of-the-art performance for information-theoretic approaches across a wide suite of tasks. As a light-weight approach with superior results, JES provides a new go-to acquisition function for Bayesian optimization.
    Neural Network Architecture Beyond Width and Depth. (arXiv:2205.09459v4 [cs.LG] UPDATED)
    This paper proposes a new neural network architecture by introducing an additional dimension called height beyond width and depth. Neural network architectures with height, width, and depth as hyper-parameters are called three-dimensional architectures. It is shown that neural networks with three-dimensional architectures are significantly more expressive than the ones with two-dimensional architectures (those with only width and depth as hyper-parameters), e.g., standard fully connected networks. The new network architecture is constructed recursively via a nested structure, and hence we call a network with the new architecture nested network (NestNet). A NestNet of height $s$ is built with each hidden neuron activated by a NestNet of height $\le s-1$. When $s=1$, a NestNet degenerates to a standard network with a two-dimensional architecture. It is proved by construction that height-$s$ ReLU NestNets with $\mathcal{O}(n)$ parameters can approximate $1$-Lipschitz continuous functions on $[0,1]^d$ with an error $\mathcal{O}(n^{-(s+1)/d})$, while the optimal approximation error of standard ReLU networks with $\mathcal{O}(n)$ parameters is $\mathcal{O}(n^{-2/d})$. Furthermore, such a result is extended to generic continuous functions on $[0,1]^d$ with the approximation error characterized by the modulus of continuity. Finally, we use numerical experimentation to show the advantages of the super-approximation power of ReLU NestNets.
    TransBoost: Improving the Best ImageNet Performance using Deep Transduction. (arXiv:2205.13331v4 [cs.CV] UPDATED)
    This paper deals with deep transductive learning, and proposes TransBoost as a procedure for fine-tuning any deep neural model to improve its performance on any (unlabeled) test set provided at training time. TransBoost is inspired by a large margin principle and is efficient and simple to use. Our method significantly improves the ImageNet classification performance on a wide range of architectures, such as ResNets, MobileNetV3-L, EfficientNetB0, ViT-S, and ConvNext-T, leading to state-of-the-art transductive performance. Additionally we show that TransBoost is effective on a wide variety of image classification datasets. The implementation of TransBoost is provided at: https://github.com/omerb01/TransBoost .
    Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?. (arXiv:2206.05266v4 [cs.LG] UPDATED)
    We investigate whether self-supervised learning (SSL) can improve online reinforcement learning (RL) from pixels. We extend the contrastive reinforcement learning framework (e.g., CURL) that jointly optimizes SSL and RL losses and conduct an extensive amount of experiments with various self-supervised losses. Our observations suggest that the existing SSL framework for RL fails to bring meaningful improvement over the baselines only taking advantage of image augmentation when the same amount of data and augmentation is used. We further perform evolutionary searches to find the optimal combination of multiple self-supervised losses for RL, but find that even such a loss combination fails to meaningfully outperform the methods that only utilize carefully designed image augmentations. After evaluating these approaches together in multiple different environments including a real-world robot environment, we confirm that no single self-supervised loss or image augmentation method can dominate all environments and that the current framework for joint optimization of SSL and RL is limited. Finally, we conduct the ablation study on multiple factors and demonstrate the properties of representations learned with different approaches.
    Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. (arXiv:2203.09675v3 [stat.ML] UPDATED)
    Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be run on the full posterior can instead be run inexpensively on the coreset, with results that approximate those on the full data. However, current approaches are limited by either a significant run-time or the need for the user to specify a low-cost approximation to the full posterior. We propose a Bayesian coreset construction algorithm that first selects a uniformly random subset of data, and then optimizes the weights using a novel quasi-Newton method. Our algorithm is a simple to implement, black-box method, that does not require the user to specify a low-cost posterior approximation. It is the first to come with a general high-probability bound on the KL divergence of the output coreset posterior. Experiments demonstrate that our method provides significant improvements in coreset quality against alternatives with comparable construction times, with far less storage cost and user input required.
    Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language. (arXiv:2204.10429v2 [cs.NI] UPDATED)
    Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the transmission and inference costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution cost and time, reliability, and belief efficiency.
    Learning Neural Acoustic Fields. (arXiv:2204.00628v2 [cs.SD] UPDATED)
    Our environment is filled with rich and dynamic acoustic information. When we walk into a cathedral, the reverberations as much as appearance inform us of the sanctuary's wide open space. Similarly, as an object moves around us, we expect the sound emitted to also exhibit this movement. While recent advances in learned implicit functions have led to increasingly higher quality representations of the visual world, there have not been commensurate advances in learning spatial auditory representations. To address this gap, we introduce Neural Acoustic Fields (NAFs), an implicit representation that captures how sounds propagate in a physical scene. By modeling acoustic propagation in a scene as a linear time-invariant system, NAFs learn to continuously map all emitter and listener location pairs to a neural impulse response function that can then be applied to arbitrary sounds. We demonstrate that the continuous nature of NAFs enables us to render spatial acoustics for a listener at an arbitrary location, and can predict sound propagation at novel locations. We further show that the representation learned by NAFs can help improve visual learning with sparse views. Finally, we show that a representation informative of scene structure emerges during the learning of NAFs.
    Multi-sensor large-scale dataset for multi-view 3D reconstruction. (arXiv:2203.06111v2 [cs.CV] UPDATED)
    We present a new multi-sensor dataset for multi-view 3D surface reconstruction. It includes registered RGB and depth data from sensors of different resolutions and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial cameras, and structured-light scanner. The data for each scene is obtained under a large number of lighting conditions, and the scenes are selected to emphasize a diverse set of material properties challenging for existing algorithms. Overall, we provide around 1.4 million images of 107 different scenes acquired at 14 lighting conditions from 100 viewing directions. We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms of different types and for other related tasks.
    Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments. (arXiv:2204.03140v2 [cs.RO] UPDATED)
    Autonomous exploration has many important applications. However, classic information gain-based or frontier-based exploration only relies on the robot current state to determine the immediate exploration goal, which lacks the capability of predicting the value of future states and thus leads to inefficient exploration decisions. This paper presents a method to learn how "good" states are, measured by the state value function, to provide a guidance for robot exploration in real-world challenging environments. We formulate our work as a off-policy evaluation (OPE) problem for robot exploration (OPERE). It consists of offline Monte-Carlo training on real-world data and performs Temporal Difference (TD) online adaptation to optimize the trained value estimator. We also design an intrinsic reward function based on sensor information coverage to enable the robot to gain more information with sparse extrinsic rewards. Results demonstrate that our method enables the robot to predict the value of future states so as to better guide robot exploration. The proposed algorithm achieves better prediction performance compared with other state-of-the-art OPE methods. To the best of our knowledge, this work for the first time demonstrates value function prediction on real-world dataset for robot exploration in challenging subterranean and urban environments. More details and demo videos can be found at https://jeffreyyh.github.io/opere/.
    coVariance Neural Networks. (arXiv:2205.15856v4 [cs.LG] UPDATED)
    Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the projection of data on the eigenspace of the covariance matrix and draws similarities with the graph convolutional filters in GNNs. Motivated by this observation, we study a GNN architecture, called coVariance neural network (VNN), that operates on sample covariance matrices as graphs. We theoretically establish the stability of VNNs to perturbations in the covariance matrix, thus, implying an advantage over standard PCA-based data analysis approaches that are prone to instability due to principal components associated with close eigenvalues. Our experiments on real-world datasets validate our theoretical results and show that VNN performance is indeed more stable than PCA-based statistical approaches. Moreover, our experiments on multi-resolution datasets also demonstrate that VNNs are amenable to transferability of performance over covariance matrices of different dimensions; a feature that is infeasible for PCA-based approaches.
    Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks. (arXiv:2110.13330v2 [cs.LG] UPDATED)
    Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function. This paper shows that a PINN can be sensitive to errors in training data and overfit itself in dynamically propagating these errors over the domain of the solution of the PDE. It also shows how physical regularizations based on continuity criteria and conservation laws fail to address this issue and rather introduce problems of their own causing the deep network to converge to a physics-obeying local minimum instead of the global minimum. We introduce Gaussian Process (GP) based smoothing that recovers the performance of a PINN and promises a robust architecture against noise/errors in measurements. Additionally, we illustrate an inexpensive method of quantifying the evolution of uncertainty based on the variance estimation of GPs on boundary data. Robust PINN performance is also shown to be achievable by choice of sparse sets of inducing points based on sparsely induced GPs. We demonstrate the performance of our proposed methods and compare the results from existing benchmark models in literature for time-dependent Schr\"odinger and Burgers' equations.
    Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environments. (arXiv:2205.00446v2 [math.OC] UPDATED)
    In the past few years, Online Convex Optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this paper, we propose new step-size rules and OCO algorithms that simultaneously exploit gradient predictions, function predictions and dynamics, features particularly pertinent to control applications. The proposed algorithms enjoy static and dynamic regret bounds in terms of the dynamics of the reference action sequence, gradient prediction error, and function prediction error, which are generalizations of known regularity measures from the literature. We present results for both convex and strongly convex costs. We validate the performance of the proposed algorithms in a trajectory tracking case study, as well as portfolio optimization using real-world datasets.
    Toward Explainable AI for Regression Models. (arXiv:2112.11407v2 [cs.LG] UPDATED)
    In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks. Gaining a better understanding is especially important e.g. for safety-critical ML applications or medical diagnostics etc. While such Explainable AI (XAI) techniques have reached significant popularity for classifiers, so far little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI for regression and classification tasks, establish novel theoretical insights and analysis for XAIR, provide demonstrations of XAIR on genuine practical regression problems, and finally discuss the challenges remaining for the field.
    Generalization Error Bounds for Multiclass Sparse Linear Classifiers. (arXiv:2204.06264v2 [math.ST] UPDATED)
    We consider high-dimensional multiclass classification by sparse multinomial logistic regression. Unlike binary classification, in the multiclass setup one can think about an entire spectrum of possible notions of sparsity associated with different structural assumptions on the regression coefficients matrix. We propose a computationally feasible feature selection procedure based on penalized maximum likelihood with convex penalties capturing a specific type of sparsity at hand. In particular, we consider global sparsity, double row-wise sparsity, and low-rank sparsity, and show that with the properly chosen tuning parameters the derived plug-in classifiers attain the minimax generalization error bounds (in terms of misclassification excess risk) within the corresponding classes of multiclass sparse linear classifiers. The developed approach is general and can be adapted to other types of sparsity as well.
    Smoothed Online Combinatorial Optimization Using Imperfect Predictions. (arXiv:2204.10979v2 [cs.LG] UPDATED)
    Smoothed online combinatorial optimization considers a learner who repeatedly chooses a combinatorial decision to minimize an unknown changing cost function with a penalty on switching decisions in consecutive rounds. We study smoothed online combinatorial optimization problems when an imperfect predictive model is available, where the model can forecast the future cost functions with uncertainty. We show that using predictions to plan for a finite time horizon leads to regret dependent on the total predictive uncertainty and an additional switching cost. This observation suggests choosing a suitable planning window to balance between uncertainty and switching cost, which leads to an online algorithm with guarantees on the upper and lower bounds of the cumulative regret. Empirically, our algorithm shows a significant improvement in cumulative regret compared to other baselines in synthetic online distributed streaming problems.
    3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction. (arXiv:2205.14575v2 [cs.CV] UPDATED)
    Recently, the transformer model has been successfully employed for the multi-view 3D reconstruction problem. However, challenges remain on designing an attention mechanism to explore the multiview features and exploit their relations for reinforcing the encoding-decoding modules. This paper proposes a new model, namely 3D coarse-to-fine transformer (3D-C2FT), by introducing a novel coarse-to-fine(C2F) attention mechanism for encoding multi-view features and rectifying defective 3D objects. C2F attention mechanism enables the model to learn multi-view information flow and synthesize 3D surface correction in a coarse to fine-grained manner. The proposed model is evaluated by ShapeNet and Multi-view Real-life datasets. Experimental results show that 3D-C2FT achieves notable results and outperforms several competing models on these datasets.
    Compositional Visual Generation with Composable Diffusion Models. (arXiv:2206.01714v6 [cs.CV] UPDATED)
    Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible, they struggle to understand the composition of certain concepts, such as confusing the attributes of different objects or relations between objects. In this paper, we propose an alternative structured approach for compositional generation using diffusion models. An image is generated by composing a set of diffusion models, with each of them modeling a certain component of the image. To do this, we interpret diffusion models as energy-based models in which the data distributions defined by the energy functions may be explicitly combined. The proposed method can generate scenes at test time that are substantially more complex than those seen in training, composing sentence descriptions, object relations, human facial attributes, and even generalizing to new combinations that are rarely seen in the real world. We further illustrate how our approach may be used to compose pre-trained text-guided diffusion models and generate photorealistic images containing all the details described in the input descriptions, including the binding of certain object attributes that have been shown difficult for DALLE-2. These results point to the effectiveness of the proposed method in promoting structured generalization for visual generation. Project page: https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
    Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health. (arXiv:2202.00916v3 [cs.LG] UPDATED)
    This paper studies restless multi-armed bandit (RMAB) problems with unknown arm transition dynamics but with known correlated arm features. The goal is to learn a model to predict transition dynamics given features, where the Whittle index policy solves the RMAB problems using predicted transitions. However, prior works often learn the model by maximizing the predictive accuracy instead of final RMAB solution quality, causing a mismatch between training and evaluation objectives. To address this shortcoming, we propose a novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality. We present three key contributions: (i) we establish differentiability of the Whittle index policy to support decision-focused learning; (ii) we significantly improve the scalability of decision-focused learning approaches in sequential problems, specifically RMAB problems; (iii) we apply our algorithm to a previously collected dataset of maternal and child health to demonstrate its performance. Indeed, our algorithm is the first for decision-focused learning in RMAB that scales to real-world problem sizes.
    Handling Bias in Toxic Speech Detection: A Survey. (arXiv:2202.00126v3 [cs.SI] UPDATED)
    Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors such as the context, geography, socio-political climate, and background of the producers and consumers of the posts play a crucial role in determining if the content can be flagged as toxic. Adoption of automated toxicity detection models in production can thus lead to a sidelining of the various groups they aim to help in the first place. It has piqued researchers' interest in examining unintended biases and their mitigation. Due to the nascent and multi-faceted nature of the work, complete literature is chaotic in its terminologies, techniques, and findings. In this paper, we put together a systematic study of the limitations and challenges of existing methods for mitigating bias in toxicity detection. We look closely at proposed methods for evaluating and mitigating bias in toxic speech detection. To examine the limitations of existing methods, we also conduct a case study to introduce the concept of bias shift due to knowledge-based bias mitigation. The survey concludes with an overview of the critical challenges, research gaps, and future directions. While reducing toxicity on online platforms continues to be an active area of research, a systematic study of various biases and their mitigation strategies will help the research community produce robust and fair models.
    A Ranking Game for Imitation Learning. (arXiv:2202.03481v3 [cs.LG] UPDATED)
    We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward. In this game, the reward agent learns to satisfy pairwise performance rankings between behaviors, while the policy agent learns to maximize this reward. In imitation learning, near-optimal expert data can be difficult to obtain, and even in the limit of infinite data cannot imply a total ordering over trajectories as preferences can. On the other hand, learning from preferences alone is challenging as a large number of preferences are required to infer a high-dimensional reward function, though preference data is typically much easier to collect than expert demonstrations. The classical inverse reinforcement learning (IRL) formulation learns from expert demonstrations but provides no mechanism to incorporate learning from offline preferences and vice versa. We instantiate the proposed ranking-game framework with a novel ranking loss giving an algorithm that can simultaneously learn from expert demonstrations and preferences, gaining the advantages of both modalities. Our experiments show that the proposed method achieves state-of-the-art sample efficiency and can solve previously unsolvable tasks in the Learning from Observation (LfO) setting. Project video and code can be found at https://hari-sikchi.github.io/rank-game/
    Dynamic Combination of Heterogeneous Models for Hierarchical Time Series. (arXiv:2112.11669v2 [cs.LG] UPDATED)
    We introduce a framework to dynamically combine heterogeneous models called \texttt{DYCHEM}, which forecasts a set of time series that are related through an aggregation hierarchy. Different types of forecasting models can be employed as individual ``experts'' so that each model is tailored to the nature of the corresponding time series. \texttt{DYCHEM} learns hierarchical structures during the training stage to help generalize better across all the time series being modeled and also mitigates coherency issues that arise due to constraints imposed by the hierarchy. To improve the reliability of forecasts, we construct quantile estimations based on the point forecasts obtained from combined heterogeneous models. The resulting quantile forecasts are coherent and independent of the choice of forecasting models. We conduct a comprehensive evaluation of both point and quantile forecasts for hierarchical time series (HTS), including public data and user records from a large financial software company. In general, our method is robust, adaptive to datasets with different properties, and highly configurable and efficient for large-scale forecasting pipelines.
    $A^{3}D$: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks. (arXiv:2203.03128v2 [cs.LG] UPDATED)
    The robustness of deep neural networks (DNN) models has attracted increasing attention due to the urgent need for security in many applications. Numerous existing open-sourced tools or platforms are developed to evaluate the robustness of DNN models by ensembling the majority of adversarial attack or defense algorithms. Unfortunately, current platforms do not possess the ability to optimize the architectures of DNN models or the configuration of adversarial attacks to further enhance the robustness of models or the performance of adversarial attacks. To alleviate these problems, in this paper, we first propose a novel platform called auto adversarial attack and defense ($A^{3}D$), which can help search for robust neural network architectures and efficient adversarial attacks. In $A^{3}D$, we employ multiple neural architecture search methods, which consider different robustness evaluation metrics, including four types of noises: adversarial noise, natural noise, system noise, and quantified metrics, resulting in finding robust architectures. Besides, we propose a mathematical model for auto adversarial attack, and provide multiple optimization algorithms to search for efficient adversarial attacks. In addition, we combine auto adversarial attack and defense together to form a unified framework. Among auto adversarial defense, the searched efficient attack can be used as the new robustness evaluation to further enhance the robustness. In auto adversarial attack, the searched robust architectures can be utilized as the threat model to help find stronger adversarial attacks. Experiments on CIFAR10, CIFAR100, and ImageNet datasets demonstrate the feasibility and effectiveness of the proposed platform, which can also provide a benchmark and toolkit for researchers in the application of automated machine learning in evaluating and improving the DNN model robustnesses.
    One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer. (arXiv:2110.10325v8 [cs.LG] UPDATED)
    Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced artificial intelligence technologies. One-step abductive multi-target learning (OSAMTL), an approach inspired by abductive learning, via simply combining machine learning and logical reasoning in a one-step balanced way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions.
    Gap Minimization for Knowledge Sharing and Transfer. (arXiv:2201.11231v2 [cs.LG] UPDATED)
    Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of \emph{performance gap}, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g., $\mathcal{H}$-divergence or discrepancy distance), we theoretically show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees. More importantly, it also provides new insights and motivates a novel principle for designing strategies for knowledge sharing and transfer: gap minimization. We instantiate this principle with two algorithms: 1. gapBoost, a novel and principled boosting algorithm that explicitly minimizes the performance gap between source and target domains for transfer learning; and 2. gapMTNN, a representation learning algorithm that reformulates gap minimization as semantic conditional matching for multitask learning. Our extensive evaluation on both transfer learning and multitask learning benchmark data sets shows that our methods outperform existing baselines.
    Forecasting Market Changes using Variational Inference. (arXiv:2205.00605v2 [q-fin.ST] UPDATED)
    Though various approaches have been considered, forecasting near-term market changes of equities and similar market data remains quite difficult. In this paper we introduce an approach to forecast near-term market changes for equity indices as well as portfolios using variational inference (VI). VI is a machine learning approach which uses optimization techniques to estimate complex probability densities. In the proposed approach, clusters of explanatory variables are identified and market changes are forecast based on cluster-specific linear regression. Apart from the expected value of changes, the proposed approach can also be used to obtain the distribution of possible outcomes. Another advantage of the proposed approach is the clear model interpretation, as clusters of explanatory variables (or market regimes) are identified for which the future changes follow similar relationships. Knowledge about such clusters can provide useful insights about portfolio performance and identify the relative importance of variables in different market regimes. An illustrative example of predicting one-day S\&P change is considered and it is shown that even with as few as three explanatory variables, the proposed approach provides useful predictions.
    Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulations. (arXiv:2111.10189v3 [cond-mat.stat-mech] UPDATED)
    We provide a deepened study of autocorrelations in Neural Markov Chain Monte Carlo (NMCMC) simulations, a version of the traditional Metropolis algorithm which employs neural networks to provide independent proposals. We illustrate our ideas using the two-dimensional Ising model. We discuss several estimates of autocorrelation times in the context of NMCMC, some inspired by analytical results derived for the Metropolized Independent Sampler (MIS). We check their reliability by estimating them on a small system where analytical results can also be obtained. Based on the analytical results for MIS we propose a new loss function and study its impact on the autocorelation times. Although, this function's performance is a bit inferior to the traditional Kullback-Leibler divergence, it offers two training algorithms which in some situations may be beneficial. By studying a small, $4 \times 4$, system we gain access to the dynamics of the training process which we visualize using several observables. Furthermore, we quantitatively investigate the impact of imposing global discrete symmetries of the system in the neural network training process on the autocorrelation times. Eventually, we propose a scheme which incorporates partial heat-bath updates which considerably improves the quality of the training. The impact of the above enhancements is discussed for a $16 \times 16$ spin system. The summary of our findings may serve as a guidance to the implementation of Neural Markov Chain Monte Carlo simulations for more complicated models.
    Federated Learning with Heterogeneous Differential Privacy. (arXiv:2110.15252v2 [cs.LG] UPDATED)
    Federated learning (FL) takes a first step towards privacy-preserving machine learning by training models while keeping client data local. Models trained using FL may still leak private client information through model updates during training. Differential privacy (DP) may be employed on model updates to provide privacy guarantees within FL, typically at the cost of degraded performance of the final trained model. Both non-private FL and DP-FL can be solved using variants of the federated averaging (FedAvg) algorithm. In this work, we consider a heterogeneous DP setup where clients require varying degrees of privacy guarantees. First, we analyze the optimal solution to the federated linear regression problem with heterogeneous DP in a Bayesian setup. We find that unlike the non-private setup, where the optimal solution for homogeneous data amounts to a single global solution for all clients learned through FedAvg, the optimal solution for each client in this setup would be a personalized one even for homogeneous data. We also analyze the privacy-utility trade-off for this setup, where we characterize the gain obtained from heterogeneous privacy where some clients opt for less strict privacy guarantees. We propose a new algorithm for FL with heterogeneous DP, named FedHDP, which employs personalization and weighted averaging at the server using the privacy choices of clients, to achieve better performance on clients' local models. Through numerical experiments, we show that FedHDP provides up to $9.27\%$ performance gain compared to the baseline DP-FL for the considered datasets where $5\%$ of clients opt out of DP. Additionally, we show a gap in the average performance of local models between non-private and private clients of up to $3.49\%$, empirically illustrating that the baseline DP-FL might incur a large utility cost when not all clients require the stricter privacy guarantees.
    MANDERA: Malicious Node Detection in Federated Learning via Ranking. (arXiv:2110.11736v2 [cs.LG] UPDATED)
    Byzantine attacks hinder the deployment of federated learning algorithms. Although we know that the benign gradients and Byzantine attacked gradients are distributed differently, to detect the malicious gradients is challenging due to (1) the gradient is high-dimensional and each dimension has its unique distribution and (2) the benign gradients and the attacked gradients are always mixed (two-sample test methods cannot apply directly). To address the above, for the first time, we propose MANDERA which is theoretically guaranteed to efficiently detect all malicious gradients under Byzantine attacks with no prior knowledge or history about the number of attacked nodes. More specifically, we transfer the original updating gradient space into a ranking matrix. By such an operation, the scales of different dimensions of the gradients in the ranking space become identical. The high-dimensional benign gradients and the malicious gradients can be easily separated. The effectiveness of MANDERA is further confirmed by experimentation on four Byzantine attack implementations (Gaussian, Zero Gradient, Sign Flipping, Shifted Mean), comparing with state-of-the-art defenses. The experiments cover both IID and Non-IID datasets.
    A Deep Reinforcement Learning Approach for Online Parcel Assignment. (arXiv:2109.03467v2 [cs.LG] UPDATED)
    In this paper, we investigate the online parcel assignment (OPA) problem, in which each stochastically generated parcel needs to be assigned to a candidate route for delivery to minimize the total cost subject to certain business constraints. The OPA problem is challenging due to its stochastic nature: each parcel's candidate routes, which depends on the parcel's origin, destination, weight, etc., are unknown until its order is placed, and the total parcel volume is uncertain in advance. To tackle this challenge, we propose the PPO-OPA algorithm based on deep reinforcement learning that shows competitive performance. More specifically, we introduce a novel Markov Decision Process (MDP) framework to model the OPA problem, and develop a policy gradient algorithm that adopts attention networks for policy evaluation. By designing a dedicated reward function, our proposed algorithm can achieve a lower total cost with smaller violation of constraints, comparing to the traditional method which assigns parcels to candidate routes proportionally. In addition, the performances of our proposed algorithm and the Primal-Dual algorithm are comparable, while the later assumes a known total parcel volume in advance, which is unrealistic in practice.
    First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems. (arXiv:2204.03132v2 [math.OC] UPDATED)
    We consider the problem of computing an equilibrium in a class of \textit{nonlinear generalized Nash equilibrium problems (NGNEPs)} in which the strategy sets for each player are defined by the equality and inequality constraints that may depend on the choices of rival players. While the asymptotic global convergence and local convergence rate of certain algorithms have been extensively investigated, the iteration complexity analysis is still in its infancy. This paper provides two first-order algorithms based on quadratic penalty method (QPM) and augmented Lagrangian method (ALM), respectively, with an accelerated mirror-prox algorithm as the solver in each inner loop. We show the nonasymptotic convergence rate for these algorithms. In particular, we establish the global convergence guarantee for solving monotone and strongly monotone NGNEPs and provide the complexity bounds expressed in terms of the number of gradient evaluations. Experimental results demonstrate the efficiency of our algorithms in practice.
    GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy. (arXiv:2104.10569v3 [cs.LG] UPDATED)
    Graph neural networks (GNNs) have been demonstrated as a powerful tool for analyzing non-Euclidean graph data. However, the lack of efficient distributed graph learning systems severely hinders applications of GNNs, especially when graphs are big and GNNs are relatively deep. Herein, we present GraphTheta, the first distributed and scalable graph learning system built upon vertex-centric distributed graph processing with neural network operators implemented as user-defined functions. This system supports multiple training strategies and enables efficient and scalable big-graph learning on distributed (virtual) machines with low memory. To facilitate graph convolutions, GraphTheta puts forward a new graph learning abstraction named NN-TGAR to bridge the gap between graph processing and graph deep learning. A distributed graph engine is proposed to conduct the stochastic gradient descent optimization with a hybrid-parallel execution, and a new cluster-batched training strategy is supported. We evaluate GraphTheta using several datasets with network sizes ranging from small-, modest- to large-scale. Experimental results show that GraphTheta can scale well to 1,024 workers for training an in-house developed GNN on an industry-scale Alipay dataset of 1.4 billion nodes and 4.1 billion attributed edges, with a cluster of CPU virtual machines (dockers) of small memory each (5$\sim$12GB). Moreover, GraphTheta can outperform DistDGL by up to $2.02\times$, with better scalability, and GraphLearn by up to $30.56\times$. As for model accuracy, GraphTheta is capable of learning as good GNNs as existing frameworks. To the best of our knowledge, this work presents the largest edge-attributed GNN learning task in the literature.
    Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. (arXiv:2106.13624v2 [cs.LG] UPDATED)
    We present a new second-order oracle bound for the expected risk of a weighted majority vote. The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a.k.a. one-sided Chebyshev's), which is amenable to efficient minimization. The new form resolves the optimization challenge faced by prior oracle bounds based on the Chebyshev-Cantelli inequality, the C-bounds [Germain et al., 2015], and, at the same time, it improves on the oracle bound based on second order Markov's inequality introduced by Masegosa et al. [2020]. We also derive a new concentration of measure inequality, which we name PAC-Bayes-Bennett, since it combines PAC-Bayesian bounding with Bennett's inequality. We use it for empirical estimation of the oracle bound. The PAC-Bayes-Bennett inequality improves on the PAC-Bayes-Bernstein inequality of Seldin et al. [2012]. We provide an empirical evaluation demonstrating that the new bounds can improve on the work of Masegosa et al. [2020]. Both the parametric form of the Chebyshev-Cantelli inequality and the PAC-Bayes-Bennett inequality may be of independent interest for the study of concentration of measure in other domains.
    The Prominence of Artificial Intelligence in COVID-19. (arXiv:2111.09537v2 [cs.LG] UPDATED)
    In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.
    SITTA: Single Image Texture Translation for Data Augmentation. (arXiv:2106.13804v2 [cs.CV] UPDATED)
    Recent advances in data augmentation enable one to translate images by learning the mapping between a source domain and a target domain. Existing methods tend to learn the distributions by training a model on a variety of datasets, with results evaluated largely in a subjective manner. Relatively few works in this area, however, study the potential use of image synthesis methods for recognition tasks. In this paper, we propose and explore the problem of image translation for data augmentation. We first propose a lightweight yet efficient model for translating texture to augment images based on a single input of source texture, allowing for fast training and testing, referred to as Single Image Texture Translation for data Augmentation (SITTA). Then we explore the use of augmented data in long-tailed and few-shot image classification tasks. We find the proposed augmentation method and workflow is capable of translating the texture of input data into a target domain, leading to consistently improved image recognition performance. Finally, we examine how SITTA and related image translation methods can provide a basis for a data-efficient, "augmentation engineering" approach to model training. Codes are available at https://github.com/Boyiliee/SITTA.
    Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets. (arXiv:2106.13919v2 [cs.LG] UPDATED)
    This paper aims for set-to-hypergraph prediction, where the goal is to infer the set of relations for a given set of entities. This is a common abstraction for applications in particle physics, biological systems, and combinatorial optimization. We address two common scaling problems encountered in set-to-hypergraph tasks that limit the size of the input set: the exponentially growing number of hyperedges and the run-time complexity, both leading to higher memory requirements. We make three contributions. First, we propose to predict and supervise the \emph{positive} edges only, which changes the asymptotic memory scaling from exponential to linear. Second, we introduce a training method that encourages iterative refinement of the predicted hypergraph, which allows us to skip iterations in the backward pass for improved efficiency and constant memory usage. Third, we combine both contributions in a single set-to-hypergraph model that enables us to address problems with larger input set sizes. We provide ablations for our main technical contributions and show that our model outperforms prior state-of-the-art, especially for larger sets.
    Genetic algorithm for feature selection of EEG heterogeneous data. (arXiv:2103.07117v2 [cs.NE] UPDATED)
    The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.
    Relay Variational Inference: A Method for Accelerated Encoderless VI. (arXiv:2110.13422v2 [cs.LG] UPDATED)
    Variational Inference (VI) offers a method for approximating intractable likelihoods. In neural VI, inference of approximate posteriors is commonly done using an encoder. Alternatively, encoderless VI offers a framework for learning generative models from data without encountering suboptimalities caused by amortization via an encoder (e.g. in presence of missing or uncertain data). However, in absence of an encoder, such methods often suffer in convergence due to the slow nature of gradient steps required to learn the approximate posterior parameters. In this paper, we introduce Relay VI (RVI), a framework that dramatically improves both the convergence and performance of encoderless VI. In our experiments over multiple datasets, we study the effectiveness of RVI in terms of convergence speed, loss, representation power and missing data imputation. We find RVI to be a unique tool, often superior in both performance and convergence speed to previously proposed encoderless as well as amortized VI models (e.g. VAE).
    Labels, Information, and Computation: Efficient Learning Using Sufficient Labels. (arXiv:2104.09015v3 [cs.LG] UPDATED)
    In supervised learning, obtaining a large set of fully-labeled training data is expensive. We show that we do not always need full label information on every single training example to train a competent classifier. Specifically, inspired by the principle of sufficiency in statistics, we present a statistic (a summary) of the fully-labeled training set that captures almost all the relevant information for classification but at the same time is easier to obtain directly. We call this statistic "sufficiently-labeled data" and prove its sufficiency and efficiency for finding the optimal hidden representations, on which competent classifier heads can be trained using as few as a single randomly-chosen fully-labeled example per class. Sufficiently-labeled data can be obtained from annotators directly without collecting the fully-labeled data first. And we prove that it is easier to directly obtain sufficiently-labeled data than obtaining fully-labeled data. Furthermore, sufficiently-labeled data is naturally more secure since it stores relative, instead of absolute, information. Extensive experimental results are provided to support our theory.
    Random Planted Forest: a directly interpretable tree ensemble. (arXiv:2012.14563v2 [stat.ML] UPDATED)
    We introduce a novel interpretable, tree based algorithm for prediction in a regression setting in which each tree in a classical random forest is replaced by a family of planted trees that grow simultaneously. The motivation for our algorithm is to estimate the unknown regression function from a functional decomposition perspective, where each tree corresponds to a function within that decomposition. The maximal order of approximation in the decomposition can be specified or left unlimited. If a first order approximation is chosen, the result is an additive model. In the other extreme case, if the order of approximation is not limited, the resulting model places no restrictions on the form of the regression function. In a simulation study we find encouraging prediction and visualisation properties of our random planted forest method. We also develop theory for an idealised version of random planted forests in cases where the maximal order of approximation is low. We show that if the order is smaller than three, the idealised version achieves asymptotically optimal convergence rates up to a logarithmic factor. ode is available on https://github.com/PlantedML/randomPlantedForest
    Post-training Quantization for Neural Networks with Provable Guarantees. (arXiv:2201.11113v3 [cs.LG] UPDATED)
    While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research. By replacing the weights of a neural network with quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation cost, memory, and power consumption are attained. To that end, we generalize a post-training neural-network quantization method, GPFQ, that is based on a greedy path-following mechanism. Among other things, we propose modifications to promote sparsity of the weights, and rigorously analyze the associated error. Additionally, our error analysis expands the results of previous work on GPFQ to handle general quantization alphabets, showing that for quantizing a single-layer network, the relative square error essentially decays linearly in the number of weights -- i.e., level of over-parametrization. Our result holds across a range of input distributions and for both fully-connected and convolutional architectures thereby also extending previous results. To empirically evaluate the method, we quantize several common architectures with few bits per weight, and test them on ImageNet, showing only minor loss of accuracy compared to unquantized models. We also demonstrate that standard modifications, such as bias correction and mixed precision quantization, further improve accuracy.
    When saliency goes off on a tangent: Interpreting Deep Neural Networks with nonlinear saliency maps. (arXiv:2110.06639v3 [cs.LG] UPDATED)
    A fundamental bottleneck in utilising complex machine learning systems for critical applications has been not knowing why they do and what they do, thus preventing the development of any crucial safety protocols. To date, no method exist that can provide full insight into the granularity of the neural network's decision process. In the past, saliency maps were an early attempt at resolving this problem through sensitivity calculations, whereby dimensions of a data point are selected based on how sensitive the output of the system is to them. However, the success of saliency maps has been at best limited, mainly due to the fact that they interpret the underlying learning system through a linear approximation. We present a novel class of methods for generating nonlinear saliency maps which fully account for the nonlinearity of the underlying learning system. While agreeing with linear saliency maps on simple problems where linear saliency maps are correct, they clearly identify more specific drivers of classification on complex examples where nonlinearities are more pronounced. This new class of methods significantly aids interpretability of deep neural networks and related machine learning systems. Crucially, they provide a starting point for their more broad use in serious applications, where 'why' is equally important as 'what'.
    On the Sample Complexity of Stability Constrained Imitation Learning. (arXiv:2102.09161v3 [cs.LG] UPDATED)
    We study the following question in the context of imitation learning for continuous control: how are the underlying stability properties of an expert policy reflected in the sample-complexity of an imitation learning task? We provide the first results showing that a surprisingly granular connection can be made between the underlying expert system's incremental gain stability, a novel measure of robust convergence between pairs of system trajectories, and the dependency on the task horizon $T$ of the resulting generalization bounds. In particular, we propose and analyze incremental gain stability constrained versions of behavior cloning and a DAgger-like algorithm, and show that the resulting sample-complexity bounds naturally reflect the underlying stability properties of the expert system. As a special case, we delineate a class of systems for which the number of trajectories needed to achieve $\varepsilon$-suboptimality is sublinear in the task horizon $T$, and do so without requiring (strong) convexity of the loss function in the policy parameters. Finally, we conduct numerical experiments demonstrating the validity of our insights on both a simple nonlinear system for which the underlying stability properties can be easily tuned, and on a high-dimensional quadrupedal robotic simulation.
    Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks. (arXiv:2007.01498v2 [cs.AI] UPDATED)
    In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an optimal policy in this setting typically requires a large amount of training experiences. Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy. However, to the best of our knowledge, the theoretical properties of reward shaping have thus far only been established in the discounted setting. This paper presents the first reward shaping framework for average-reward learning and proves that, under standard assumptions, the optimal policy under the original reward function can be recovered. In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula. The formula is automatically translated to a shaping function that provides additional reward throughout the learning process. We evaluate the proposed method on three continuing tasks. In all cases, shaping speeds up the average-reward learning rate without any reduction in the performance of the learned policy compared to relevant baselines.
    Sustainable Recreational Fishing Using a Novel Electrical Muscle Stimulation (EMS) Lure and Ensemble Network Algorithm to Maximize Catch and Release Survivability. (arXiv:2006.10125v2 [cs.CV] UPDATED)
    With 200-700 million anglers in the world, sportfishing is nearly five times more common than commercial trawling. Worldwide, hundreds of thousands of jobs are linked to the sportfishing industry, which generates billions of dollars for water-side communities and fisheries conservatories alike. However, the sheer popularity of recreational fishing poses threats to aquatic biodiversity that are hard to regulate. For example, as much as 25% of overfished populations can be traced to anglers. This alarming statistic is explained by the average catch and release mortality rate of 43%, which primarily results from hook-related injuries and careless out-of-water handling. The provisional-patented design proposed in this paper addresses both these problems separately First, a novel, electrical muscle stimulation based fishing lure is proposed as a harmless and low cost alternative to sharp hooks. Early prototypes show a constant electrical current of 90 mA applied through a 200g European perch's jaw can support a reeling tension of 2N - safely within the necessary ranges. Second, a fish-eye camera bob is designed to wirelessly relay underwater footage to a smartphone app, where an ensemble convolutional neural network automatically classifies the fish's species, estimates its length, and cross references with local and state fishing regulations (ie. minimum size, maximum bag limit, and catch season). This capability reduces overfishing by helping anglers avoid accidentally violating guidelines and eliminates the need to reel the fish in and expose it to negligent handling. IN conjunction, this cheap, lightweight, yet high-tech invention is a paradigm shift in preserving a world favorite pastime; while at the same time making recreational fishing more sustainable.
    Universal Prediction Band via Semi-Definite Programming. (arXiv:2103.17203v3 [stat.ML] UPDATED)
    We propose a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification, with or without any user-specified predictive model. Our approach provides an alternative to the now-standard conformal prediction for uncertainty quantification, with novel theoretical insights and computational advantages. The data-adaptive prediction band is universally applicable with minimal distributional assumptions, has strong non-asymptotic coverage properties, and is easy to implement using standard convex programs. Our approach can be viewed as a novel variance interpolation with confidence and further leverages techniques from semi-definite programming and sum-of-squares optimization. Theoretical and numerical performances for the proposed approach for uncertainty quantification are analyzed.
    Decentralized Exploration in Multi-Armed Bandits -- Extended version. (arXiv:1811.07763v6 [cs.LG] UPDATED)
    We consider the decentralized exploration problem: a set of players collaborate to identify the best arm by asynchronously interacting with the same stochastic environment. The objective is to insure privacy in the best arm identification problem between asynchronous, collaborative, and thrifty players. In the context of a digital service, we advocate that this decentralized approach allows a good balance between the interests of users and those of service providers: the providers optimize their services, while protecting the privacy of the users and saving resources. We define the privacy level as the amount of information an adversary could infer by intercepting the messages concerning a single user. We provide a generic algorithm Decentralized Elimination, which uses any best arm identification algorithm as a subroutine. We prove that this algorithm insures privacy, with a low communication cost, and that in comparison to the lower bound of the best arm identification problem, its sample complexity suffers from a penalty depending on the inverse of the probability of the most frequent players. Then, thanks to the genericity of the approach, we extend the proposed algorithm to the non-stationary bandits. Finally, experiments illustrate and complete the analysis.
    Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems. (arXiv:2007.03481v5 [cs.LG] UPDATED)
    This paper presents an inverse reinforcement learning~(IRL) framework for Bayesian stopping time problems. By observing the actions of a Bayesian decision maker, we provide a necessary and sufficient condition to identify if these actions are consistent with optimizing a cost function. In a Bayesian (partially observed) setting, the inverse learner can at best identify optimality wrt the observed actions. Our IRL algorithm identifies optimality and then constructs set valued estimates of the cost function. To achieve this IRL objective, we use novel ideas from Bayesian revealed preferences stemming from microeconomics. We illustrate the proposed IRL scheme using two important examples of stopping time problems, namely, sequential hypothesis testing and Bayesian search, and also on a real-world YouTube dataset. Finally, for finite datasets, we propose an IRL detection algorithm and give finite sample bounds on its error probabilities.
    Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part I. (arXiv:2007.00118v4 [math.FA] UPDATED)
    We study the approximation of functions by tensor networks (TNs). We show that Lebesgue $L^p$-spaces in one dimension can be identified with tensor product spaces of arbitrary order through tensorization. We use this tensor product structure to define subsets of $L^p$ of rank-structured functions of finite representation complexity. These subsets are then used to define different approximation classes of tensor networks, associated with different measures of complexity. These approximation classes are shown to be quasi-normed linear spaces. We study some elementary properties and relationships of said spaces. In part II of this work, we will show that classical smoothness (Besov) spaces are continuously embedded into these approximation classes. We will also show that functions in these approximation classes do not possess any Besov smoothness, unless one restricts the depth of the tensor networks. The results of this work are both an analysis of the approximation spaces of TNs and a study of the expressivity of a particular type of neural networks (NN) -- namely feed-forward sum-product networks with sparse architecture. The input variables of this network result from the tensorization step, interpreted as a particular featuring step which can also be implemented with a neural network with a specific architecture. We point out interesting parallels to recent results on the expressivity of rectified linear unit (ReLU) networks -- currently one of the most popular type of NNs.
    Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part II. (arXiv:2007.00128v4 [math.FA] UPDATED)
    We study the approximation by tensor networks (TNs) of functions from classical smoothness classes. The considered approximation tool combines a tensorization of functions in $L^p([0,1))$, which allows to identify a univariate function with a multivariate function (or tensor), and the use of tree tensor networks (the tensor train format) for exploiting low-rank structures of multivariate functions. The resulting tool can be interpreted as a feed-forward neural network, with first layers implementing the tensorization, interpreted as a particular featuring step, followed by a sum-product network with sparse architecture. In part I of this work, we presented several approximation classes associated with different measures of complexity of tensor networks and studied their properties. In this work (part II), we show how classical approximation tools, such as polynomials or splines (with fixed or free knots), can be encoded as a tensor network with controlled complexity. We use this to derive direct (Jackson) inequalities for the approximation spaces of tensor networks. This is then utilized to show that Besov spaces are continuously embedded into these approximation spaces. In other words, we show that arbitrary Besov functions can be approximated with optimal or near to optimal rate. We also show that an arbitrary function in the approximation class possesses no Besov smoothness, unless one limits the depth of the tensor network.
    Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time Signals. (arXiv:2102.02876v3 [stat.ML] UPDATED)
    We study the classical problem of recovering a multidimensional source signal from observations of nonlinear mixtures of this signal. We show that this recovery is possible (up to a permutation and monotone scaling of the source's original component signals) if the mixture is due to a sufficiently differentiable and invertible but otherwise arbitrarily nonlinear function and the component signals of the source are statistically independent with 'non-degenerate' second-order statistics. The latter assumption requires the source signal to meet one of three regularity conditions which essentially ensure that the source is sufficiently far away from the non-recoverable extremes of being deterministic or constant in time. These assumptions, which cover many popular time series models and stochastic processes, allow us to reformulate the initial problem of nonlinear blind source separation as a simple-to-state problem of optimisation-based function approximation. We propose to solve this approximation problem by minimizing a novel type of objective function that efficiently quantifies the mutual statistical dependence between multiple stochastic processes via cumulant-like statistics. This yields a scalable and direct new method for nonlinear Independent Component Analysis with widely applicable theoretical guarantees and for which our experiments indicate good performance.
    Robust Max Entrywise Error Bounds for Tensor Estimation from Sparse Observations via Similarity Based Collaborative Filtering. (arXiv:1908.01241v4 [cs.LG] UPDATED)
    Consider the task of estimating a 3-order $n \times n \times n$ tensor from noisy observations of randomly chosen entries in the sparse regime. We introduce a similarity based collaborative filtering algorithm for estimating a tensor from sparse observations and argue that it achieves sample complexity that nearly matches the conjectured computationally efficient lower bound on the sample complexity for the setting of low-rank tensors. Our algorithm uses the matrix obtained from the flattened tensor to compute similarity, and estimates the tensor entries using a nearest neighbor estimator. We prove that the algorithm recovers a finite rank tensor with maximum entry-wise error (MEE) and mean-squared-error (MSE) decaying to $0$ as long as each entry is observed independently with probability $p = \Omega(n^{-3/2 + \kappa})$ for any arbitrarily small $\kappa > 0$. More generally, we establish robustness of the estimator, showing that when arbitrary noise bounded by $\varepsilon \geq 0$ is added to each observation, the estimation error with respect to MEE and MSE degrades by $\text{poly}(\varepsilon)$. Consequently, even if the tensor may not have finite rank but can be approximated within $\varepsilon \geq 0$ by a finite rank tensor, then the estimation error converges to $\text{poly}(\varepsilon)$. Our analysis sheds insight into the conjectured sample complexity lower bound, showing that it matches the connectivity threshold of the graph used by our algorithm for estimating similarity between coordinates.
    Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning. (arXiv:1909.05850v6 [stat.ML] UPDATED)
    Off-policy evaluation (OPE) in reinforcement learning is notoriously difficult in long- and infinite-horizon settings due to diminishing overlap between behavior and target policies. In this paper, we study the role of Markovian and time-invariant structure in efficient OPE. We first derive the efficiency bounds for OPE when one assumes each of these structures. This precisely characterizes the curse of horizon: in time-variant processes, OPE is only feasible in the near-on-policy setting, where behavior and target policies are sufficiently similar. But, in time-invariant Markov decision processes, our bounds show that truly-off-policy evaluation is feasible, even with only just one dependent trajectory, and provide the limits of how well we could hope to do. We develop a new estimator based on Double Reinforcement Learning (DRL) that leverages this structure for OPE using the efficient influence function we derive. Our DRL estimator simultaneously uses estimated stationary density ratios and $q$-functions and remains efficient when both are estimated at slow, nonparametric rates and remains consistent when either is estimated consistently. We investigate these properties and the performance benefits of leveraging the problem structure for more efficient OPE.
    Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine Interfaces. (arXiv:2004.11690v3 [eess.SP] UPDATED)
    Motor-Imagery Brain--Machine Interfaces (MI-BMIs)promise direct and accessible communication between human brains and machines by analyzing brain activities recorded with Electroencephalography (EEG). Latency, reliability, and privacy constraints make it unsuitable to offload the computation to the cloud. Practical use cases demand a wearable, battery-operated device with low average power consumption for long-term use. Recently, sophisticated algorithms, in particular deep learning models, have emerged for classifying EEG signals. While reaching outstanding accuracy, these models often exceed the limitations of edge devices due to their memory and computational requirements. In this paper, we demonstrate algorithmic and implementation optimizations for EEGNET, a compact Convolutional Neural Network (CNN) suitable for many BMI paradigms. We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0.4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr.Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster. With our proposed optimization steps, we can obtain an overall speedup of 64x and a reduction of up to 85% in memory footprint with respect to a single-core layer-wise baseline implementation. Our implementation takes only 5.82 ms and consumes 0.627 mJ per inference. With 21.0GMAC/s/W, it is 256x more energy-efficient than an EEGNET implementation on an ARM Cortex-M7 (0.082GMAC/s/W).
    Approximation Theory of Tree Tensor Networks: Tensorized Multivariate Functions. (arXiv:2101.11932v2 [math.FA] UPDATED)
    We study the approximation of multivariate functions with tensor networks (TNs). The main conclusion of this work is an answer to the following two questions: "What are the approximation capabilities of TNs?" and "What is an appropriate model class of functions that can be approximated with TNs?" To answer the former: we show that TNs can (near to) optimally replicate $h$-uniform and $h$-adaptive approximation, for any smoothness order of the target function. Tensor networks thus exhibit universal expressivity w.r.t. isotropic, anisotropic and mixed smoothness spaces that is comparable with more general neural networks families such as deep rectified linear unit (ReLU) networks. Put differently, TNs have the capacity to (near to) optimally approximate many function classes -- without being adapted to the particular class in question. To answer the latter: as a candidate model class we consider approximation classes of TNs and show that these are (quasi-)Banach spaces, that many types of classical smoothness spaces are continuously embedded into said approximation classes and that TN approximation classes are themselves not embedded in any classical smoothness space.
    Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms. (arXiv:2006.14514v4 [cs.LG] UPDATED)
    Artificial neural networks (ANNs) are typically highly nonlinear systems which are finely tuned via the optimization of their associated, non-convex loss functions. In many cases, the gradient of any such loss function has superlinear growth, making the use of the widely-accepted (stochastic) gradient descent methods, which are based on Euler numerical schemes, problematic. We offer a new learning algorithm based on an appropriately constructed variant of the popular stochastic gradient Langevin dynamics (SGLD), which is called tamed unadjusted stochastic Langevin algorithm (TUSLA). We also provide a nonasymptotic analysis of the new algorithm's convergence properties in the context of non-convex learning problems with the use of ANNs. Thus, we provide finite-time guarantees for TUSLA to find approximate minimizers of both empirical and population risks. The roots of the TUSLA algorithm are based on the taming technology for diffusion processes with superlinear coefficients as developed in \citet{tamed-euler, SabanisAoAP} and for MCMC algorithms in \citet{tula}. Numerical experiments are presented which confirm the theoretical findings and illustrate the need for the use of the new algorithm in comparison to vanilla SGLD within the framework of ANNs.
    Learned Lifted Linearization Applied to Unstable Dynamic Systems Enabled by Koopman Direct Encoding. (arXiv:2210.13602v2 [cs.LG] UPDATED)
    This paper presents a Koopman lifting linearization method that is applicable to nonlinear dynamical systems having both stable and unstable regions. It is known that DMD and other standard data-driven methods face a fundamental difficulty in constructing a Koopman model when applied to unstable systems. Here we solve the problem by incorporating knowledge about a nonlinear state equation with a learning method for finding an effective set of observables. In a lifted space, stable and unstable regions are separated into independent subspaces. Based on this property, we propose to find effective observables through neural net training where training data are separated into stable and unstable trajectories. The resultant learned observables are used for constructing a linear state transition matrix using method known as Direct Encoding, which transforms the nonlinear state equation to a state transition matrix through inner product computations with the observables. The proposed method shows a dramatic improvement over existing DMD and data-driven methods.
    Identifying Time Lag in Dynamical Systems with Copula Entropy based Transfer Entropy. (arXiv:2301.06037v1 [cs.LG])
    Time lag between variables is a key characteristics of dynamical systems in different fields and identifying such time lag is a central problem in complex systems with many applications. Transfer Entropy (TE) was proposed as a tool for time lag identification recently. Unfortunately, estimating TE has been a notoriously difficult problem. Copula Entropy (CE) is a measure of statistical independence and it was proved that TE can be represented with only CE. Therefore, a non-parametric estimator of TE based on CE was proposed according to such representation recently. In this paper we propose to use the CE-based estimator of TE to identify time lag in dynamical systems. Both simulated and real data are used to verify the effectiveness of the proposed method in the experiments. Experimental results show that the proposed method can identify the time lags in the three simulated systems. The real data experiment with the data on power consumption of the Tetouan city also demonstrates that our method can identify the pattern of time lags through the estimated TE from the weather factors to the power consumption of the city.
    Pluto's Surface Mapping using Unsupervised Learning from Near-Infrared Observations of LEISA/Ralph. (arXiv:2301.06027v1 [astro-ph.EP])
    We map the surface of Pluto using an unsupervised machine learning technique using the near-infrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. The principal component reduced Gaussian mixture model was implemented to investigate the geographic distribution of the surface units across the dwarf planet. We also present the likelihood of each surface unit at the image pixel level. Average I/F spectra of each unit were analyzed -- in terms of the position and strengths of absorption bands of abundant volatiles such as N${}_{2}$, CH${}_{4}$, and CO and nonvolatile H${}_{2}$O -- to connect the unit to surface composition, geology, and geographic location. The distribution of surface units shows a latitudinal pattern with distinct surface compositions of volatiles -- consistent with the existing literature. However, previous mapping efforts were based primarily on compositional analysis using spectral indices (indicators) or implementation of complex radiative transfer models, which need (prior) expert knowledge, label data, or optical constants of representative endmembers. We prove that an application of unsupervised learning in this instance renders a satisfactory result in mapping the spatial distribution of ice compositions without any prior information or label data. Thus, such an application is specifically advantageous for a planetary surface mapping when label data are poorly constrained or completely unknown, because an understanding of surface material distribution is vital for volatile transport modeling at the planetary scale. We emphasize that the unsupervised learning used in this study has wide applicability and can be expanded to other planetary bodies of the Solar System for mapping surface material distribution.
    Self-recovery of memory via generative replay. (arXiv:2301.06030v1 [cs.NE])
    A remarkable capacity of the brain is its ability to autonomously reorganize memories during offline periods. Memory replay, a mechanism hypothesized to underlie biological offline learning, has inspired offline methods for reducing forgetting in artificial neural networks in continual learning settings. A memory-efficient and neurally-plausible method is generative replay, which achieves state of the art performance on continual learning benchmarks. However, unlike the brain, standard generative replay does not self-reorganize memories when trained offline on its own replay samples. We propose a novel architecture that augments generative replay with an adaptive, brain-like capacity to autonomously recover memories. We demonstrate this capacity of the architecture across several continual learning tasks and environments.
    Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression. (arXiv:2301.05901v1 [cs.LG])
    The coordination of robotic swarms and the remote wireless control of industrial systems are among the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that needs to be shared over the wireless medium can overload even high-capacity connections. Consequently, solving the effective communication problem by optimizing the transmission strategy to discard irrelevant information can provide a significant advantage, but is often a very complex task. In this work, we consider a prototypal system in which an observer must communicate its sensory data to an actor controlling a task (e.g., a mobile robot in a factory). We then model it as a remote Partially Observable Markov Decision Process (POMDP), considering the effect of adopting semantic and effective communication-oriented solutions on the overall system performance. We split the communication problem by considering an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level, considering both the current state of the environment and the memory of past messages. We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase over traditional approaches
    An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing. (arXiv:2004.00077v3 [eess.SP] UPDATED)
    This paper presents an accurate and robust embedded motor-imagery brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet, matches the requirements of memory footprint and computational resources of low-power microcontroller units (MCUs), such as the ARM Cortex-M family. Furthermore, the paper presents a set of methods, including temporal downsampling, channel selection, and narrowing of the classification window, to further scale down the model to relax memory requirements with negligible accuracy degradation. Experimental results on the Physionet EEG Motor Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and 65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global validation, outperforming the state-of-the-art (SoA) convolutional neural network (CNN) by 2.05%, 5.25%, and 5.48%. Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6x memory footprint reduction and a small accuracy loss of 2.51% with 15x reduction. The scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and consuming 4.28mJ per inference for operating the smallest model, and on a Cortex-M7 with 44ms and 18.1mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI.
    Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders. (arXiv:2210.16844v2 [cs.LG] UPDATED)
    Generative models for graph data are an important research topic in machine learning. Graph data comprise two levels that are typically analyzed separately: node-level properties such as the existence of a link between a pair of nodes, and global aggregate graph-level statistics, such as motif counts. This paper proposes a new multi-level framework that jointly models node-level properties and graph-level statistics, as mutually reinforcing sources of information. We introduce a new micro-macro training objective for graph generation that combines node-level and graph-level losses. We utilize the micro-macro objective to improve graph generation with a GraphVAE, a well-established model based on graph-level latent variables, that provides fast training and generation time for medium-sized graphs. Our experiments show that adding micro-macro modeling to the GraphVAE model improves graph quality scores up to 2 orders of magnitude on five benchmark datasets, while maintaining the GraphVAE generation speed advantage.
    EvoAAA: An evolutionary methodology for automated \neural autoencoder architecture search. (arXiv:2301.06047v1 [cs.NE])
    Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized coding, in a reduced time.
    Margin Optimal Classification Trees. (arXiv:2210.10567v4 [math.OC] UPDATED)
    In recent years there has been growing attention to interpretable machine learning models which can give explanatory insights on their behavior. Thanks to their interpretability, decision trees have been intensively studied for classification tasks, and due to the remarkable advances in mixed-integer programming (MIP), various approaches have been proposed to formulate the problem of training an Optimal Classification Tree (OCT) as a MIP model. We present a novel mixed-integer quadratic formulation for the OCT problem, which exploits the generalization capabilities of Support Vector Machines for binary classification. Our model, denoted as Margin Optimal Classification Tree (MARGOT), encompasses the use of maximum margin multivariate hyperplanes nested in a binary tree structure. To enhance the interpretability of our approach, we analyse two alternative versions of MARGOT, which include feature selection constraints inducing local sparsity of the hyperplanes. First, MARGOT has been tested on non-linearly separable synthetic datasets in 2-dimensional feature space to provide a graphical representation of the maximum margin approach. Finally, the proposed models have been tested on benchmark datasets from the UCI repository. The MARGOT formulation turns out to be easier to solve than other OCT approaches, and the generated tree better generalizes on new observations. The two interpretable versions are effective in selecting the most relevant features and maintaining good prediction quality.
    A Review on the effectiveness of Dimensional Reduction with Computational Forensics: An Application on Malware Analysis. (arXiv:2301.06031v1 [cs.CR])
    The Android operating system is pervasively adopted as the operating system platform of choice for smart devices. However, the strong adoption has also resulted in exponential growth in the number of Android based malicious software or malware. To deal with such cyber threats as part of cyber investigation and digital forensics, computational techniques in the form of machine learning algorithms are applied for such malware identification, detection and forensics analysis. However, such Computational Forensics modelling techniques are constrained the volume, velocity, variety and veracity of the malware landscape. This in turn would affect its identification and detection effectiveness. Such consequence would inherently induce the question of sustainability with such solution approach. One approach to optimise effectiveness is to apply dimensional reduction techniques like Principal Component Analysis with the intent to enhance algorithmic performance. In this paper, we evaluate the effectiveness of the application of Principle Component Analysis on Computational Forensics task of detecting Android based malware. We applied our research hypothesis to three different datasets with different machine learning algorithms. Our research result showed that the dimensionally reduced dataset would result in a measure of degradation in accuracy performance.
    On the role of Model Uncertainties in Bayesian Optimization. (arXiv:2301.05983v1 [stat.ML])
    Bayesian optimization (BO) is a popular method for black-box optimization, which relies on uncertainty as part of its decision-making process when deciding which experiment to perform next. However, not much work has addressed the effect of uncertainty on the performance of the BO algorithm and to what extent calibrated uncertainties improve the ability to find the global optimum. In this work, we provide an extensive study of the relationship between the BO performance (regret) and uncertainty calibration for popular surrogate models and compare them across both synthetic and real-world experiments. Our results confirm that Gaussian Processes are strong surrogate models and that they tend to outperform other popular models. Our results further show a positive association between calibration error and regret, but interestingly, this association disappears when we control for the type of model in the analysis. We also studied the effect of re-calibration and demonstrate that it generally does not lead to improved regret. Finally, we provide theoretical justification for why uncertainty calibration might be difficult to combine with BO due to the small sample sizes commonly used.
    Sinkhorn Divergences for Unbalanced Optimal Transport. (arXiv:1910.12958v3 [math.OC] UPDATED)
    Optimal transport induces the Earth Mover's (Wasserstein) distance between probability distributions, a geometric divergence that is relevant to a wide range of problems. Over the last decade, two relaxations of optimal transport have been studied in depth: unbalanced transport, which is robust to the presence of outliers and can be used when distributions don't have the same total mass; entropy-regularized transport, which is robust to sampling noise and lends itself to fast computations using the Sinkhorn algorithm. This paper combines both lines of work to put robust optimal transport on solid ground. Our main contribution is a generalization of the Sinkhorn algorithm to unbalanced transport: our method alternates between the standard Sinkhorn updates and the pointwise application of a contractive function. This implies that entropic transport solvers on grid images, point clouds and sampled distributions can all be modified easily to support unbalanced transport, with a proof of linear convergence that holds in all settings. We then show how to use this method to define pseudo-distances on the full space of positive measures that satisfy key geometric axioms: (unbalanced) Sinkhorn divergences are differentiable, positive, definite, convex, statistically robust and avoid any "entropic bias" towards a shrinkage of the measures' supports.
    On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. (arXiv:2301.06010v1 [cs.LG])
    When there are unlabeled Out-Of-Distribution (OOD) data from other classes, Semi-Supervised Learning (SSL) methods suffer from severe performance degradation and even get worse than merely training on labeled data. In this paper, we empirically analyze Pseudo-Labeling (PL) in class-mismatched SSL. PL is a simple and representative SSL method that transforms SSL problems into supervised learning by creating pseudo-labels for unlabeled data according to the model's prediction. We aim to answer two main questions: (1) How do OOD data influence PL? (2) What is the proper usage of OOD data with PL? First, we show that the major problem of PL is imbalanced pseudo-labels on OOD data. Second, we find that OOD data can help classify In-Distribution (ID) data given their OOD ground truth labels. Based on the findings, we propose to improve PL in class-mismatched SSL with two components -- Re-balanced Pseudo-Labeling (RPL) and Semantic Exploration Clustering (SEC). RPL re-balances pseudo-labels of high-confidence data, which simultaneously filters out OOD data and addresses the imbalance problem. SEC uses balanced clustering on low-confidence data to create pseudo-labels on extra classes, simulating the process of training with ground truth. Experiments show that our method achieves steady improvement over supervised baseline and state-of-the-art performance under all class mismatch ratios on different benchmarks.
    Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes. (arXiv:2301.06021v1 [cs.LG])
    This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gaussian graphical models for tensor-variate data and summarization of complex time-varying texts using topic models. This work advances the state-of-the-art in several directions. First, it introduces a new class of tensor-variate Gaussian graphical models via the Sylvester tensor equation. Second, it develops an optimization technique based on a fast-converging proximal alternating linearized minimization method, which scales tensor-variate Gaussian graphical model estimations to modern big-data settings. Third, it connects Kronecker-structured (inverse) covariance models with spatio-temporal partial differential equations (PDEs) and introduces a new framework for ensemble Kalman filtering that is capable of tracking chaotic physical systems. Fourth, it proposes a modular and interpretable framework for unsupervised and weakly-supervised probabilistic topic modeling of time-varying data that combines generative statistical models with computational geometric methods. Throughout, practical applications of the methodology are considered using real datasets. This includes brain-connectivity analysis using EEG data, space weather forecasting using solar imaging data, longitudinal analysis of public opinions using Twitter data, and mining of mental health related issues using TalkLife data. We show in each case that the graphical modeling framework introduced here leads to improved interpretability, accuracy, and scalability.
    Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data. (arXiv:2210.08642v2 [cs.LG] UPDATED)
    Offline reinforcement learning (RL) can be used to improve future performance by leveraging historical data. There exist many different algorithms for offline RL, and it is well recognized that these algorithms, and their hyperparameter settings, can lead to decision policies with substantially differing performance. This prompts the need for pipelines that allow practitioners to systematically perform algorithm-hyperparameter selection for their setting. Critically, in most real-world settings, this pipeline must only involve the use of historical data. Inspired by statistical model selection methods for supervised learning, we introduce a task- and method-agnostic pipeline for automatically training, comparing, selecting, and deploying the best policy when the provided dataset is limited in size. In particular, our work highlights the importance of performing multiple data splits to produce more reliable algorithm-hyperparameter selection. While this is a common approach in supervised learning, to our knowledge, this has not been discussed in detail in the offline RL setting. We show it can have substantial impacts when the dataset is small. Compared to alternate approaches, our proposed pipeline outputs higher-performing deployed policies from a broad range of offline policy learning algorithms and across various simulation domains in healthcare, education, and robotics. This work contributes toward the development of a general-purpose meta-algorithm for automatic algorithm-hyperparameter selection for offline RL.
    Static, dynamic and stability analysis of multi-dimensional functional graded plate with variable thickness using deep neural network. (arXiv:2301.05900v1 [cs.LG])
    The goal of this paper is to analyze and predict the central deflection, natural frequency, and critical buckling load of the multi-directional functionally graded (FG) plate with variable thickness resting on an elastic Winkler foundation. First, the mathematical models of the static and eigenproblems are formulated in great detail. The FG material properties are assumed to vary smoothly and continuously throughout three directions of the plate according to a Mori-Tanaka micromechanics model distribution of volume fraction of constituents. Then, finite element analysis (FEA) with mixed interpolation of tensorial components of 4-nodes (MITC4) is implemented in order to eliminate theoretically a shear locking phenomenon existing. Next, influences of the variable thickness functions (uniform, non-uniform linear, and non-uniform non-linear), material properties, length-to-thickness ratio, boundary conditions, and elastic parameters on the plate response are investigated and discussed in detail through several numerical examples. Finally, a deep neural network (DNN) technique using batch normalization (BN) is learned to predict the non-dimensional values of multi-directional FG plates. The DNN model also shows that it is a powerful technique capable of handling an extensive database and different vital parameters in engineering applications.
    Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation. (arXiv:2301.05980v1 [cs.RO])
    Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these applications is to reach a destination without colliding with obstacles or other robot arms. Commonly used sampling-based path planning approaches such as RRT require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing the approaches in new environments, a tedious engineering effort in setting hyperparameters needs to be conducted, which is time- and cost-intensive. On the other hand, Deep Reinforcement Learning has shown remarkable results in dealing with unknown environments, generalizing new problem instances, and solving motion planning problems efficiently. On that account, this paper proposes a Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We evaluated our model against state-of-the-art sampling-based planners in several experiments. The results show the superiority of our planner in terms of path length and execution time.
    Recent advances in artificial intelligence for retrosynthesis. (arXiv:2301.05864v1 [cs.LG])
    Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand-new molecules. Conventional rule-based or expert-based computer-aided synthesis has obvious limitations, such as high labor costs and limited search space. In recent years, dramatic breakthroughs driven by artificial intelligence have revolutionized retrosynthesis. Here we aim to present a comprehensive review of recent advances in AI-based retrosynthesis. For single-step and multi-step retrosynthesis both, we first list their goal and provide a thorough taxonomy of existing methods. Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets. In the next part we introduce popular databases and established platforms for retrosynthesis. Finally, this review concludes with a discussion about promising research directions in this field.
    Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning. (arXiv:2301.05931v1 [cs.LG])
    Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we collected data from various datasets covering various drug-related aspects. Then, we take advantage of large-scale pre-training models to generate informative representations and features for drugs, proteins, and diseases. Based on that, a message-passing graph is built on top to propagate information together with graph structure learning flexibility. This is first introduced in the biological networks and enables us to generate pseudo-relations in the graph. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods on synergistic prediction benchmark datasets. We are also capable of inferencing new drug combination data in a test on an independent set released by AstraZeneca, where 10% of improvement over previous methods is observed. In addition, we're robust against unseen drugs and surpass almost 15% AU ROC compared to the second-best model. We believe our framework contributes to both the future wet-lab discovery of novel drugs and the building of promising guidance for precise combination medicine.
    Desbordante: from benchmarking suite to high-performance science-intensive data profiler (preprint). (arXiv:2301.05965v1 [cs.DB])
    Pioneering data profiling systems such as Metanome and OpenClean brought public attention to science-intensive data profiling. This type of profiling aims to extract complex patterns (primitives) such as functional dependencies, data constraints, association rules, and others. However, these tools are research prototypes rather than production-ready systems. The following work presents Desbordante - a high-performance science-intensive data profiler with open source code. Unlike similar systems, it is built with emphasis on industrial application in a multi-user environment. It is efficient, resilient to crashes, and scalable. Its efficiency is ensured by implementing discovery algorithms in C++, resilience is achieved by extensive use of containerization, and scalability is based on replication of containers. Desbordante aims to open industrial-grade primitive discovery to a broader public, focusing on domain experts who are not IT professionals. Aside from the discovery of various primitives, Desbordante offers primitive validation, which not only reports whether a given instance of primitive holds or not, but also points out what prevents it from holding via the use of special screens. Next, Desbordante supports pipelines - ready-to-use functionality implemented using the discovered primitives, for example, typo detection. We provide built-in pipelines, and the users can construct their own via provided Python bindings. Unlike other profilers, Desbordante works not only with tabular data, but with graph and transactional data as well. In this paper, we present Desbordante, the vision behind it and its use-cases. To provide a more in-depth perspective, we discuss its current state, architecture, and design decisions it is built on. Additionally, we outline our future plans.
    World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges. (arXiv:2301.05832v1 [cs.RO])
    Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e., controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future.
    Hand Gesture Recognition through Reflected Infrared Light Wave Signals. (arXiv:2301.05955v1 [eess.SP])
    In this study, we present a wireless (non-contact) gesture recognition method using only incoherent light wave signals reflected from a human subject. In comparison to existing radar, light shadow, sound and camera-based sensing systems, this technology uses a low-cost ubiquitous light source (e.g., infrared LED) to send light towards the subject's hand performing gestures and the reflected light is collected by a light sensor (e.g., photodetector). This light wave sensing system recognizes different gestures from the variations of the received light intensity within a 20-35cm range. The hand gesture recognition results demonstrate up to 96% accuracy on average. The developed system can be utilized in numerous Human-computer Interaction (HCI) applications as a low-cost and non-contact gesture recognition technology.
    Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures. (arXiv:2301.05981v1 [cs.LG])
    Traditional reinforcement learning (RL) aims to maximize the expected total reward, while the risk of uncertain outcomes needs to be controlled to ensure reliable performance in a risk-averse setting. In this paper, we consider the problem of maximizing dynamic risk of a sequence of rewards in infinite-horizon Markov Decision Processes (MDPs). We adapt the Expected Conditional Risk Measures (ECRMs) to the infinite-horizon risk-averse MDP and prove its time consistency. Using a convex combination of expectation and conditional value-at-risk (CVaR) as a special one-step conditional risk measure, we reformulate the risk-averse MDP as a risk-neutral counterpart with augmented action space and manipulation on the immediate rewards. We further prove that the related Bellman operator is a contraction mapping, which guarantees the convergence of any value-based RL algorithms. Accordingly, we develop a risk-averse deep Q-learning framework, and our numerical studies based on two simple MDPs show that the risk-averse setting can reduce the variance and enhance robustness of the results.
    Generalized Invariant Matching Property via LASSO. (arXiv:2301.05975v1 [stat.ME])
    Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance principle is violated when the response is intervened, making it a difficult setting. In a recent work, the invariant matching property has been developed to shed light on this scenario and shows promising performance. In this work, we generalize the invariant matching property by formulating a high-dimensional problem with intrinsic sparsity. We propose a more robust and computation-efficient algorithm by leveraging a variant of Lasso, improving upon the existing algorithms.
    Adaptive Neural Networks Using Residual Fitting. (arXiv:2301.05744v1 [cs.LG])
    Current methods for estimating the required neural-network size for a given problem class have focused on methods that can be computationally intensive, such as neural-architecture search and pruning. In contrast, methods that add capacity to neural networks as needed may provide similar results to architecture search and pruning, but do not require as much computation to find an appropriate network size. Here, we present a network-growth method that searches for explainable error in the network's residuals and grows the network if sufficient error is detected. We demonstrate this method using examples from classification, imitation learning, and reinforcement learning. Within these tasks, the growing network can often achieve better performance than small networks that do not grow, and similar performance to networks that begin much larger.
    Day-Ahead PV Power Forecasting Based on MSTL-TFT. (arXiv:2301.05911v1 [cs.LG])
    Energy demand is increasing dramatically as global urbanization progresses.Solar energy is a clean energy source with low production and maintenance costs.Accurately predicted PV generation is of great importance for grid integration.Recent day-ahead PV forecasting studies mainly include generation data decomposition, additional meteorological and equipment features, improvement and integration of ANN-based models.We proposed a MSTL-TFT method for day-ahead PV forecasting. The results are better than any of the other studies we have surveyed on day-ahead DKASC PV forecasting.
    Lung airway geometry as an early predictor of autism: A preliminary machine learning-based study. (arXiv:2301.05777v1 [cs.LG])
    The goal of this study is to assess the feasibility of airway geometry as a biomarker for ASD. Chest CT images of children with a documented diagnosis of ASD as well as healthy controls were identified retrospectively. 54 scans were obtained for analysis, including 31 ASD cases and 23 age and sex-matched controls. A feature selection and classification procedure using principal component analysis (PCA) and support vector machine (SVM) achieved a peak cross validation accuracy of nearly 89% using a feature set of 8 airway branching angles. Sensitivity was 94%, but specificity was only 78%. The results suggest a measurable difference in airway branchpoint angles between children with ASD and the control population.
    CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials. (arXiv:2301.05852v1 [cs.LG])
    In recent years, graph neural network (GNN) based approaches have emerged as a powerful technique to encode complex topological structure of crystal materials in an enriched representation space. These models are often supervised in nature and using the property-specific training data, learn relationship between crystal structure and different properties like formation energy, bandgap, bulk modulus, etc. Most of these methods require a huge amount of property-tagged data to train the system which may not be available for different properties. However, there is an availability of a huge amount of crystal data with its chemical composition and structural bonds. To leverage these untapped data, this paper presents CrysGNN, a new pre-trained GNN framework for crystalline materials, which captures both node and graph level structural information of crystal graphs using a huge amount of unlabelled material data. Further, we extract distilled knowledge from CrysGNN and inject into different state of the art property predictors to enhance their property prediction accuracy. We conduct extensive experiments to show that with distilled knowledge from the pre-trained model, all the SOTA algorithms are able to outperform their own vanilla version with good margins. We also observe that the distillation process provides a significant improvement over the conventional approach of finetuning the pre-trained model. We have released the pre-trained model along with the large dataset of 800K crystal graph which we carefully curated; so that the pretrained model can be plugged into any existing and upcoming models to enhance their prediction accuracy.
    Discovery of 2D materials using Transformer Network based Generative Design. (arXiv:2301.05824v1 [cond-mat.mtrl-sci])
    Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have been reported. Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications. Here we propose a generative material design pipeline, namely material transformer generator(MTG), for large-scale discovery of hypothetical 2D materials. We train two 2D materials composition generators using self-learning neural language models based on Transformers with and without transfer learning. The models are then used to generate a large number of candidate 2D compositions, which are fed to known 2D materials templates for crystal structure prediction. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. We report four new DFT-verified stable 2D materials with zero e-above-hull energies, including NiCl$_4$, IrSBr, CuBr$_3$, and CoBrCl. Our work thus demonstrates the potential of our MTG generative materials design pipeline in the discovery of novel 2D materials and other functional materials.
    A Survey of Self-Supervised Learning from Multiple Perspectives: Algorithms, Theory, Applications and Future Trends. (arXiv:2301.05712v1 [cs.LG])
    Deep supervised learning algorithms generally require large numbers of labeled examples to attain satisfactory performance. To avoid the expensive cost incurred by collecting and labeling too many examples, as a subset of unsupervised learning, self-supervised learning (SSL) was proposed to learn good features from many unlabeled examples without any human-annotated labels. SSL has recently become a hot research topic, and many related algorithms have been proposed. However, few comprehensive studies have explained the connections among different SSL variants and how they have evolved. In this paper, we attempt to provide a review of the various SSL methods from the perspectives of algorithms, theory, applications, three main trends, and open questions. First, the motivations of most SSL algorithms are introduced in detail, and their commonalities and differences are compared. Second, the theoretical issues associated with SSL are investigated. Third, typical applications of SSL in areas such as image processing and computer vision (CV), as well as natural language processing (NLP), are discussed. Finally, the three main trends of SSL and the open research questions are discussed. A collection of useful materials is available at https://github.com/guijiejie/SSL.
    Survey of Knowledge Distillation in Federated Edge Learning. (arXiv:2301.05849v1 [cs.LG])
    The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data. \textcolor{black}{Limited by device hardware, diverse user behaviors and network infrastructure, the algorithm design of FEL faces challenges related to resources, personalization and network environments}, and Knowledge Distillation (KD) has been leveraged as an important technique to tackle the above challenges in FEL. In this paper, we investigate the works that KD applies to FEL, discuss the limitations and open problems of existing KD-based FEL approaches, and provide guidance for their real deployment.
    First Three Years of the International Verification of Neural Networks Competition (VNN-COMP). (arXiv:2301.05815v1 [cs.LG])
    This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments.
    A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows. (arXiv:2301.05763v1 [cs.LG])
    The ability to replicate predictions by machine learning (ML) or artificial intelligence (AI) models and results in scientific workflows that incorporate such ML/AI predictions is driven by numerous factors. An uncertainty-aware metric that can quantitatively assess the reproducibility of quantities of interest (QoI) would contribute to the trustworthiness of results obtained from scientific workflows involving ML/AI models. In this article, we discuss how uncertainty quantification (UQ) in a Bayesian paradigm can provide a general and rigorous framework for quantifying reproducibility for complex scientific workflows. Such as framework has the potential to fill a critical gap that currently exists in ML/AI for scientific workflows, as it will enable researchers to determine the impact of ML/AI model prediction variability on the predictive outcomes of ML/AI-powered workflows. We expect that the envisioned framework will contribute to the design of more reproducible and trustworthy workflows for diverse scientific applications, and ultimately, accelerate scientific discoveries.
    CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence. (arXiv:2301.05872v1 [math.OC])
    In this paper, we consider solving the distributed optimization problem over a multi-agent network under the communication restricted setting. We study a compressed decentralized stochastic gradient method, termed ``compressed exact diffusion with adaptive stepsizes (CEDAS)", and show the method asymptotically achieves comparable convergence rate as centralized SGD for both smooth strongly convex objective functions and smooth nonconvex objective functions under unbiased compression operators. In particular, to our knowledge, CEDAS enjoys so far the shortest transient time (with respect to the graph specifics) for achieving the convergence rate of centralized SGD, which behaves as $\mathcal{O}(nC^3/(1-\lambda_2)^{2})$ under smooth strongly convex objective functions, and $\mathcal{O}(n^3C^6/(1-\lambda_2)^4)$ under smooth nonconvex objective functions, where $(1-\lambda_2)$ denotes the spectral gap of the mixing matrix, and $C>0$ is the compression-related parameter. Numerical experiments further demonstrate the effectiveness of the proposed algorithm.
    Poisoning Attacks and Defenses in Federated Learning: A Survey. (arXiv:2301.05795v1 [cs.CR])
    Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients datasets and the training process poses a variety of security threats. This survey provides the taxonomy of poisoning attacks and experimental evaluation to discuss the need for robust FL.
    GAR: Generalized Autoregression for Multi-Fidelity Fusion. (arXiv:2301.05729v1 [stat.ML])
    In many scientific research and engineering applications where repeated simulations of complex systems are conducted, a surrogate is commonly adopted to quickly estimate the whole system. To reduce the expensive cost of generating training examples, it has become a promising approach to combine the results of low-fidelity (fast but inaccurate) and high-fidelity (slow but accurate) simulations. Despite the fast developments of multi-fidelity fusion techniques, most existing methods require particular data structures and do not scale well to high-dimensional output. To resolve these issues, we generalize the classic autoregression (AR), which is wildly used due to its simplicity, robustness, accuracy, and tractability, and propose generalized autoregression (GAR) using tensor formulation and latent features. GAR can deal with arbitrary dimensional outputs and arbitrary multifidelity data structure to satisfy the demand of multi-fidelity fusion for complex problems; it admits a fully tractable likelihood and posterior requiring no approximate inference and scales well to high-dimensional problems. Furthermore, we prove the autokrigeability theorem based on GAR in the multi-fidelity case and develop CIGAR, a simplified GAR with the exact predictive mean accuracy with computation reduction by a factor of d 3, where d is the dimensionality of the output. The empirical assessment includes many canonical PDEs and real scientific examples and demonstrates that the proposed method consistently outperforms the SOTA methods with a large margin (up to 6x improvement in RMSE) with only a couple high-fidelity training samples.
    A Comprehensive Survey of Graph-level Learning. (arXiv:2301.05860v1 [cs.LG])
    Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as input, has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs tend to rely on hand-crafted features, such as substructures. But while these methods benefit from good interpretability, they often suffer from computational bottlenecks as they cannot skirt the graph isomorphism problem. Conversely, deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and decoding graphs into low-dimensional representations. As a result, these deep graph learning methods have been responsible for many successes. Yet, there is no comprehensive survey that reviews graph-level learning starting with traditional learning and moving through to the deep learning approaches. This article fills this gap and frames the representative algorithms into a systematic taxonomy covering traditional learning, graph-level deep neural networks, graph-level graph neural networks, and graph pooling. To ensure a thoroughly comprehensive survey, the evolutions, interactions, and communications between methods from four different branches of development are also examined. This is followed by a brief review of the benchmark data sets, evaluation metrics, and common downstream applications. The survey concludes with 13 future directions of necessary research that will help to overcome the challenges facing this booming field.
    Local Model Explanations and Uncertainty Without Model Access. (arXiv:2301.05761v1 [cs.LG])
    We present a model-agnostic algorithm for generating post-hoc explanations and uncertainty intervals for a machine learning model when only a sample of inputs and outputs from the model is available, rather than direct access to the model itself. This situation may arise when model evaluations are expensive; when privacy, security and bandwidth constraints are imposed; or when there is a need for real-time, on-device explanations. Our algorithm constructs explanations using local polynomial regression and quantifies the uncertainty of the explanations using a bootstrapping approach. Through a simulation study, we show that the uncertainty intervals generated by our algorithm exhibit a favorable trade-off between interval width and coverage probability compared to the naive confidence intervals from classical regression analysis. We further demonstrate the capabilities of our method by applying it to black-box models trained on two real datasets.
    Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making. (arXiv:2301.05809v1 [cs.HC])
    In AI-assisted decision-making, it is critical for human decision-makers to know when to trust AI and when to trust themselves. However, prior studies calibrated human trust only based on AI confidence indicating AI's correctness likelihood (CL) but ignored humans' CL, hindering optimal team decision-making. To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level. We first modeled humans' CL by approximating their decision-making models and computing their potential performance in similar instances. We demonstrated the feasibility and effectiveness of our model via two preliminary studies. Then, we proposed three CL exploitation strategies to calibrate users' trust explicitly/implicitly in the AI-assisted decision-making process. Results from a between-subjects experiment (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence. We further provided practical implications for more human-compatible AI-assisted decision-making.
    Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification. (arXiv:2301.05869v1 [cs.LG])
    It is desirable for statistical models to detect signals of interest independently of their position. If the data is generated by some smooth process, this additional structure should be taken into account. We introduce a new class of neural networks that are shift invariant and preserve smoothness of the data: functional neural networks (FNNs). For this, we use methods from functional data analysis (FDA) to extend multi-layer perceptrons and convolutional neural networks to functional data. We propose different model architectures, show that the models outperform a benchmark model from FDA in terms of accuracy and successfully use FNNs to classify electroencephalography (EEG) data.
    Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement. (arXiv:2206.13310v3 [eess.AS] UPDATED)
    The key advantage of using multiple microphones for speech enhancement is that spatial filtering can be used to complement the tempo-spectral processing. In a traditional setting, linear spatial filtering (beamforming) and single-channel post-filtering are commonly performed separately. In contrast, there is a trend towards employing deep neural networks (DNNs) to learn a joint spatial and tempo-spectral non-linear filter, which means that the restriction of a linear processing model and that of a separate processing of spatial and tempo-spectral information can potentially be overcome. However, the internal mechanisms that lead to good performance of such data-driven filters for multi-channel speech enhancement are not well understood. Therefore, in this work, we analyse the properties of a non-linear spatial filter realized by a DNN as well as its interdependency with temporal and spectral processing by carefully controlling the information sources (spatial, spectral, and temporal) available to the network. We confirm the superiority of a non-linear spatial processing model, which outperforms an oracle linear spatial filter in a challenging speaker extraction scenario for a low number of microphones by 0.24 POLQA score. Our analyses reveal that in particular spectral information should be processed jointly with spatial information as this increases the spatial selectivity of the filter. Our systematic evaluation then leads to a simple network architecture, that outperforms state-of-the-art network architectures on a speaker extraction task by 0.22 POLQA score and by 0.32 POLQA score on the CHiME3 data.
    Disentangling representations in Restricted Boltzmann Machines without adversaries. (arXiv:2206.11600v3 [cs.LG] UPDATED)
    A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties. Such disentangled representations make easier to interpret the significant latent factors of variation in the data, as well as to generate new data with desirable features. Methods for disentangling representations often rely on an adversarial scheme, in which representations are tuned to avoid discriminators from being able to reconstruct information about the data properties (labels). Unfortunately adversarial training is generally difficult to implement in practice. Here we propose a simple, effective way of disentangling representations without any need to train adversarial discriminators, and apply our approach to Restricted Boltzmann Machines (RBM), one of the simplest representation-based generative models. Our approach relies on the introduction of adequate constraints on the weights during training, which allows us to concentrate information about labels on a small subset of latent variables. The effectiveness of the approach is illustrated with four examples: the CelebA dataset of facial images, the two-dimensional Ising model, the MNIST dataset of handwritten digits, and the taxonomy of protein families. In addition, we show how our framework allows for analytically computing the cost, in terms of log-likelihood of the data, associated to the disentanglement of their representations.
    Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification. (arXiv:2207.07189v2 [cs.CV] UPDATED)
    We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations, and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository: https://github.com/biasvariancelabs/aitlas-arena
    Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction. (arXiv:2206.07085v3 [cs.LG] UPDATED)
    Normalization layers (e.g., Batch Normalization, Layer Normalization) were introduced to help with optimization difficulties in very deep nets, but they clearly also help generalization, even in not-so-deep nets. Motivated by the long-held belief that flatter minima lead to better generalization, this paper gives mathematical analysis and supporting experiments suggesting that normalization (together with accompanying weight-decay) encourages GD to reduce the sharpness of loss surface. Here "sharpness" is carefully defined given that the loss is scale-invariant, a known consequence of normalization. Specifically, for a fairly broad class of neural nets with normalization, our theory explains how GD with a finite learning rate enters the so-called Edge of Stability (EoS) regime, and characterizes the trajectory of GD in this regime via a continuous sharpness-reduction flow.
    Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. (arXiv:2205.15947v4 [cs.LG] UPDATED)
    We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance. These shifts are defined via parametric changes in the causal mechanisms of observed variables, where constraints on parameters yield a "robustness set" of plausible distributions and a corresponding worst-case loss over the set. While the loss under an individual parametric shift can be estimated via reweighting techniques such as importance sampling, the resulting worst-case optimization problem is non-convex, and the estimate may suffer from large variance. For small shifts, however, we can construct a local second-order approximation to the loss under shift and cast the problem of finding a worst-case shift as a particular non-convex quadratic optimization problem, for which efficient algorithms are available. We demonstrate that this second-order approximation can be estimated directly for shifts in conditional exponential family models, and we bound the approximation error. We apply our approach to a computer vision task (classifying gender from images), revealing sensitivity to shifts in non-causal attributes.
    Counterfactual Fairness with Partially Known Causal Graph. (arXiv:2205.13972v3 [cs.LG] UPDATED)
    Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrimination and bias through causal effects. Though causality-based fair learning is attracting increasing attention, current methods assume the true causal graph is fully known. This paper proposes a general method to achieve the notion of counterfactual fairness when the true causal graph is unknown. To be able to select features that lead to counterfactual fairness, we derive the conditions and algorithms to identify ancestral relations between variables on a \textit{Partially Directed Acyclic Graph (PDAG)}, specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. Interestingly, we find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided: the sensitive attributes do not have ancestors in the causal graph. Results on both simulated and real-world datasets demonstrate the effectiveness of our method.
    Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies. (arXiv:2204.01058v2 [math.PR] UPDATED)
    This article considers fully connected neural networks with Gaussian random weights and biases as well as $L$ hidden layers, each of width proportional to a large parameter $n$. For polynomially bounded non-linearities we give sharp estimates in powers of $1/n$ for the joint cumulants of the network output and its derivatives. Moreover, we show that network cumulants form a perturbatively solvable hierarchy in powers of $1/n$ in that $k$-th order cumulants in one layer have recursions that depend to leading order in $1/n$ only on $j$-th order cumulants at the previous layer with $j\leq k$. By solving a variety of such recursions, however, we find that the depth-to-width ratio $L/n$ plays the role of an effective network depth, controlling both the scale of fluctuations at individual neurons and the size of inter-neuron correlations. Thus, while the cumulant recursions we derive form a hierarchy in powers of $1/n$, contributions of order $1/n^k$ often grow like $L^k$ and are hence non-negligible at positive $L/n$. We use this to study a somewhat simplified version of the exploding and vanishing gradient problem, proving that this particular variant occurs if and only if $L/n$ is large. Several key ideas in this article were first developed at a physics level of rigor in a recent monograph of Daniel A. Roberts, Sho Yaida, and the author. This article not only makes these ideas mathematically precise but also significantly extends them, opening the way to obtaining corrections to all orders in $1/n$.
    Policy Gradients using Variational Quantum Circuits. (arXiv:2203.10591v3 [quant-ph] UPDATED)
    Variational Quantum Circuits are being used as versatile Quantum Machine Learning models. Some empirical results exhibit an advantage in supervised and generative learning tasks. However, when applied to Reinforcement Learning, less is known. In this work, we considered a Variational Quantum Circuit composed of a low-depth hardware-efficient ansatz as the parameterized policy of a Reinforcement Learning agent. We show that an $\epsilon$-approximation of the policy gradient can be obtained using a logarithmic number of samples concerning the total number of parameters. We empirically verify that such quantum models behave similarly or even outperform typical classical neural networks used in standard benchmarking environments and in quantum control, using only a fraction of the parameters. Moreover, we study the Barren Plateau phenomenon in quantum policy gradients using the Fisher Information Matrix spectrum.
    Sharing to learn and learning to share -- Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review. (arXiv:2111.12146v5 [cs.LG] UPDATED)
    Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms such as transfer learning, meta learning, and multi-task learning reflect the human learning process by exploiting the prior knowledge for new tasks, encouraging faster learning and good generalization for new tasks. This article gives a detailed view of these learning paradigms and their comparative analysis. The weakness of one learning algorithm turns out to be a strength of another, and thus merging them is a prevalent trait in the literature. There are numerous research papers that focus on each of these learning paradigms separately and provide a comprehensive overview of them. However, this article provides a review of research studies that combine (two of) these learning algorithms. This survey describes how these techniques are combined to solve problems in many different fields of study, including computer vision, natural language processing, hyperspectral imaging, and many more, in supervised setting only. As a result, the global generic learning network an amalgamation of meta learning, transfer learning, and multi-task learning is introduced here, along with some open research questions and future research directions in the multi-task setting.
    Privatized Graph Federated Learning. (arXiv:2203.07105v2 [cs.LG] UPDATED)
    Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting personal information on the communication links. In this work, we introduce graph federated learning (GFL), which consists of multiple federated units connected by a graph. We then show how graph homomorphic perturbations can be used to ensure the algorithm is differentially private. We conduct both convergence and privacy theoretical analyses and illustrate performance by means of computer simulations.
    Learning Partial Equivariances from Data. (arXiv:2110.10211v3 [cs.CV] UPDATED)
    Group Convolutional Neural Networks (G-CNNs) constrain learned features to respect the symmetries in the selected group, and lead to better generalization when these symmetries appear in the data. If this is not the case, however, equivariance leads to overly constrained models and worse performance. Frequently, transformations occurring in data can be better represented by a subset of a group than by a group as a whole, e.g., rotations in $[-90^{\circ}, 90^{\circ}]$. In such cases, a model that respects equivariance $\textit{partially}$ is better suited to represent the data. In addition, relevant transformations may differ for low and high-level features. For instance, full rotation equivariance is useful to describe edge orientations in a face, but partial rotation equivariance is better suited to describe face poses relative to the camera. In other words, the optimal level of equivariance may differ per layer. In this work, we introduce $\textit{Partial G-CNNs}$: G-CNNs able to learn layer-wise levels of partial and full equivariance to discrete, continuous groups and combinations thereof as part of training. Partial G-CNNs retain full equivariance when beneficial, e.g., for rotated MNIST, but adjust it whenever it becomes harmful, e.g., for classification of 6 / 9 digits or natural images. We empirically show that partial G-CNNs pair G-CNNs when full equivariance is advantageous, and outperform them otherwise.
    Variational Actor-Critic Algorithms. (arXiv:2108.01215v4 [cs.LG] UPDATED)
    We introduce a class of variational actor-critic algorithms based on a variational formulation over both the value function and the policy. The objective function of the variational formulation consists of two parts: one for maximizing the value function and the other for minimizing the Bellman residual. Besides the vanilla gradient descent with both the value function and the policy updates, we propose two variants, the clipping method and the flipping method, in order to speed up the convergence. We also prove that, when the prefactor of the Bellman residual is sufficiently large, the fixed point of the algorithm is close to the optimal policy.
    Smart Choices and the Selection Monad. (arXiv:2007.08926v7 [cs.LO] UPDATED)
    Describing systems in terms of choices and their resulting costs and rewards offers the promise of freeing algorithm designers and programmers from specifying how those choices should be made; in implementations, the choices can be realized by optimization techniques and, increasingly, by machine-learning methods. We study this approach from a programming-language perspective. We define two small languages that support decision-making abstractions: one with choices and rewards, and the other additionally with probabilities. We give both operational and denotational semantics. In the case of the second language we consider three denotational semantics, with varying degrees of correlation between possible program values and expected rewards. The operational semantics combine the usual semantics of standard constructs with optimization over spaces of possible execution strategies. The denotational semantics, which are compositional, rely on the selection monad, to handle choice, augmented with an auxiliary monad to handle other effects, such as rewards or probability. We establish adequacy theorems that the two semantics coincide in all cases. We also prove full abstraction at base types, with varying notions of observation in the probabilistic case corresponding to the various degrees of correlation. We present axioms for choice combined with rewards and probability, establishing completeness at base types for the case of rewards without probability.
    Elastic Similarity and Distance Measures for Multivariate Time Series. (arXiv:2102.10231v2 [cs.LG] UPDATED)
    This paper contributes multivariate versions of seven commonly used elastic similarity and distance measures for time series data analytics. Elastic similarity and distance measures are a class of similarity measures that can compensate for misalignments in the time axis of time series data. We adapt two existing strategies used in a multivariate version of the well-known Dynamic Time Warping (DTW), namely, Independent and Dependent DTW, to these seven measures. While these measures can be applied to various time series analysis tasks, we demonstrate their utility on multivariate time series classification using the nearest neighbor classifier. On 23 well-known datasets, we demonstrate that each of the measures but one achieves the highest accuracy relative to others on at least one dataset, supporting the value of developing a suite of multivariate similarity and distance measures. We also demonstrate that there are datasets for which either the dependent versions of all measures are more accurate than their independent counterparts or vice versa. In addition, we also construct a nearest neighbor-based ensemble of the measures and show that it is competitive to other state-of-the-art single-strategy multivariate time series classifiers.
    SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification. (arXiv:2012.03803v2 [eess.SP] UPDATED)
    A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer electronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to low-resolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG signals by jointly considering the accuracies when applied to the DL-based high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional interpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the enhanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances.
    Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection. (arXiv:2006.14563v2 [cs.CV] UPDATED)
    It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose D3M, to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go. Source code, analysis data, and other details are publicly available at $\href{https://github.com/asvspoof/D3M}{\text{https://github.com/asvspoof/D3M}}$.
    Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy. (arXiv:2301.06035v1 [cs.LG])
    The number of rooftop photovoltaic (PV) systems has significantly increased in recent years around the globe, including in Australia. This trend is anticipated to continue in the next few years. Given their high share of generation in power systems, detecting malfunctions and abnormalities in rooftop PV systems is essential for ensuring their high efficiency and safety. In this paper, we present a novel anomaly detection method for a large number of rooftop PV systems installed in a region using big data and a time series complexity measure called weighted permutation entropy (WPE). This efficient method only uses the historical PV generation data in a given region to identify anomalous PV systems and requires no new sensor or smart device. Using a real-world PV generation dataset, we discuss how the hyperparameters of WPE should be tuned for the purpose. The proposed PV anomaly detection method is then tested on rooftop PV generation data from over 100 South Australian households. The results demonstrate that anomalous systems detected by our method have indeed encountered problems and require a close inspection. The detection and resolution of potential faults would result in better rooftop PV systems, longer lifetimes, and higher returns on investment.
    Hawk: An Industrial-strength Multi-label Document Classifier. (arXiv:2301.06057v1 [cs.CL])
    There are a plethora of methods and algorithms that solve the classical multi-label document classification. However, when it comes to deployment and usage in an industry setting, most, if not all the contemporary approaches fail to address some of the vital aspects or requirements of an ideal solution: i. ability to operate on variable-length texts and rambling documents. ii. catastrophic forgetting problem. iii. modularity when it comes to online learning and updating the model. iv. ability to spotlight relevant text while producing the prediction, i.e. visualizing the predictions. v. ability to operate on imbalanced or skewed datasets. vi. scalability. The paper describes the significance of these problems in detail and proposes a unique neural network architecture that addresses the above problems. The proposed architecture views documents as a sequence of sentences and leverages sentence-level embeddings for input representation. A hydranet-like architecture is designed to have granular control over and improve the modularity, coupled with a weighted loss driving task-specific heads. In particular, two specific mechanisms are compared: Bi-LSTM and Transformer-based. The architecture is benchmarked on some of the popular benchmarking datasets such as Web of Science - 5763, Web of Science - 11967, BBC Sports, and BBC News datasets. The experimental results reveal that the proposed model outperforms the existing methods by a substantial margin. The ablation study includes comparisons of the impact of the attention mechanism and the application of weighted loss functions to train the task-specific heads in the hydranet.
    Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes. (arXiv:2301.05938v1 [cs.CV])
    Deep learning has been shown to be useful to detect breast cancer metastases by analyzing whole slide images of sentinel lymph nodes. However, it requires extensive scanning and analysis of all the lymph nodes slides for each case. Our deep learning study focuses on breast cancer screening with only a small set of image patches from any sentinel lymph node, positive or negative for metastasis, to detect changes in tumor environment and not in the tumor itself. We design a convolutional neural network in the Python language to build a diagnostic model for this purpose. The excellent results from this preliminary study provided a proof of concept for incorporating automated metastatic screen into the digital pathology workflow to augment the pathologists' productivity. Our approach is unique since it provides a very rapid screen rather than an exhaustive search for tumor in all fields of all sentinel lymph nodes.
    Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks. (arXiv:1906.04893v2 [cs.LG] UPDATED)
    Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many applications ranging from robustness certification of classifiers to stability analysis of closed-loop systems with reinforcement learning controllers. Existing methods in the literature for estimating the Lipschitz constant suffer from either lack of accuracy or poor scalability. In this paper, we present a convex optimization framework to compute guaranteed upper bounds on the Lipschitz constant of DNNs both accurately and efficiently. Our main idea is to interpret activation functions as gradients of convex potential functions. Hence, they satisfy certain properties that can be described by quadratic constraints. This particular description allows us to pose the Lipschitz constant estimation problem as a semidefinite program (SDP). The resulting SDP can be adapted to increase either the estimation accuracy (by capturing the interaction between activation functions of different layers) or scalability (by decomposition and parallel implementation). We illustrate the utility of our approach with a variety of experiments on randomly generated networks and on classifiers trained on the MNIST and Iris datasets. In particular, we experimentally demonstrate that our Lipschitz bounds are the most accurate compared to those in the literature. We also study the impact of adversarial training methods on the Lipschitz bounds of the resulting classifiers and show that our bounds can be used to efficiently provide robustness guarantees.
    Compress Then Test: Powerful Kernel Testing in Near-linear Time. (arXiv:2301.05974v1 [stat.ML])
    Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on $n$ sample points. However, existing kernel tests either run in $n^2$ time or sacrifice undue power to improve runtime. To address these shortcomings, we introduce Compress Then Test (CTT), a new framework for high-powered kernel testing based on sample compression. CTT cheaply approximates an expensive test by compressing each $n$ point sample into a small but provably high-fidelity coreset. For standard kernels and subexponential distributions, CTT inherits the statistical behavior of a quadratic-time test -- recovering the same optimal detection boundary -- while running in near-linear time. We couple these advances with cheaper permutation testing, justified by new power analyses; improved time-vs.-quality guarantees for low-rank approximation; and a fast aggregation procedure for identifying especially discriminating kernels. In our experiments with real and simulated data, CTT and its extensions provide 20--200x speed-ups over state-of-the-art approximate MMD tests with no loss of power.
    A data science and machine learning approach to continuous analysis of Shakespeare's plays. (arXiv:2301.06024v1 [cs.CL])
    The availability of quantitative methods that can analyze text has provided new ways of examining literature in a manner that was not available in the pre-information era. Here we apply comprehensive machine learning analysis to the work of William Shakespeare. The analysis shows clear change in style of writing over time, with the most significant changes in the sentence length, frequency of adjectives and adverbs, and the sentiments expressed in the text. Applying machine learning to make a stylometric prediction of the year of the play shows a Pearson correlation of 0.71 between the actual and predicted year, indicating that Shakespeare's writing style as reflected by the quantitative measurements changed over time. Additionally, it shows that the stylometrics of some of the plays is more similar to plays written either before or after the year they were written. For instance, Romeo and Juliet is dated 1596, but is more similar in stylometrics to plays written by Shakespeare after 1600. The source code for the analysis is available for free download.
    Transferring Fairness under Distribution Shifts via Fair Consistency Regularization. (arXiv:2206.12796v3 [cs.LG] UPDATED)
    The increasing reliance on ML models in high-stakes tasks has raised a major concern on fairness violations. Although there has been a surge of work that improves algorithmic fairness, most of them are under the assumption of an identical training and test distribution. In many real-world applications, however, such an assumption is often violated as previously trained fair models are often deployed in a different environment, and the fairness of such models has been observed to collapse. In this paper, we study how to transfer model fairness under distribution shifts, a widespread issue in practice. We conduct a fine-grained analysis of how the fair model is affected under different types of distribution shifts and find that domain shifts are more challenging than subpopulation shifts. Inspired by the success of self-training in transferring accuracy under domain shifts, we derive a sufficient condition for transferring group fairness. Guided by it, we propose a practical algorithm with a fair consistency regularization as the key component. A synthetic dataset benchmark, which covers all types of distribution shifts, is deployed for experimental verification of the theoretical findings. Experiments on synthetic and real datasets including image and tabular data demonstrate that our approach effectively transfers fairness and accuracy under various distribution shifts.
    Evaluating the Spectral Bias of Coordinate Based MLPs. (arXiv:2301.05816v1 [cs.LG])
    In recent years, representations given by fully connected neural networks have shown to represent scenes, objects, and other measurements well in dense low-dimensional settings. For these models, termed coordinate based MLPs, sinusoidal encodings are necessary in allowing for convergence to the high frequency components of the target function. This requirement is a result of their severe spectral bias when using dense, low dimensional coordinate based inputs. Previous work explained this phenomena using Neural Tangent Kernel (NTK) and Fourier analysis. While these methods provide insight towards this large spectral bias and the benefits of positional encoding, the properties of ReLU networks that induce this behavior are not fully determined. Analyzing spectral bias directly through the computations of ReLU networks would expose their limitations in dense settings, while providing a clearer explanation as to how this behavior emerges during the learning process. In this paper, we systematically analyze the spectral bias of a coordinate based MLP through its activation regions and gradient descent dynamics. This allows us to relate the network's expressive capacity to the speed at which gradient descent converges for components of varying frequency, and how the density of the data further restricts the model.
    What Can Transformers Learn In-Context? A Case Study of Simple Function Classes. (arXiv:2208.01066v2 [cs.CL] UPDATED)
    In-context learning refers to the ability of a model to condition on a prompt sequence consisting of in-context examples (input-output pairs corresponding to some task) along with a new query input, and generate the corresponding output. Crucially, in-context learning happens only at inference time without any parameter updates to the model. While large language models such as GPT-3 exhibit some ability to perform in-context learning, it is unclear what the relationship is between tasks on which this succeeds and what is present in the training data. To make progress towards understanding in-context learning, we consider the well-defined problem of training a model to in-context learn a function class (e.g., linear functions): that is, given data derived from some functions in the class, can we train a model to in-context learn "most" functions from this class? We show empirically that standard Transformers can be trained from scratch to perform in-context learning of linear functions -- that is, the trained model is able to learn unseen linear functions from in-context examples with performance comparable to the optimal least squares estimator. In fact, in-context learning is possible even under two forms of distribution shift: (i) between the training data of the model and inference-time prompts, and (ii) between the in-context examples and the query input during inference. We also show that we can train Transformers to in-context learn more complex function classes -- namely sparse linear functions, two-layer neural networks, and decision trees -- with performance that matches or exceeds task-specific learning algorithms. Our code and models are available at https://github.com/dtsip/in-context-learning .
    Reinforcement learning on graphs: A survey. (arXiv:2204.06127v4 [cs.LG] UPDATED)
    Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic design communities in recent years, and there has been some pioneering work employing the research-rich Reinforcement Learning (RL) techniques to address graph data mining tasks. However, these graph mining methods and RL models are dispersed in different research areas, which makes it hard to compare them. In this survey, we provide a comprehensive overview of RL and graph mining methods and generalize these methods to Graph Reinforcement Learning (GRL) as a unified formulation. We further discuss the applications of GRL methods across various domains and summarize the method descriptions, open-source codes, and benchmark datasets of GRL methods. Furthermore, we propose important directions and challenges to be solved in the future. As far as we know, this is the latest work on a comprehensive survey of GRL, this work provides a global view and a learning resource for scholars. In addition, we create an online open-source for both interested scholars who want to enter this rapidly developing domain and experts who would like to compare GRL methods.
    K-Deep Simplex: Deep Manifold Learning via Local Dictionaries. (arXiv:2012.02134v3 [cs.LG] UPDATED)
    We propose K-Deep Simplex (KDS) which, given a set of data points, learns a dictionary comprising synthetic landmarks, along with representation coefficients supported on a simplex. KDS integrates manifold learning and sparse coding/dictionary learning: reconstruction term, as in classical dictionary learning, and a novel local weighted $\ell_1$ penalty that encourages each data point to represent itself as a convex combination of nearby landmarks. We solve the proposed optimization program using alternating minimization and design an efficient, interpretable autoencoder using algorithm enrolling. We theoretically analyze the proposed program by relating the weighted $\ell_1$ penalty in KDS to a weighted $\ell_0$ program. Assuming that the data are generated from a Delaunay triangulation, we prove the equivalence of the weighted $\ell_1$ and weighted $\ell_0$ programs. If the representation coefficients are given, we prove that the resulting dictionary is unique. Further, we show that low-dimensional representations can be efficiently obtained from the covariance of the coefficient matrix. We apply KDS to the unsupervised clustering problem and prove theoretical performance guarantees. Experiments show that the algorithm is highly efficient and performs competitively on synthetic and real data sets.
    Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context. (arXiv:2301.05804v1 [cs.CV])
    Detecting road traffic signs and accurately determining how they can affect the driver's future actions is a critical task for safe autonomous driving systems. However, various traffic signs in a driving scene have an unequal impact on the driver's decisions, making detecting the salient traffic signs a more important task. Our research addresses this issue, constructing a traffic sign detection model which emphasizes performance on salient signs, or signs that influence the decisions of a driver. We define a traffic sign salience property and use it to construct the LAVA Salient Signs Dataset, the first traffic sign dataset that includes an annotated salience property. Next, we use a custom salience loss function, Salience-Sensitive Focal Loss, to train a Deformable DETR object detection model in order to emphasize stronger performance on salient signs. Results show that a model trained with Salience-Sensitive Focal Loss outperforms a model trained without, with regards to recall of both salient signs and all signs combined. Further, the performance margin on salient signs compared to all signs is largest for the model trained with Salience-Sensitive Focal Loss.  ( 2 min )
    MLOps: A Primer for Policymakers on a New Frontier in Machine Learning. (arXiv:2301.05775v1 [cs.LG])
    This chapter is written with the Data Scientist or MLOps professional in mind but can be used as a resource for policy makers, reformists, AI Ethicists, sociologists, and others interested in finding methods that help reduce bias in algorithms. I will take a deployment centered approach with the assumption that the professionals reading this work have already read the amazing work on the implications of algorithms on historically marginalized groups by Gebru, Buolamwini, Benjamin and Shane to name a few. If you have not read those works, I refer you to the "Important Reading for Ethical Model Building" list at the end of this paper as it will help give you a framework on how to think about Machine Learning models more holistically taking into account their effect on marginalized people. In the Introduction to this chapter, I root the significance of their work in real world examples of what happens when models are deployed without transparent data collected for the training process and are deployed without the practitioners paying special attention to what happens to models that adapt to exploit gaps between their training environment and the real world. The rest of this chapter builds on the work of the aforementioned researchers and discusses the reality of models performing post production and details ways ML practitioners can identify bias using tools during the MLOps lifecycle to mitigate bias that may be introduced to models in the real world.  ( 2 min )
    ML Approach for Power Consumption Prediction in Virtualized Base Stations. (arXiv:2301.05764v1 [cs.LG])
    The flexibility introduced with the Open Radio Access Network (O-RAN) architecture allows us to think beyond static configurations in all parts of the network. This paper addresses the issue related to predicting the power consumption of different radio schedulers, and the potential offered by O-RAN to collect data, train models, and deploy policies to control the power consumption. We propose a black-box (Neural Network) model to learn the power consumption function. We compare our approach with a known hand-crafted solution based on domain knowledge. Our solution reaches similar performance without any previous knowledge of the application and provides more flexibility in scenarios where the system behavior is not well understood or the domain knowledge is not available.  ( 2 min )
    FedSSC: Shared Supervised-Contrastive Federated Learning. (arXiv:2301.05797v1 [cs.LG])
    Federated learning is widely used to perform decentralized training of a global model on multiple devices while preserving the data privacy of each device. However, it suffers from heterogeneous local data on each training device which increases the difficulty to reach the same level of accuracy as the centralized training. Supervised Contrastive Learning which outperform cross-entropy tries to minimizes the difference between feature space of points belongs to the same class and pushes away points from different classes. We propose Supervised Contrastive Federated Learning in which devices can share the learned class-wise feature spaces with each other and add the supervised-contrastive learning loss as a regularization term to foster the feature space learning. The loss tries to minimize the cosine similarity distance between the feature map and the averaged feature map from another device in the same class and maximizes the distance between the feature map and that in a different class. This new regularization term when added on top of the moon regularization term is found to outperform the other state-of-the-art regularization terms in solving the heterogeneous data distribution problem.  ( 2 min )
    A domain-decomposed VAE method for Bayesian inverse problems. (arXiv:2301.05708v1 [stat.ML])
    Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs). This is typically caused by expensive forward model evaluations and high-dimensional parameterization of priors. This paper proposes a domain-decomposed variational auto-encoder Markov chain Monte Carlo (DD-VAE-MCMC) method to tackle these challenges simultaneously. Through partitioning the global physical domain into small subdomains, the proposed method first constructs local deterministic generative models based on local historical data, which provide efficient local prior representations. Gaussian process models with active learning address the domain decomposition interface conditions. Then inversions are conducted on each subdomain independently in parallel and in low-dimensional latent parameter spaces. The local inference solutions are post-processed through the Poisson image blending procedure to result in an efficient global inference result. Numerical examples are provided to demonstrate the performance of the proposed method.  ( 2 min )
    Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation. (arXiv:2301.05739v1 [cs.LG])
    The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are three-fold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws of the vehicle engine into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.  ( 2 min )
    Diatom-inspired architected materials using language-based deep learning: Perception, transformation and manufacturing. (arXiv:2301.05875v1 [cond-mat.mtrl-sci])
    Learning from nature has been a quest of humanity for millennia. While this has taken the form of humans assessing natural designs such as bones, butterfly wings, or spider webs, we can now achieve generating designs using advanced computational algorithms. In this paper we report novel biologically inspired designs of diatom structures, enabled using transformer neural networks, using natural language models to learn, process and transfer insights across manifestations. We illustrate a series of novel diatom-based designs and also report a manufactured specimen, created using additive manufacturing. The method applied here could be expanded to focus on other biological design cues, implement a systematic optimization to meet certain design targets, and include a hybrid set of material design sets.  ( 2 min )
    Artificial Benchmark for Community Detection with Outliers (ABCD+o). (arXiv:2301.05749v1 [cs.SI])
    The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR one, and its main parameter $\xi$ can be tuned to mimic its counterpart in the LFR model, the mixing parameter $\mu$. In this paper, we extend the ABCD model to include potential outliers. We perform some exploratory experiments on both the new ABCD+o model as well as a real-world network to show that outliers possess some desired, distinguishable properties.  ( 2 min )
    Efficient Activation Function Optimization through Surrogate Modeling. (arXiv:2301.05785v1 [cs.LG])
    Carefully designed activation functions can improve the performance of neural networks in many machine learning tasks. However, it is difficult for humans to construct optimal activation functions, and current activation function search algorithms are prohibitively expensive. This paper aims to improve the state of the art through three steps: First, the benchmark datasets Act-Bench-CNN, Act-Bench-ResNet, and Act-Bench-ViT were created by training convolutional, residual, and vision transformer architectures from scratch with 2,913 systematically generated activation functions. Second, a characterization of the benchmark space was developed, leading to a new surrogate-based method for optimization. More specifically, the spectrum of the Fisher information matrix associated with the model's predictive distribution at initialization and the activation function's output distribution were found to be highly predictive of performance. Third, the surrogate was used to discover improved activation functions in CIFAR-100 and ImageNet tasks. Each of these steps is a contribution in its own right; together they serve as a practical and theoretical foundation for further research on activation function optimization. Code is available at https://github.com/cognizant-ai-labs/aquasurf, and the benchmark datasets are at https://github.com/cognizant-ai-labs/act-bench.  ( 2 min )
    Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities. (arXiv:2301.05753v1 [cs.CY])
    As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions made by these systems has proliferated. However, various research communities have independently conceptualized these harms, envisioned potential applications, and proposed interventions. The result is a somewhat fractured landscape of literature focused generally on ensuring decision-making algorithms "do the right thing". In this paper, we compare and discuss work across two major subsets of this literature: algorithmic fairness, which focuses primarily on predictive systems, and ethical decision making, which focuses primarily on sequential decision making and planning. We explore how each of these settings has articulated its normative concerns, the viability of different techniques for these different settings, and how ideas from each setting may have utility for the other.  ( 2 min )
  • Open

    Joint Entropy Search for Maximally-Informed Bayesian Optimization. (arXiv:2206.04771v5 [cs.LG] UPDATED)
    Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Search and Predictive Entropy Search both consider the entropy over the optimum in the input space, while the recent Max-value Entropy Search considers the entropy over the optimal value in the output space. We propose Joint Entropy Search (JES), a novel information-theoretic acquisition function that considers an entirely new quantity, namely the entropy over the joint optimal probability density over both input and output space. To incorporate this information, we consider the reduction in entropy from conditioning on fantasized optimal input/output pairs. The resulting approach primarily relies on standard GP machinery and removes complex approximations typically associated with information-theoretic methods. With minimal computational overhead, JES shows superior decision-making, and yields state-of-the-art performance for information-theoretic approaches across a wide suite of tasks. As a light-weight approach with superior results, JES provides a new go-to acquisition function for Bayesian optimization.  ( 2 min )
    Unbalanced Optimal Transport, from Theory to Numerics. (arXiv:2211.08775v2 [stat.ML] UPDATED)
    Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need for a large number of samples in high dimension and the difficulty to handle data in distinct spaces. In this review, we detail several recently proposed approaches to mitigate these issues. We insist in particular on unbalanced OT, which compares arbitrary positive measures, not restricted to probability distributions (i.e. their total mass can vary). This generalization of OT makes it robust to outliers and missing data. The second workhorse of modern computational OT is entropic regularization, which leads to scalable algorithms while lowering the sample complexity in high dimension. The last point presented in this review is the Gromov-Wasserstein (GW) distance, which extends OT to cope with distributions belonging to different metric spaces. The main motivation for this review is to explain how unbalanced OT, entropic regularization and GW can work hand-in-hand to turn OT into efficient geometric loss functions for data sciences.  ( 2 min )
    Primal Dual Alternating Proximal Gradient Algorithms for Nonsmooth Nonconvex Minimax Problems with Coupled Linear Constraints. (arXiv:2212.04672v2 [math.OC] UPDATED)
    Nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose a primal dual alternating proximal gradient (PDAPG) algorithm and a primal dual proximal gradient (PDPG-L) algorithm for solving nonsmooth nonconvex-(strongly) concave and nonconvex-linear minimax problems with coupled linear constraints, respectively. The iteration complexity of the two algorithms are proved to be $\mathcal{O}\left( \varepsilon ^{-2} \right)$ (resp. $\mathcal{O}\left( \varepsilon ^{-4} \right)$) under nonconvex-strongly concave (resp. nonconvex-concave) setting and $\mathcal{O}\left( \varepsilon ^{-3} \right)$ under nonconvex-linear setting to reach an $\varepsilon$-stationary point, respectively. To our knowledge, they are the first two algorithms with iteration complexity guarantee for solving the nonconvex minimax problems with coupled linear constraints.  ( 2 min )
    Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data. (arXiv:2210.08642v2 [cs.LG] UPDATED)
    Offline reinforcement learning (RL) can be used to improve future performance by leveraging historical data. There exist many different algorithms for offline RL, and it is well recognized that these algorithms, and their hyperparameter settings, can lead to decision policies with substantially differing performance. This prompts the need for pipelines that allow practitioners to systematically perform algorithm-hyperparameter selection for their setting. Critically, in most real-world settings, this pipeline must only involve the use of historical data. Inspired by statistical model selection methods for supervised learning, we introduce a task- and method-agnostic pipeline for automatically training, comparing, selecting, and deploying the best policy when the provided dataset is limited in size. In particular, our work highlights the importance of performing multiple data splits to produce more reliable algorithm-hyperparameter selection. While this is a common approach in supervised learning, to our knowledge, this has not been discussed in detail in the offline RL setting. We show it can have substantial impacts when the dataset is small. Compared to alternate approaches, our proposed pipeline outputs higher-performing deployed policies from a broad range of offline policy learning algorithms and across various simulation domains in healthcare, education, and robotics. This work contributes toward the development of a general-purpose meta-algorithm for automatic algorithm-hyperparameter selection for offline RL.  ( 2 min )
    Linear Convergence of ISTA and FISTA. (arXiv:2212.06319v2 [math.OC] UPDATED)
    In this paper, we revisit the class of iterative shrinkage-thresholding algorithms (ISTA) for solving the linear inverse problem with sparse representation, which arises in signal and image processing. It is shown in the numerical experiment to deblur an image that the convergence behavior in the logarithmic-scale ordinate tends to be linear instead of logarithmic, approximating to be flat. Making meticulous observations, we find that the previous assumption for the smooth part to be convex weakens the least-square model. Specifically, assuming the smooth part to be strongly convex is more reasonable for the least-square model, even though the image matrix is probably ill-conditioned. Furthermore, we improve the pivotal inequality tighter for composite optimization with the smooth part to be strongly convex instead of general convex, which is first found in [Li et al., 2022]. Based on this pivotal inequality, we generalize the linear convergence to composite optimization in both the objective value and the squared proximal subgradient norm. Meanwhile, we set a simple ill-conditioned matrix which is easy to compute the singular values instead of the original blur matrix. The new numerical experiment shows the proximal generalization of Nesterov's accelerated gradient descent (NAG) for the strongly convex function has a faster linear convergence rate than ISTA. Based on the tighter pivotal inequality, we also generalize the faster linear convergence rate to composite optimization, in both the objective value and the squared proximal subgradient norm, by taking advantage of the well-constructed Lyapunov function with a slight modification and the phase-space representation based on the high-resolution differential equation framework from the implicit-velocity scheme.  ( 2 min )
    Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. (arXiv:2209.00735v2 [cs.LG] UPDATED)
    Neural networks (NNs) struggle to efficiently solve certain problems, such as learning parities, even when there are simple learning algorithms for those problems. Can NNs discover learning algorithms on their own? We exhibit a NN architecture that, in polynomial time, learns as well as any efficient learning algorithm describable by a constant-sized program. For example, on parity problems, the NN learns as well as Gaussian elimination, an efficient algorithm that can be succinctly described. Our architecture combines both recurrent weight sharing between layers and convolutional weight sharing to reduce the number of parameters down to a constant, even though the network itself may have trillions of nodes. While in practice the constants in our analysis are too large to be directly meaningful, our work suggests that the synergy of Recurrent and Convolutional NNs (RCNNs) may be more natural and powerful than either alone, particularly for concisely parameterizing discrete algorithms.  ( 2 min )
    Failure-informed adaptive sampling for PINNs. (arXiv:2210.00279v3 [math.NA] UPDATED)
    Physics-informed neural networks (PINNs) have emerged as an effective technique for solving PDEs in a wide range of domains. It is noticed, however, the performance of PINNs can vary dramatically with different sampling procedures. For instance, a fixed set of (prior chosen) training points may fail to capture the effective solution region (especially for problems with singularities). To overcome this issue, we present in this work an adaptive strategy, termed the failure-informed PINNs (FI-PINNs), which is inspired by the viewpoint of reliability analysis. The key idea is to define an effective failure probability based on the residual, and then, with the aim of placing more samples in the failure region, the FI-PINNs employs a failure-informed enrichment technique to adaptively add new collocation points to the training set, such that the numerical accuracy is dramatically improved. In short, similar as adaptive finite element methods, the proposed FI-PINNs adopts the failure probability as the posterior error indicator to generate new training points. We prove rigorous error bounds of FI-PINNs and illustrate its performance through several problems.  ( 2 min )
    Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time Signals. (arXiv:2102.02876v3 [stat.ML] UPDATED)
    We study the classical problem of recovering a multidimensional source signal from observations of nonlinear mixtures of this signal. We show that this recovery is possible (up to a permutation and monotone scaling of the source's original component signals) if the mixture is due to a sufficiently differentiable and invertible but otherwise arbitrarily nonlinear function and the component signals of the source are statistically independent with 'non-degenerate' second-order statistics. The latter assumption requires the source signal to meet one of three regularity conditions which essentially ensure that the source is sufficiently far away from the non-recoverable extremes of being deterministic or constant in time. These assumptions, which cover many popular time series models and stochastic processes, allow us to reformulate the initial problem of nonlinear blind source separation as a simple-to-state problem of optimisation-based function approximation. We propose to solve this approximation problem by minimizing a novel type of objective function that efficiently quantifies the mutual statistical dependence between multiple stochastic processes via cumulant-like statistics. This yields a scalable and direct new method for nonlinear Independent Component Analysis with widely applicable theoretical guarantees and for which our experiments indicate good performance.  ( 2 min )
    Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. (arXiv:2106.13624v2 [cs.LG] UPDATED)
    We present a new second-order oracle bound for the expected risk of a weighted majority vote. The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a.k.a. one-sided Chebyshev's), which is amenable to efficient minimization. The new form resolves the optimization challenge faced by prior oracle bounds based on the Chebyshev-Cantelli inequality, the C-bounds [Germain et al., 2015], and, at the same time, it improves on the oracle bound based on second order Markov's inequality introduced by Masegosa et al. [2020]. We also derive a new concentration of measure inequality, which we name PAC-Bayes-Bennett, since it combines PAC-Bayesian bounding with Bennett's inequality. We use it for empirical estimation of the oracle bound. The PAC-Bayes-Bennett inequality improves on the PAC-Bayes-Bernstein inequality of Seldin et al. [2012]. We provide an empirical evaluation demonstrating that the new bounds can improve on the work of Masegosa et al. [2020]. Both the parametric form of the Chebyshev-Cantelli inequality and the PAC-Bayes-Bennett inequality may be of independent interest for the study of concentration of measure in other domains.  ( 2 min )
    Universal Prediction Band via Semi-Definite Programming. (arXiv:2103.17203v3 [stat.ML] UPDATED)
    We propose a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification, with or without any user-specified predictive model. Our approach provides an alternative to the now-standard conformal prediction for uncertainty quantification, with novel theoretical insights and computational advantages. The data-adaptive prediction band is universally applicable with minimal distributional assumptions, has strong non-asymptotic coverage properties, and is easy to implement using standard convex programs. Our approach can be viewed as a novel variance interpolation with confidence and further leverages techniques from semi-definite programming and sum-of-squares optimization. Theoretical and numerical performances for the proposed approach for uncertainty quantification are analyzed.  ( 2 min )
    Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection. (arXiv:2006.14563v2 [cs.CV] UPDATED)
    It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose D3M, to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go. Source code, analysis data, and other details are publicly available at $\href{https://github.com/asvspoof/D3M}{\text{https://github.com/asvspoof/D3M}}$.  ( 3 min )
    Elastic Similarity and Distance Measures for Multivariate Time Series. (arXiv:2102.10231v2 [cs.LG] UPDATED)
    This paper contributes multivariate versions of seven commonly used elastic similarity and distance measures for time series data analytics. Elastic similarity and distance measures are a class of similarity measures that can compensate for misalignments in the time axis of time series data. We adapt two existing strategies used in a multivariate version of the well-known Dynamic Time Warping (DTW), namely, Independent and Dependent DTW, to these seven measures. While these measures can be applied to various time series analysis tasks, we demonstrate their utility on multivariate time series classification using the nearest neighbor classifier. On 23 well-known datasets, we demonstrate that each of the measures but one achieves the highest accuracy relative to others on at least one dataset, supporting the value of developing a suite of multivariate similarity and distance measures. We also demonstrate that there are datasets for which either the dependent versions of all measures are more accurate than their independent counterparts or vice versa. In addition, we also construct a nearest neighbor-based ensemble of the measures and show that it is competitive to other state-of-the-art single-strategy multivariate time series classifiers.  ( 2 min )
    Learning Probabilistic Models from Generator Latent Spaces with Hat EBM. (arXiv:2210.16486v2 [cs.CV] UPDATED)
    This work proposes a method for using any generator network as the foundation of an Energy-Based Model (EBM). Our formulation posits that observed images are the sum of unobserved latent variables passed through the generator network and a residual random variable that spans the gap between the generator output and the image manifold. One can then define an EBM that includes the generator as part of its forward pass, which we call the Hat EBM. The model can be trained without inferring the latent variables of the observed data or calculating the generator Jacobian determinant. This enables explicit probabilistic modeling of the output distribution of any type of generator network. Experiments show strong performance of the proposed method on (1) unconditional ImageNet synthesis at 128x128 resolution, (2) refining the output of existing generators, and (3) learning EBMs that incorporate non-probabilistic generators. Code and pretrained models to reproduce our results are available at https://github.com/point0bar1/hat-ebm.  ( 2 min )
    Improved Algorithms for Neural Active Learning. (arXiv:2210.00423v3 [cs.LG] UPDATED)
    We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work. Then, the proposed algorithm leverages the powerful representation of NNs for both exploitation and exploration, has the query decision-maker tailored for $k$-class classification problems with the performance guarantee, utilizes the full feedback, and updates parameters in a more practical and efficient manner. These careful designs lead to an instance-dependent regret upper bound, roughly improving by a multiplicative factor $O(\log T)$ and removing the curse of input dimensionality. Furthermore, we show that the algorithm can achieve the same performance as the Bayes-optimal classifier in the long run under the hard-margin setting in classification problems. In the end, we use extensive experiments to evaluate the proposed algorithm and SOTA baselines, to show the improved empirical performance.  ( 2 min )
    Generic Error Bounds for the Generalized Lasso with Sub-Exponential Data. (arXiv:2004.05361v3 [math.ST] UPDATED)
    This work performs a non-asymptotic analysis of the generalized Lasso under the assumption of sub-exponential data. Our main results continue recent research on the benchmark case of (sub-)Gaussian sample distributions and thereby explore what conclusions are still valid when going beyond. While many statistical features remain unaffected (e.g., consistency and error decay rates), the key difference becomes manifested in how the complexity of the hypothesis set is measured. It turns out that the estimation error can be controlled by means of two complexity parameters that arise naturally from a generic-chaining-based proof strategy. The output model can be non-realizable, while the only requirement for the input vector is a generic concentration inequality of Bernstein-type, which can be implemented for a variety of sub-exponential distributions. This abstract approach allows us to reproduce, unify, and extend previously known guarantees for the generalized Lasso. In particular, we present applications to semi-parametric output models and phase retrieval via the lifted Lasso. Moreover, our findings are discussed in the context of sparse recovery and high-dimensional estimation problems.  ( 2 min )
    Black-box Coreset Variational Inference. (arXiv:2211.02377v2 [stat.ML] UPDATED)
    Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly accelerating subsequent downstream tasks. Existing variational coreset constructions rely on either selecting subsets of the observed datapoints, or jointly performing approximate inference and optimizing pseudodata in the observed space akin to inducing points methods in Gaussian Processes. So far, both approaches are limited by complexities in evaluating their objectives for general purpose models, and require generating samples from a typically intractable posterior over the coreset throughout inference and testing. In this work, we present a black-box variational inference framework for coresets that overcomes these constraints and enables principled application of variational coresets to intractable models, such as Bayesian neural networks. We apply our techniques to supervised learning problems, and compare them with existing approaches in the literature for data summarization and inference.  ( 2 min )
    On the Exactness of Dantzig-Wolfe Relaxation for Rank Constrained Optimization Problems. (arXiv:2210.16191v2 [math.OC] UPDATED)
    In the rank-constrained optimization problem (RCOP), it minimizes a linear objective function over a prespecified closed rank-constrained domain set and $m$ generic two-sided linear matrix inequalities. Motivated by the Dantzig-Wolfe (DW) decomposition, a popular approach of solving many nonconvex optimization problems, we investigate the strength of DW relaxation (DWR) of the RCOP, which admits the same formulation as RCOP except replacing the domain set by its closed convex hull. Notably, our goal is to characterize conditions under which the DWR matches RCOP for any m two-sided linear matrix inequalities. From the primal perspective, we develop the first-known simultaneously necessary and sufficient conditions that achieve: (i) extreme point exactness -- all the extreme points of the DWR feasible set belong to that of the RCOP; (ii) convex hull exactness -- the DWR feasible set is identical to the closed convex hull of RCOP feasible set; and (iii) objective exactness -- the optimal values of the DWR and RCOP coincide. The proposed conditions unify, refine, and extend the existing exactness results in the quadratically constrained quadratic program (QCQP) and fair unsupervised learning. These conditions can be very useful to identify new results, including the extreme point exactness for a QCQP problem that admits an inhomogeneous objective function with two homogeneous two-sided quadratic constraints and the convex hull exactness for fair SVD.  ( 2 min )
    Outlier Robust and Sparse Estimation of Linear Regression Coefficients. (arXiv:2208.11592v2 [math.ST] UPDATED)
    We consider outlier-robust and sparse estimation of linear regression coefficients, when covariate vectors and noises are sampled, respectively, from an $\mathfrak{L}$-subGaussian distribution and a heavy-tailed distribution. Additionally, the covariate vectors and noises are contaminated by adversarial outliers. We deal with two cases: the covariance matrix of the covariates is known or unknown. Particularly, in the known case, our estimator can attain a nearly information theoretical optimal error bound, and our error bound is sharper than those of earlier studies dealing with similar situations. Our estimator analysis relies heavily on generic chaining to derive sharp error bounds.  ( 2 min )
    Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. (arXiv:2209.03695v3 [cs.LG] UPDATED)
    A fundamental property of deep learning normalization techniques, such as batch normalization, is making the pre-normalization parameters scale invariant. The intrinsic domain of such parameters is the unit sphere, and therefore their gradient optimization dynamics can be represented via spherical optimization with varying effective learning rate (ELR), which was studied previously. However, the varying ELR may obscure certain characteristics of the intrinsic loss landscape structure. In this work, we investigate the properties of training scale-invariant neural networks directly on the sphere using a fixed ELR. We discover three regimes of such training depending on the ELR value: convergence, chaotic equilibrium, and divergence. We study these regimes in detail both on a theoretical examination of a toy example and on a thorough empirical analysis of real scale-invariant deep learning models. Each regime has unique features and reflects specific properties of the intrinsic loss landscape, some of which have strong parallels with previous research on both regular and scale-invariant neural networks training. Finally, we demonstrate how the discovered regimes are reflected in conventional training of normalized networks and how they can be leveraged to achieve better optima.  ( 2 min )
    Relay Variational Inference: A Method for Accelerated Encoderless VI. (arXiv:2110.13422v2 [cs.LG] UPDATED)
    Variational Inference (VI) offers a method for approximating intractable likelihoods. In neural VI, inference of approximate posteriors is commonly done using an encoder. Alternatively, encoderless VI offers a framework for learning generative models from data without encountering suboptimalities caused by amortization via an encoder (e.g. in presence of missing or uncertain data). However, in absence of an encoder, such methods often suffer in convergence due to the slow nature of gradient steps required to learn the approximate posterior parameters. In this paper, we introduce Relay VI (RVI), a framework that dramatically improves both the convergence and performance of encoderless VI. In our experiments over multiple datasets, we study the effectiveness of RVI in terms of convergence speed, loss, representation power and missing data imputation. We find RVI to be a unique tool, often superior in both performance and convergence speed to previously proposed encoderless as well as amortized VI models (e.g. VAE).  ( 2 min )
    Spectrum of non-Hermitian deep-Hebbian neural networks. (arXiv:2208.11411v2 [q-bio.NC] UPDATED)
    Neural networks with recurrent asymmetric couplings are important to understand how episodic memories are encoded in the brain. Here, we integrate the experimental observation of wide synaptic integration window into our model of sequence retrieval in the continuous time dynamics. The model with non-normal neuron-interactions is theoretically studied by deriving a random matrix theory of the Jacobian matrix in neural dynamics. The spectra bears several distinct features, such as breaking rotational symmetry about the origin, and the emergence of nested voids within the spectrum boundary. The spectral density is thus highly non-uniformly distributed in the complex plane. The random matrix theory also predicts a transition to chaos. In particular, the edge of chaos provides computational benefits for the sequential retrieval of memories. Our work provides a systematic study of time-lagged correlations with arbitrary time delays, and thus can inspire future studies of a broad class of memory models, and even big data analysis of biological time series.  ( 2 min )
    Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies. (arXiv:2204.01058v2 [math.PR] UPDATED)
    This article considers fully connected neural networks with Gaussian random weights and biases as well as $L$ hidden layers, each of width proportional to a large parameter $n$. For polynomially bounded non-linearities we give sharp estimates in powers of $1/n$ for the joint cumulants of the network output and its derivatives. Moreover, we show that network cumulants form a perturbatively solvable hierarchy in powers of $1/n$ in that $k$-th order cumulants in one layer have recursions that depend to leading order in $1/n$ only on $j$-th order cumulants at the previous layer with $j\leq k$. By solving a variety of such recursions, however, we find that the depth-to-width ratio $L/n$ plays the role of an effective network depth, controlling both the scale of fluctuations at individual neurons and the size of inter-neuron correlations. Thus, while the cumulant recursions we derive form a hierarchy in powers of $1/n$, contributions of order $1/n^k$ often grow like $L^k$ and are hence non-negligible at positive $L/n$. We use this to study a somewhat simplified version of the exploding and vanishing gradient problem, proving that this particular variant occurs if and only if $L/n$ is large. Several key ideas in this article were first developed at a physics level of rigor in a recent monograph of Daniel A. Roberts, Sho Yaida, and the author. This article not only makes these ideas mathematically precise but also significantly extends them, opening the way to obtaining corrections to all orders in $1/n$.  ( 2 min )
    Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit. (arXiv:2207.08799v3 [cs.LG] UPDATED)
    There is mounting evidence of emergent phenomena in the capabilities of deep learning methods as we scale up datasets, model sizes, and training times. While there are some accounts of how these resources modulate statistical capacity, far less is known about their effect on the computational problem of model training. This work conducts such an exploration through the lens of learning a $k$-sparse parity of $n$ bits, a canonical discrete search problem which is statistically easy but computationally hard. Empirically, we find that a variety of neural networks successfully learn sparse parities, with discontinuous phase transitions in the training curves. On small instances, learning abruptly occurs at approximately $n^{O(k)}$ iterations; this nearly matches SQ lower bounds, despite the apparent lack of a sparse prior. Our theoretical analysis shows that these observations are not explained by a Langevin-like mechanism, whereby SGD "stumbles in the dark" until it finds the hidden set of features (a natural algorithm which also runs in $n^{O(k)}$ time). Instead, we show that SGD gradually amplifies the sparse solution via a Fourier gap in the population gradient, making continual progress that is invisible to loss and error metrics.  ( 2 min )
    Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. (arXiv:2205.15947v4 [cs.LG] UPDATED)
    We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance. These shifts are defined via parametric changes in the causal mechanisms of observed variables, where constraints on parameters yield a "robustness set" of plausible distributions and a corresponding worst-case loss over the set. While the loss under an individual parametric shift can be estimated via reweighting techniques such as importance sampling, the resulting worst-case optimization problem is non-convex, and the estimate may suffer from large variance. For small shifts, however, we can construct a local second-order approximation to the loss under shift and cast the problem of finding a worst-case shift as a particular non-convex quadratic optimization problem, for which efficient algorithms are available. We demonstrate that this second-order approximation can be estimated directly for shifts in conditional exponential family models, and we bound the approximation error. We apply our approach to a computer vision task (classifying gender from images), revealing sensitivity to shifts in non-causal attributes.  ( 2 min )
    When saliency goes off on a tangent: Interpreting Deep Neural Networks with nonlinear saliency maps. (arXiv:2110.06639v3 [cs.LG] UPDATED)
    A fundamental bottleneck in utilising complex machine learning systems for critical applications has been not knowing why they do and what they do, thus preventing the development of any crucial safety protocols. To date, no method exist that can provide full insight into the granularity of the neural network's decision process. In the past, saliency maps were an early attempt at resolving this problem through sensitivity calculations, whereby dimensions of a data point are selected based on how sensitive the output of the system is to them. However, the success of saliency maps has been at best limited, mainly due to the fact that they interpret the underlying learning system through a linear approximation. We present a novel class of methods for generating nonlinear saliency maps which fully account for the nonlinearity of the underlying learning system. While agreeing with linear saliency maps on simple problems where linear saliency maps are correct, they clearly identify more specific drivers of classification on complex examples where nonlinearities are more pronounced. This new class of methods significantly aids interpretability of deep neural networks and related machine learning systems. Crucially, they provide a starting point for their more broad use in serious applications, where 'why' is equally important as 'what'.  ( 2 min )
    AutoML Two-Sample Test. (arXiv:2206.08843v3 [cs.LG] UPDATED)
    Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of many sophisticated test procedures going beyond the standard supervised learning frameworks, whose usage can require specialized knowledge about two-sample testing. We use a simple test that takes the mean discrepancy of a witness function as the test statistic and prove that minimizing a squared loss leads to a witness with optimal testing power. This allows us to leverage recent advancements in AutoML. Without any user input about the problems at hand, and using the same method for all our experiments, our AutoML two-sample test achieves competitive performance on a diverse distribution shift benchmark as well as on challenging two-sample testing problems. We provide an implementation of the AutoML two-sample test in the Python package autotst.  ( 2 min )
    The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization. (arXiv:2205.14546v2 [cs.LG] UPDATED)
    Machine learning models often generalize poorly to out-of-distribution (OOD) data as a result of relying on features that are spuriously correlated with the label during training. Recently, the technique of Invariant Risk Minimization (IRM) was proposed to learn predictors that only use invariant features by conserving the feature-conditioned label expectation $\mathbb{E}_e[y|f(x)]$ across environments. However, more recent studies have demonstrated that IRM-v1, a practical version of IRM, can fail in various settings. Here, we identify a fundamental flaw of IRM formulation that causes the failure. We then introduce a complementary notion of invariance, MRI, based on conserving the label-conditioned feature expectation $\mathbb{E}_e[f(x)|y]$, which is free of this flaw. Further, we introduce a simplified, practical version of the MRI formulation called MRI-v1. We prove that for general linear problems, MRI-v1 guarantees invariant predictors given sufficient number of environments. We also empirically demonstrate that MRI-v1 strongly out-performs IRM-v1 and consistently achieves near-optimal OOD generalization in image-based nonlinear problems.  ( 2 min )
    RenyiCL: Contrastive Representation Learning with Skew Renyi Divergence. (arXiv:2208.06270v2 [stat.ML] UPDATED)
    Contrastive representation learning seeks to acquire useful representations by estimating the shared information between multiple views of data. Here, the choice of data augmentation is sensitive to the quality of learned representations: as harder the data augmentations are applied, the views share more task-relevant information, but also task-irrelevant one that can hinder the generalization capability of representation. Motivated by this, we present a new robust contrastive learning scheme, coined R\'enyiCL, which can effectively manage harder augmentations by utilizing R\'enyi divergence. Our method is built upon the variational lower bound of R\'enyi divergence, but a na\"ive usage of a variational method is impractical due to the large variance. To tackle this challenge, we propose a novel contrastive objective that conducts variational estimation of a skew R\'enyi divergence and provide a theoretical guarantee on how variational estimation of skew divergence leads to stable training. We show that R\'enyi contrastive learning objectives perform innate hard negative sampling and easy positive sampling simultaneously so that it can selectively learn useful features and ignore nuisance features. Through experiments on ImageNet, we show that R\'enyi contrastive learning with stronger augmentations outperforms other self-supervised methods without extra regularization or computational overhead. Moreover, we also validate our method on other domains such as graph and tabular, showing empirical gain over other contrastive methods.  ( 2 min )
    The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning. (arXiv:2205.06226v3 [cs.LG] UPDATED)
    Recently the surprising discovery of the Bootstrap Your Own Latent (BYOL) method by Grill et al. shows the negative term in contrastive loss can be removed if we add the so-called prediction head to the network. This initiated the research of non-contrastive self-supervised learning. It is mysterious why even when there exist trivial collapsed global optimal solutions, neural networks trained by (stochastic) gradient descent can still learn competitive representations. This phenomenon is a typical example of implicit bias in deep learning and remains little understood. In this work, we present our empirical and theoretical discoveries on non-contrastive self-supervised learning. Empirically, we find that when the prediction head is initialized as an identity matrix with only its off-diagonal entries being trainable, the network can learn competitive representations even though the trivial optima still exist in the training objective. Theoretically, we present a framework to understand the behavior of the trainable, but identity-initialized prediction head. Under a simple setting, we characterized the substitution effect and acceleration effect of the prediction head. The substitution effect happens when learning the stronger features in some neurons can substitute for learning these features in other neurons through updating the prediction head. And the acceleration effect happens when the substituted features can accelerate the learning of other weaker features to prevent them from being ignored. These two effects enable the neural networks to learn all the features rather than focus only on learning the stronger features, which is likely the cause of the dimensional collapse phenomenon. To the best of our knowledge, this is also the first end-to-end optimization guarantee for non-contrastive methods using nonlinear neural networks with a trainable prediction head and normalization.  ( 3 min )
    coVariance Neural Networks. (arXiv:2205.15856v4 [cs.LG] UPDATED)
    Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the projection of data on the eigenspace of the covariance matrix and draws similarities with the graph convolutional filters in GNNs. Motivated by this observation, we study a GNN architecture, called coVariance neural network (VNN), that operates on sample covariance matrices as graphs. We theoretically establish the stability of VNNs to perturbations in the covariance matrix, thus, implying an advantage over standard PCA-based data analysis approaches that are prone to instability due to principal components associated with close eigenvalues. Our experiments on real-world datasets validate our theoretical results and show that VNN performance is indeed more stable than PCA-based statistical approaches. Moreover, our experiments on multi-resolution datasets also demonstrate that VNNs are amenable to transferability of performance over covariance matrices of different dimensions; a feature that is infeasible for PCA-based approaches.  ( 2 min )
    Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulations. (arXiv:2111.10189v3 [cond-mat.stat-mech] UPDATED)
    We provide a deepened study of autocorrelations in Neural Markov Chain Monte Carlo (NMCMC) simulations, a version of the traditional Metropolis algorithm which employs neural networks to provide independent proposals. We illustrate our ideas using the two-dimensional Ising model. We discuss several estimates of autocorrelation times in the context of NMCMC, some inspired by analytical results derived for the Metropolized Independent Sampler (MIS). We check their reliability by estimating them on a small system where analytical results can also be obtained. Based on the analytical results for MIS we propose a new loss function and study its impact on the autocorelation times. Although, this function's performance is a bit inferior to the traditional Kullback-Leibler divergence, it offers two training algorithms which in some situations may be beneficial. By studying a small, $4 \times 4$, system we gain access to the dynamics of the training process which we visualize using several observables. Furthermore, we quantitatively investigate the impact of imposing global discrete symmetries of the system in the neural network training process on the autocorrelation times. Eventually, we propose a scheme which incorporates partial heat-bath updates which considerably improves the quality of the training. The impact of the above enhancements is discussed for a $16 \times 16$ spin system. The summary of our findings may serve as a guidance to the implementation of Neural Markov Chain Monte Carlo simulations for more complicated models.  ( 2 min )
    Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables. (arXiv:2206.00706v2 [stat.ML] UPDATED)
    We present a new concentration of measure inequality for sums of independent bounded random variables, which we name a split-kl inequality. The inequality is particularly well-suited for ternary random variables, which naturally show up in a variety of problems, including analysis of excess losses in classification, analysis of weighted majority votes, and learning with abstention. We demonstrate that for ternary random variables the inequality is simultaneously competitive with the kl inequality, the Empirical Bernstein inequality, and the Unexpected Bernstein inequality, and in certain regimes outperforms all of them. It resolves an open question by Tolstikhin and Seldin [2013] and Mhammedi et al. [2019] on how to match simultaneously the combinatorial power of the kl inequality when the distribution happens to be close to binary and the power of Bernstein inequalities to exploit low variance when the probability mass is concentrated on the middle value. We also derive a PAC-Bayes-split-kl inequality and compare it with the PAC-Bayes-kl, PAC-Bayes-Empirical-Bennett, and PAC-Bayes-Unexpected-Bernstein inequalities in an analysis of excess losses and in an analysis of a weighted majority vote for several UCI datasets. Last but not least, our study provides the first direct comparison of the Empirical Bernstein and Unexpected Bernstein inequalities and their PAC-Bayes extensions.  ( 2 min )
    Geometry-Complete Perceptron Networks for 3D Molecular Graphs. (arXiv:2211.02504v2 [cs.LG] UPDATED)
    The field of geometric deep learning has had a profound impact on the development of innovative and powerful graph neural network architectures. Disciplines such as computer vision and computational biology have benefited significantly from such methodological advances, which has led to breakthroughs in scientific domains such as protein structure prediction and design. In this work, we introduce GCPNet, a new geometry-complete, SE(3)-equivariant graph neural network designed for 3D molecular graph representation learning. We demonstrate the state-of-the-art utility and expressiveness of our method on six independent datasets designed for three distinct geometric tasks: protein-ligand binding affinity prediction, protein structure ranking, and Newtonian many-body systems modeling. Our results suggest that GCPNet is a powerful, general method for capturing complex geometric and physical interactions within 3D molecular graphs for downstream prediction tasks. The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.  ( 2 min )
    Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks. (arXiv:2110.13330v2 [cs.LG] UPDATED)
    Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function. This paper shows that a PINN can be sensitive to errors in training data and overfit itself in dynamically propagating these errors over the domain of the solution of the PDE. It also shows how physical regularizations based on continuity criteria and conservation laws fail to address this issue and rather introduce problems of their own causing the deep network to converge to a physics-obeying local minimum instead of the global minimum. We introduce Gaussian Process (GP) based smoothing that recovers the performance of a PINN and promises a robust architecture against noise/errors in measurements. Additionally, we illustrate an inexpensive method of quantifying the evolution of uncertainty based on the variance estimation of GPs on boundary data. Robust PINN performance is also shown to be achievable by choice of sparse sets of inducing points based on sparsely induced GPs. We demonstrate the performance of our proposed methods and compare the results from existing benchmark models in literature for time-dependent Schr\"odinger and Burgers' equations.  ( 2 min )
    Sinkhorn Divergences for Unbalanced Optimal Transport. (arXiv:1910.12958v3 [math.OC] UPDATED)
    Optimal transport induces the Earth Mover's (Wasserstein) distance between probability distributions, a geometric divergence that is relevant to a wide range of problems. Over the last decade, two relaxations of optimal transport have been studied in depth: unbalanced transport, which is robust to the presence of outliers and can be used when distributions don't have the same total mass; entropy-regularized transport, which is robust to sampling noise and lends itself to fast computations using the Sinkhorn algorithm. This paper combines both lines of work to put robust optimal transport on solid ground. Our main contribution is a generalization of the Sinkhorn algorithm to unbalanced transport: our method alternates between the standard Sinkhorn updates and the pointwise application of a contractive function. This implies that entropic transport solvers on grid images, point clouds and sampled distributions can all be modified easily to support unbalanced transport, with a proof of linear convergence that holds in all settings. We then show how to use this method to define pseudo-distances on the full space of positive measures that satisfy key geometric axioms: (unbalanced) Sinkhorn divergences are differentiable, positive, definite, convex, statistically robust and avoid any "entropic bias" towards a shrinkage of the measures' supports.  ( 2 min )
    Generalization Error Bounds for Multiclass Sparse Linear Classifiers. (arXiv:2204.06264v2 [math.ST] UPDATED)
    We consider high-dimensional multiclass classification by sparse multinomial logistic regression. Unlike binary classification, in the multiclass setup one can think about an entire spectrum of possible notions of sparsity associated with different structural assumptions on the regression coefficients matrix. We propose a computationally feasible feature selection procedure based on penalized maximum likelihood with convex penalties capturing a specific type of sparsity at hand. In particular, we consider global sparsity, double row-wise sparsity, and low-rank sparsity, and show that with the properly chosen tuning parameters the derived plug-in classifiers attain the minimax generalization error bounds (in terms of misclassification excess risk) within the corresponding classes of multiclass sparse linear classifiers. The developed approach is general and can be adapted to other types of sparsity as well.  ( 2 min )
    Post-training Quantization for Neural Networks with Provable Guarantees. (arXiv:2201.11113v3 [cs.LG] UPDATED)
    While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research. By replacing the weights of a neural network with quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation cost, memory, and power consumption are attained. To that end, we generalize a post-training neural-network quantization method, GPFQ, that is based on a greedy path-following mechanism. Among other things, we propose modifications to promote sparsity of the weights, and rigorously analyze the associated error. Additionally, our error analysis expands the results of previous work on GPFQ to handle general quantization alphabets, showing that for quantizing a single-layer network, the relative square error essentially decays linearly in the number of weights -- i.e., level of over-parametrization. Our result holds across a range of input distributions and for both fully-connected and convolutional architectures thereby also extending previous results. To empirically evaluate the method, we quantize several common architectures with few bits per weight, and test them on ImageNet, showing only minor loss of accuracy compared to unquantized models. We also demonstrate that standard modifications, such as bias correction and mixed precision quantization, further improve accuracy.  ( 2 min )
    Adapting to Online Label Shift with Provable Guarantees. (arXiv:2207.02121v3 [cs.LG] UPDATED)
    The standard supervised learning paradigm works effectively when training data shares the same distribution as the upcoming testing samples. However, this stationary assumption is often violated in real-world applications, especially when testing data appear in an online fashion. In this paper, we formulate and investigate the problem of \emph{online label shift} (OLaS): the learner trains an initial model from the labeled offline data and then deploys it to an unlabeled online environment where the underlying label distribution changes over time but the label-conditional density does not. The non-stationarity nature and the lack of supervision make the problem challenging to be tackled. To address the difficulty, we construct a new unbiased risk estimator that utilizes the unlabeled data, which exhibits many benign properties albeit with potential non-convexity. Building upon that, we propose novel online ensemble algorithms to deal with the non-stationarity of the environments. Our approach enjoys optimal \emph{dynamic regret}, indicating that the performance is competitive with a clairvoyant who knows the online environments in hindsight and then chooses the best decision for each round. The obtained dynamic regret bound scales with the intensity and pattern of label distribution shift, hence exhibiting the adaptivity in the OLaS problem. Extensive experiments are conducted to validate the effectiveness and support our theoretical findings.  ( 2 min )
    Minimax Optimal Online Imitation Learning via Replay Estimation. (arXiv:2205.15397v5 [cs.LG] UPDATED)
    Online imitation learning is the problem of how best to mimic expert demonstrations, given access to the environment or an accurate simulator. Prior work has shown that in the infinite sample regime, exact moment matching achieves value equivalence to the expert policy. However, in the finite sample regime, even if one has no optimization error, empirical variance can lead to a performance gap that scales with $H^2 / N$ for behavioral cloning and $H / \sqrt{N}$ for online moment matching, where $H$ is the horizon and $N$ is the size of the expert dataset. We introduce the technique of replay estimation to reduce this empirical variance: by repeatedly executing cached expert actions in a stochastic simulator, we compute a smoother expert visitation distribution estimate to match. In the presence of general function approximation, we prove a meta theorem reducing the performance gap of our approach to the parameter estimation error for offline classification (i.e. learning the expert policy). In the tabular setting or with linear function approximation, our meta theorem shows that the performance gap incurred by our approach achieves the optimal $\widetilde{O} \left( \min({H^{3/2}} / {N}, {H} / {\sqrt{N}} \right)$ dependency, under significantly weaker assumptions compared to prior work. We implement multiple instantiations of our approach on several continuous control tasks and find that we are able to significantly improve policy performance across a variety of dataset sizes.  ( 2 min )
    Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited. (arXiv:2205.10914v3 [cs.LG] UPDATED)
    Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism. We give a unified view on both classes of graph kernels. We study walk-based node refinement methods and formally relate them to several widely-used techniques, including Morgan's algorithm for molecule canonization and the Weisfeiler-Leman test. We define corresponding walk-based kernels on nodes that allow fine-grained parameterized neighborhood comparison, reach Weisfeiler-Leman expressiveness, and are computed using the kernel trick. From this we show that classical random walk kernels with only minor modifications regarding definition and computation are as expressive as the widely-used Weisfeiler-Leman subtree kernel but support non-strict neighborhood comparison. We verify experimentally that walk-based kernels reach or even surpass the accuracy of Weisfeiler-Leman kernels in real-world classification tasks.  ( 2 min )
    Toward Explainable AI for Regression Models. (arXiv:2112.11407v2 [cs.LG] UPDATED)
    In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks. Gaining a better understanding is especially important e.g. for safety-critical ML applications or medical diagnostics etc. While such Explainable AI (XAI) techniques have reached significant popularity for classifiers, so far little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI for regression and classification tasks, establish novel theoretical insights and analysis for XAIR, provide demonstrations of XAIR on genuine practical regression problems, and finally discuss the challenges remaining for the field.  ( 2 min )
    Neural Network Architecture Beyond Width and Depth. (arXiv:2205.09459v4 [cs.LG] UPDATED)
    This paper proposes a new neural network architecture by introducing an additional dimension called height beyond width and depth. Neural network architectures with height, width, and depth as hyper-parameters are called three-dimensional architectures. It is shown that neural networks with three-dimensional architectures are significantly more expressive than the ones with two-dimensional architectures (those with only width and depth as hyper-parameters), e.g., standard fully connected networks. The new network architecture is constructed recursively via a nested structure, and hence we call a network with the new architecture nested network (NestNet). A NestNet of height $s$ is built with each hidden neuron activated by a NestNet of height $\le s-1$. When $s=1$, a NestNet degenerates to a standard network with a two-dimensional architecture. It is proved by construction that height-$s$ ReLU NestNets with $\mathcal{O}(n)$ parameters can approximate $1$-Lipschitz continuous functions on $[0,1]^d$ with an error $\mathcal{O}(n^{-(s+1)/d})$, while the optimal approximation error of standard ReLU networks with $\mathcal{O}(n)$ parameters is $\mathcal{O}(n^{-2/d})$. Furthermore, such a result is extended to generic continuous functions on $[0,1]^d$ with the approximation error characterized by the modulus of continuity. Finally, we use numerical experimentation to show the advantages of the super-approximation power of ReLU NestNets.  ( 2 min )
    Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. (arXiv:2203.09675v3 [stat.ML] UPDATED)
    Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be run on the full posterior can instead be run inexpensively on the coreset, with results that approximate those on the full data. However, current approaches are limited by either a significant run-time or the need for the user to specify a low-cost approximation to the full posterior. We propose a Bayesian coreset construction algorithm that first selects a uniformly random subset of data, and then optimizes the weights using a novel quasi-Newton method. Our algorithm is a simple to implement, black-box method, that does not require the user to specify a low-cost posterior approximation. It is the first to come with a general high-probability bound on the KL divergence of the output coreset posterior. Experiments demonstrate that our method provides significant improvements in coreset quality against alternatives with comparable construction times, with far less storage cost and user input required.  ( 2 min )
    Forecasting Market Changes using Variational Inference. (arXiv:2205.00605v2 [q-fin.ST] UPDATED)
    Though various approaches have been considered, forecasting near-term market changes of equities and similar market data remains quite difficult. In this paper we introduce an approach to forecast near-term market changes for equity indices as well as portfolios using variational inference (VI). VI is a machine learning approach which uses optimization techniques to estimate complex probability densities. In the proposed approach, clusters of explanatory variables are identified and market changes are forecast based on cluster-specific linear regression. Apart from the expected value of changes, the proposed approach can also be used to obtain the distribution of possible outcomes. Another advantage of the proposed approach is the clear model interpretation, as clusters of explanatory variables (or market regimes) are identified for which the future changes follow similar relationships. Knowledge about such clusters can provide useful insights about portfolio performance and identify the relative importance of variables in different market regimes. An illustrative example of predicting one-day S\&P change is considered and it is shown that even with as few as three explanatory variables, the proposed approach provides useful predictions.  ( 2 min )
    Random Planted Forest: a directly interpretable tree ensemble. (arXiv:2012.14563v2 [stat.ML] UPDATED)
    We introduce a novel interpretable, tree based algorithm for prediction in a regression setting in which each tree in a classical random forest is replaced by a family of planted trees that grow simultaneously. The motivation for our algorithm is to estimate the unknown regression function from a functional decomposition perspective, where each tree corresponds to a function within that decomposition. The maximal order of approximation in the decomposition can be specified or left unlimited. If a first order approximation is chosen, the result is an additive model. In the other extreme case, if the order of approximation is not limited, the resulting model places no restrictions on the form of the regression function. In a simulation study we find encouraging prediction and visualisation properties of our random planted forest method. We also develop theory for an idealised version of random planted forests in cases where the maximal order of approximation is low. We show that if the order is smaller than three, the idealised version achieves asymptotically optimal convergence rates up to a logarithmic factor. ode is available on https://github.com/PlantedML/randomPlantedForest  ( 2 min )
    The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation. (arXiv:2009.04266v3 [math.OC] UPDATED)
    Comparing metric measure spaces (i.e. a metric space endowed with aprobability distribution) is at the heart of many machine learning problems. The most popular distance between such metric measure spaces is theGromov-Wasserstein (GW) distance, which is the solution of a quadratic assignment problem. The GW distance is however limited to the comparison of metric measure spaces endowed with a probability distribution. To alleviate this issue, we introduce two Unbalanced Gromov-Wasserstein formulations: a distance and a more tractable upper-bounding relaxation.They both allow the comparison of metric spaces equipped with arbitrary positive measures up to isometries. The first formulation is a positive and definite divergence based on a relaxation of the mass conservation constraint using a novel type of quadratically-homogeneous divergence. This divergence works hand in hand with the entropic regularization approach which is popular to solve large scale optimal transport problems. We show that the underlying non-convex optimization problem can be efficiently tackled using a highly parallelizable and GPU-friendly iterative scheme. The second formulation is a distance between mm-spaces up to isometries based on a conic lifting. Lastly, we provide numerical experiments onsynthetic examples and domain adaptation data with a Positive-Unlabeled learning task to highlight the salient features of the unbalanced divergence and its potential applications in ML.  ( 2 min )
    A Concentration of Measure Framework to study convex problems and other implicit formulation problems in machine learning. (arXiv:2010.09877v2 [math.PR] UPDATED)
    This paper provides a framework to show the concentration of solutions $Y^*$ to convex minimizing problem where the objective function $\phi(X)(Y)$ depends on some random vector $X$ satisfying concentration of measure hypotheses. More precisely, the convex problem translates into a contractive fixed point equation that ensure the transmission of the concentration from $X$ to $Y^*$. This result is of central interest to characterize many machine learning algorithms which are defined through implicit equations (e.g., logistic regression, lasso, boosting, etc.). Based on our framework, we provide precise estimations for the first moments of the solution $Y^*$, when $X= (x_1,\ldots, x_n)$ is a data matrix of independent columns and $\phi(X)(y)$ writes as a sum $\frac{1}{n}\sum_{i=1}^n h_i(x_i^TY)$. That allows to describe the behavior and performance (e.g., generalization error) of a wide variety of machine learning classifiers.  ( 2 min )
    Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks. (arXiv:2007.01498v2 [cs.AI] UPDATED)
    In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an optimal policy in this setting typically requires a large amount of training experiences. Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy. However, to the best of our knowledge, the theoretical properties of reward shaping have thus far only been established in the discounted setting. This paper presents the first reward shaping framework for average-reward learning and proves that, under standard assumptions, the optimal policy under the original reward function can be recovered. In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula. The formula is automatically translated to a shaping function that provides additional reward throughout the learning process. We evaluate the proposed method on three continuing tasks. In all cases, shaping speeds up the average-reward learning rate without any reduction in the performance of the learned policy compared to relevant baselines.  ( 2 min )
    Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems. (arXiv:2007.03481v5 [cs.LG] UPDATED)
    This paper presents an inverse reinforcement learning~(IRL) framework for Bayesian stopping time problems. By observing the actions of a Bayesian decision maker, we provide a necessary and sufficient condition to identify if these actions are consistent with optimizing a cost function. In a Bayesian (partially observed) setting, the inverse learner can at best identify optimality wrt the observed actions. Our IRL algorithm identifies optimality and then constructs set valued estimates of the cost function. To achieve this IRL objective, we use novel ideas from Bayesian revealed preferences stemming from microeconomics. We illustrate the proposed IRL scheme using two important examples of stopping time problems, namely, sequential hypothesis testing and Bayesian search, and also on a real-world YouTube dataset. Finally, for finite datasets, we propose an IRL detection algorithm and give finite sample bounds on its error probabilities.  ( 2 min )
    Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms. (arXiv:2006.14514v4 [cs.LG] UPDATED)
    Artificial neural networks (ANNs) are typically highly nonlinear systems which are finely tuned via the optimization of their associated, non-convex loss functions. In many cases, the gradient of any such loss function has superlinear growth, making the use of the widely-accepted (stochastic) gradient descent methods, which are based on Euler numerical schemes, problematic. We offer a new learning algorithm based on an appropriately constructed variant of the popular stochastic gradient Langevin dynamics (SGLD), which is called tamed unadjusted stochastic Langevin algorithm (TUSLA). We also provide a nonasymptotic analysis of the new algorithm's convergence properties in the context of non-convex learning problems with the use of ANNs. Thus, we provide finite-time guarantees for TUSLA to find approximate minimizers of both empirical and population risks. The roots of the TUSLA algorithm are based on the taming technology for diffusion processes with superlinear coefficients as developed in \citet{tamed-euler, SabanisAoAP} and for MCMC algorithms in \citet{tula}. Numerical experiments are presented which confirm the theoretical findings and illustrate the need for the use of the new algorithm in comparison to vanilla SGLD within the framework of ANNs.  ( 2 min )
    Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning. (arXiv:1909.05850v6 [stat.ML] UPDATED)
    Off-policy evaluation (OPE) in reinforcement learning is notoriously difficult in long- and infinite-horizon settings due to diminishing overlap between behavior and target policies. In this paper, we study the role of Markovian and time-invariant structure in efficient OPE. We first derive the efficiency bounds for OPE when one assumes each of these structures. This precisely characterizes the curse of horizon: in time-variant processes, OPE is only feasible in the near-on-policy setting, where behavior and target policies are sufficiently similar. But, in time-invariant Markov decision processes, our bounds show that truly-off-policy evaluation is feasible, even with only just one dependent trajectory, and provide the limits of how well we could hope to do. We develop a new estimator based on Double Reinforcement Learning (DRL) that leverages this structure for OPE using the efficient influence function we derive. Our DRL estimator simultaneously uses estimated stationary density ratios and $q$-functions and remains efficient when both are estimated at slow, nonparametric rates and remains consistent when either is estimated consistently. We investigate these properties and the performance benefits of leveraging the problem structure for more efficient OPE.  ( 2 min )
    Decentralized Exploration in Multi-Armed Bandits -- Extended version. (arXiv:1811.07763v6 [cs.LG] UPDATED)
    We consider the decentralized exploration problem: a set of players collaborate to identify the best arm by asynchronously interacting with the same stochastic environment. The objective is to insure privacy in the best arm identification problem between asynchronous, collaborative, and thrifty players. In the context of a digital service, we advocate that this decentralized approach allows a good balance between the interests of users and those of service providers: the providers optimize their services, while protecting the privacy of the users and saving resources. We define the privacy level as the amount of information an adversary could infer by intercepting the messages concerning a single user. We provide a generic algorithm Decentralized Elimination, which uses any best arm identification algorithm as a subroutine. We prove that this algorithm insures privacy, with a low communication cost, and that in comparison to the lower bound of the best arm identification problem, its sample complexity suffers from a penalty depending on the inverse of the probability of the most frequent players. Then, thanks to the genericity of the approach, we extend the proposed algorithm to the non-stationary bandits. Finally, experiments illustrate and complete the analysis.  ( 2 min )
    Martingale Methods for Sequential Estimation of Convex Functionals and Divergences. (arXiv:2103.09267v3 [math.ST] UPDATED)
    We present a unified technique for sequential estimation of convex divergences between distributions, including integral probability metrics like the kernel maximum mean discrepancy, $\varphi$-divergences like the Kullback-Leibler divergence, and optimal transport costs, such as powers of Wasserstein distances. This is achieved by observing that empirical convex divergences are (partially ordered) reverse submartingales with respect to the exchangeable filtration, coupled with maximal inequalities for such processes. These techniques appear to be complementary and powerful additions to the existing literature on both confidence sequences and convex divergences. We construct an offline-to-sequential device that converts a wide array of existing offline concentration inequalities into time-uniform confidence sequences that can be continuously monitored, providing valid tests or confidence intervals at arbitrary stopping times. The resulting sequential bounds pay only an iterated logarithmic price over the corresponding fixed-time bounds, retaining the same dependence on problem parameters (like dimension or alphabet size if applicable). These results are also applicable to more general convex functionals, like the negative differential entropy, suprema of empirical processes, and V-Statistics.  ( 2 min )
    Robust Max Entrywise Error Bounds for Tensor Estimation from Sparse Observations via Similarity Based Collaborative Filtering. (arXiv:1908.01241v4 [cs.LG] UPDATED)
    Consider the task of estimating a 3-order $n \times n \times n$ tensor from noisy observations of randomly chosen entries in the sparse regime. We introduce a similarity based collaborative filtering algorithm for estimating a tensor from sparse observations and argue that it achieves sample complexity that nearly matches the conjectured computationally efficient lower bound on the sample complexity for the setting of low-rank tensors. Our algorithm uses the matrix obtained from the flattened tensor to compute similarity, and estimates the tensor entries using a nearest neighbor estimator. We prove that the algorithm recovers a finite rank tensor with maximum entry-wise error (MEE) and mean-squared-error (MSE) decaying to $0$ as long as each entry is observed independently with probability $p = \Omega(n^{-3/2 + \kappa})$ for any arbitrarily small $\kappa > 0$. More generally, we establish robustness of the estimator, showing that when arbitrary noise bounded by $\varepsilon \geq 0$ is added to each observation, the estimation error with respect to MEE and MSE degrades by $\text{poly}(\varepsilon)$. Consequently, even if the tensor may not have finite rank but can be approximated within $\varepsilon \geq 0$ by a finite rank tensor, then the estimation error converges to $\text{poly}(\varepsilon)$. Our analysis sheds insight into the conjectured sample complexity lower bound, showing that it matches the connectivity threshold of the graph used by our algorithm for estimating similarity between coordinates.  ( 2 min )
    Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks. (arXiv:1906.04893v2 [cs.LG] UPDATED)
    Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many applications ranging from robustness certification of classifiers to stability analysis of closed-loop systems with reinforcement learning controllers. Existing methods in the literature for estimating the Lipschitz constant suffer from either lack of accuracy or poor scalability. In this paper, we present a convex optimization framework to compute guaranteed upper bounds on the Lipschitz constant of DNNs both accurately and efficiently. Our main idea is to interpret activation functions as gradients of convex potential functions. Hence, they satisfy certain properties that can be described by quadratic constraints. This particular description allows us to pose the Lipschitz constant estimation problem as a semidefinite program (SDP). The resulting SDP can be adapted to increase either the estimation accuracy (by capturing the interaction between activation functions of different layers) or scalability (by decomposition and parallel implementation). We illustrate the utility of our approach with a variety of experiments on randomly generated networks and on classifiers trained on the MNIST and Iris datasets. In particular, we experimentally demonstrate that our Lipschitz bounds are the most accurate compared to those in the literature. We also study the impact of adversarial training methods on the Lipschitz bounds of the resulting classifiers and show that our bounds can be used to efficiently provide robustness guarantees.  ( 2 min )
    On the role of Model Uncertainties in Bayesian Optimization. (arXiv:2301.05983v1 [stat.ML])
    Bayesian optimization (BO) is a popular method for black-box optimization, which relies on uncertainty as part of its decision-making process when deciding which experiment to perform next. However, not much work has addressed the effect of uncertainty on the performance of the BO algorithm and to what extent calibrated uncertainties improve the ability to find the global optimum. In this work, we provide an extensive study of the relationship between the BO performance (regret) and uncertainty calibration for popular surrogate models and compare them across both synthetic and real-world experiments. Our results confirm that Gaussian Processes are strong surrogate models and that they tend to outperform other popular models. Our results further show a positive association between calibration error and regret, but interestingly, this association disappears when we control for the type of model in the analysis. We also studied the effect of re-calibration and demonstrate that it generally does not lead to improved regret. Finally, we provide theoretical justification for why uncertainty calibration might be difficult to combine with BO due to the small sample sizes commonly used.  ( 2 min )
    Transformers as Algorithms: Generalization and Implicit Model Selection in In-context Learning. (arXiv:2301.07067v1 [cs.LG])
    In-context learning (ICL) is a type of prompting where a transformer model operates on a sequence of (input, output) examples and performs inference on-the-fly. This implicit training is in contrast to explicitly tuning the model weights based on examples. In this work, we formalize in-context learning as an algorithm learning problem, treating the transformer model as a learning algorithm that can be specialized via training to implement-at inference-time-another target algorithm. We first explore the statistical aspects of this abstraction through the lens of multitask learning: We obtain generalization bounds for ICL when the input prompt is (1) a sequence of i.i.d. (input, label) pairs or (2) a trajectory arising from a dynamical system. The crux of our analysis is relating the excess risk to the stability of the algorithm implemented by the transformer, which holds under mild assumptions. Secondly, we use our abstraction to show that transformers can act as an adaptive learning algorithm and perform model selection across different hypothesis classes. We provide numerical evaluations that (1) demonstrate transformers can indeed implement near-optimal algorithms on classical regression problems with i.i.d. and dynamic data, (2) identify an inductive bias phenomenon where the transfer risk on unseen tasks is independent of the transformer complexity, and (3) empirically verify our theoretical predictions.  ( 2 min )
    A Fast Algorithm for Adaptive Private Mean Estimation. (arXiv:2301.07078v1 [stat.ML])
    We design an $(\varepsilon, \delta)$-differentially private algorithm to estimate the mean of a $d$-variate distribution, with unknown covariance $\Sigma$, that is adaptive to $\Sigma$. To within polylogarithmic factors, the estimator achieves optimal rates of convergence with respect to the induced Mahalanobis norm $||\cdot||_\Sigma$, takes time $\tilde{O}(n d^2)$ to compute, has near linear sample complexity for sub-Gaussian distributions, allows $\Sigma$ to be degenerate or low rank, and adaptively extends beyond sub-Gaussianity. Prior to this work, other methods required exponential computation time or the superlinear scaling $n = \Omega(d^{3/2})$ to achieve non-trivial error with respect to the norm $||\cdot||_\Sigma$.  ( 2 min )
    MAFUS: a Framework to predict mortality risk in MAFLD subjects. (arXiv:2301.06908v1 [stat.ML])
    Metabolic (dysfunction) associated fatty liver disease (MAFLD) establishes new criteria for diagnosing fatty liver disease independent of alcohol consumption and concurrent viral hepatitis infection. However, the long-term outcome of MAFLD subjects is sparse. Few articles are focused on mortality in MAFLD subjects, and none investigate how to predict a fatal outcome. In this paper, we propose an artificial intelligence-based framework named MAFUS that physicians can use for predicting mortality in MAFLD subjects. The framework uses data from various anthropometric and biochemical sources based on Machine Learning (ML) algorithms. The framework has been tested on a state-of-the-art dataset on which five ML algorithms are trained. Support Vector Machines resulted in being the best model. Furthermore, an Explainable Artificial Intelligence (XAI) analysis has been performed to understand the SVM diagnostic reasoning and the contribution of each feature to the prediction. The MAFUS framework is easy to apply, and the required parameters are readily available in the dataset.  ( 2 min )
    Enhancing Deep Traffic Forecasting Models with Dynamic Regression. (arXiv:2301.06650v1 [cs.LG])
    A common assumption in deep learning-based multivariate and multistep traffic time series forecasting models is that residuals are independent, isotropic, and uncorrelated in space and time. While this assumption provides a straightforward loss function (such as MAE/MSE), it is inevitable that residual processes will exhibit strong autocorrelation and structured spatiotemporal correlation. In this paper, we propose a complementary dynamic regression (DR) framework to enhance existing deep multistep traffic forecasting frameworks through structured specifications and learning for the residual process. Specifically, we assume the residuals of the base model (i.e., a well-developed traffic forecasting model) are governed by a matrix-variate seasonal autoregressive (AR) model, which can be seamlessly integrated into the training process by redesigning the overall loss function. Parameters in the DR framework can be jointly learned with the base model. We evaluate the effectiveness of the proposed framework in enhancing several state-of-the-art deep traffic forecasting models on both speed and flow datasets. Our experiment results show that the DR framework not only improves existing traffic forecasting models but also offers interpretable regression coefficients and spatiotemporal covariance matrices.  ( 2 min )
    Neural Operator Framework for Digital Twin and Complex Engineering Systems. (arXiv:2301.06701v1 [cs.LG])
    With modern computational advancements and statistical analysis methods, machine learning algorithms have become a vital part of engineering modeling. Neural Operator Networks (ONets) is an emerging machine learning algorithm as a "faster surrogate" for approximating solutions to partial differential equations (PDEs) due to their ability to approximate mathematical operators versus the direct approximation of Neural Networks (NN). ONets use the Universal Approximation Theorem to map finite-dimensional inputs to infinite-dimensional space using the branch-trunk architecture, which encodes domain and feature information separately before using a dot product to combine the information. ONets are expected to occupy a vital niche for surrogate modeling in physical systems and Digital Twin (DT) development. Three test cases are evaluated using ONets for operator approximation, including a 1-dimensional ordinary differential equations (ODE), general diffusion system, and convection-diffusion (Burger) system. Solutions for ODE and diffusion systems yield accurate and reliable results (R2>0.95), while solutions for Burger systems need further refinement in the ONet algorithm.  ( 2 min )
    $Ae^2I$: A Double Autoencoder for Imputation of Missing Values. (arXiv:2301.06633v1 [cs.LG])
    The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from other columns of the same row, or from the other rows of the same column. This paper introduces a double autoencoder for imputation ($Ae^2I$) that simultaneously and collaboratively uses both row-row relationship and column-column relationship to impute the missing values. Empirical tests on Movielens 1M dataset demonstrated that $Ae^2I$ outperforms the current state-of-the-art models for recommender systems by a significant margin.  ( 2 min )
    Deep Conditional Measure Quantization. (arXiv:2301.06907v1 [stat.ML])
    The quantization of a (probability) measure is replacing it by a sum of Dirac masses that is close enough to it (in some metric space of probability measures). Various methods exists to do so, but the situation of quantizing a conditional law has been less explored. We propose a method, called DCMQ, involving a Huber-energy kernel-based approach coupled with a deep neural network architecture. The method is tested on several examples and obtains promising results.  ( 2 min )
    Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency. (arXiv:2301.06240v1 [math.ST])
    We study optimal procedures for estimating a linear functional based on observational data. In many problems of this kind, a widely used assumption is strict overlap, i.e., uniform boundedness of the importance ratio, which measures how well the observational data covers the directions of interest. When it is violated, the classical semi-parametric efficiency bound can easily become infinite, so that the instance-optimal risk depends on the function class used to model the regression function. For any convex and symmetric function class $\mathcal{F}$, we derive a non-asymptotic local minimax bound on the mean-squared error in estimating a broad class of linear functionals. This lower bound refines the classical semi-parametric one, and makes connections to moduli of continuity in functional estimation. When $\mathcal{F}$ is a reproducing kernel Hilbert space, we prove that this lower bound can be achieved up to a constant factor by analyzing a computationally simple regression estimator. We apply our general results to various families of examples, thereby uncovering a spectrum of rates that interpolate between the classical theories of semi-parametric efficiency (with $\sqrt{n}$-consistency) and the slower minimax rates associated with non-parametric function estimation.  ( 2 min )
    Theoretical and computational aspects of robust optimal transportation, with applications to statistics and machine learning. (arXiv:2301.06297v1 [math.ST])
    Optimal transport (OT) theory and the related $p$-Wasserstein distance ($W_p$, $p\geq 1$) are popular tools in statistics and machine learning. Recent studies have been remarking that inference based on OT and on $W_p$ is sensitive to outliers. To cope with this issue, we work on a robust version of the primal OT problem (ROBOT) and show that it defines a robust version of $W_1$, called robust Wasserstein distance, which is able to downweight the impact of outliers. We study properties of this novel distance and use it to define minimum distance estimators. Our novel estimators do not impose any moment restrictions: this allows us to extend the use of OT methods to inference on heavy-tailed distributions. We also provide statistical guarantees of the proposed estimators. Moreover, we derive the dual form of the ROBOT and illustrate its applicability to machine learning. Numerical exercises (see also the supplementary material) provide evidence of the benefits yielded by our methods.  ( 2 min )
    Doubly Robust Counterfactual Classification. (arXiv:2301.06199v1 [cs.LG])
    We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios. We propose a doubly-robust nonparametric estimator for a general counterfactual classifier, where we can incorporate flexible constraints by casting the classification problem as a nonlinear mathematical program involving counterfactuals. We go on to analyze the rates of convergence of the estimator and provide a closed-form expression for its asymptotic distribution. Our analysis shows that the proposed estimator is robust against nuisance model misspecification, and can attain fast $\sqrt{n}$ rates with tractable inference even when using nonparametric machine learning approaches. We study the empirical performance of our methods by simulation and apply them for recidivism risk prediction.  ( 2 min )
    Data-aware customization of activation functions reduces neural network error. (arXiv:2301.06635v1 [cs.LG])
    Activation functions play critical roles in neural networks, yet current off-the-shelf neural networks pay little attention to the specific choice of activation functions used. Here we show that data-aware customization of activation functions can result in striking reductions in neural network error. We first give a simple linear algebraic explanation of the role of activation functions in neural networks; then, through connection with the Diaconis-Shahshahani Approximation Theorem, we propose a set of criteria for good activation functions. As a case study, we consider regression tasks with a partially exchangeable target function, \emph{i.e.} $f(u,v,w)=f(v,u,w)$ for $u,v\in \mathbb{R}^d$ and $w\in \mathbb{R}^k$, and prove that for such a target function, using an even activation function in at least one of the layers guarantees that the prediction preserves partial exchangeability for best performance. Since even activation functions are seldom used in practice, we designed the ``seagull'' even activation function $\log(1+x^2)$ according to our criteria. Empirical testing on over two dozen 9-25 dimensional examples with different local smoothness, curvature, and degree of exchangeability revealed that a simple substitution with the ``seagull'' activation function in an already-refined neural network can lead to an order-of-magnitude reduction in error. This improvement was most pronounced when the activation function substitution was applied to the layer in which the exchangeable variables are connected for the first time. While the improvement is greatest for low-dimensional data, experiments on the CIFAR10 image classification dataset showed that use of ``seagull'' can reduce error even for high-dimensional cases. These results collectively highlight the potential of customizing activation functions as a general approach to improve neural network performance.  ( 2 min )
    Asymptotic normality and optimality in nonsmooth stochastic approximation. (arXiv:2301.06632v1 [math.OC])
    In their seminal work, Polyak and Juditsky showed that stochastic approximation algorithms for solving smooth equations enjoy a central limit theorem. Moreover, it has since been argued that the asymptotic covariance of the method is best possible among any estimation procedure in a local minimax sense of H\'{a}jek and Le Cam. A long-standing open question in this line of work is whether similar guarantees hold for important non-smooth problems, such as stochastic nonlinear programming or stochastic variational inequalities. In this work, we show that this is indeed the case.  ( 2 min )
    Geometric ergodicity of SGLD via reflection coupling. (arXiv:2301.06769v1 [math.PR])
    We consider the geometric ergodicity of the Stochastic Gradient Langevin Dynamics (SGLD) algorithm under nonconvexity settings. Via the technique of reflection coupling, we prove the Wasserstein contraction of SGLD when the target distribution is log-concave only outside some compact set. The time discretization and the minibatch in SGLD introduce several difficulties when applying the reflection coupling, which are addressed by a series of careful estimates of conditional expectations. As a direct corollary, the SGLD with constant step size has an invariant distribution and we are able to obtain its geometric ergodicity in terms of $W_1$ distance. The generalization to non-gradient drifts is also included.  ( 2 min )
    Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions. (arXiv:2301.06535v1 [stat.ML])
    Neural network-based survival methods can model data-driven covariate interactions. While these methods have led to better predictive performance than regression-based approaches, they cannot model both time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNN) as a new approach that combines the case-base sampling framework with flexible architectures. Our method naturally accounts for censoring and does not require method specific hyperparameters. Using a novel sampling scheme and data augmentation, we incorporate time directly into a feed-forward neural network. CBNN predicts the probability of an event occurring at a given moment and estimates the hazard function. We compare the performance of CBNN to survival methods based on regression and neural networks in two simulations and two real data applications. We report two time-dependent metrics for each model. In the simulations and real data applications, CBNN provides a more consistent predictive performance across time and outperforms the competing neural network approaches. For a simple simulation with an exponential hazard model, CBNN outperforms the other neural network methods. For a complex simulation, which highlights the ability of CBNN to model both a complex baseline hazard and time-varying interactions, CBNN outperforms all competitors. The first real data application shows CBNN outperforming all neural network competitors, while a second real data application shows competitive performance. We highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible modeling framework for data-driven, time-varying interaction modeling of survival outcomes. An R package is available at https://github.com/Jesse-Islam/cbnn.  ( 2 min )
    Expected Gradients of Maxout Networks and Consequences to Parameter Initialization. (arXiv:2301.06956v1 [stat.ML])
    We study the gradients of a maxout network with respect to inputs and parameters and obtain bounds for the moments depending on the architecture and the parameter distribution. We observe that the distribution of the input-output Jacobian depends on the input, which complicates a stable parameter initialization. Based on the moments of the gradients, we formulate parameter initialization strategies that avoid vanishing and exploding gradients in wide networks. Experiments with deep fully-connected and convolutional networks show that this strategy improves SGD and Adam training of deep maxout networks. In addition, we obtain refined bounds on the expected number of linear regions, results on the expected curve length distortion, and results on the NTK.  ( 2 min )
    Optimal Algorithms for Latent Bandits with Cluster Structure. (arXiv:2301.07040v1 [cs.LG])
    We consider the problem of latent bandits with cluster structure where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \emph{latent} clusters such that the mean reward vectors of users within the same cluster are identical. At each round, a user, selected uniformly at random, pulls an arm and observes a corresponding noisy reward. The goal of the users is to maximize their cumulative rewards. This problem is central to practical recommendation systems and has received wide attention of late \cite{gentile2014online, maillard2014latent}. Now, if each user acts independently, then they would have to explore each arm independently and a regret of $\Omega(\sqrt{\mathsf{MNT}})$ is unavoidable, where $\mathsf{M}, \mathsf{N}$ are the number of arms and users, respectively. Instead, we propose LATTICE (Latent bAndiTs via maTrIx ComplEtion) which allows exploitation of the latent cluster structure to provide the minimax optimal regret of $\widetilde{O}(\sqrt{(\mathsf{M}+\mathsf{N})\mathsf{T}})$, when the number of clusters is $\widetilde{O}(1)$. This is the first algorithm to guarantee such a strong regret bound. LATTICE is based on a careful exploitation of arm information within a cluster while simultaneously clustering users. Furthermore, it is computationally efficient and requires only $O(\log{\mathsf{T}})$ calls to an offline matrix completion oracle across all $\mathsf{T}$ rounds.  ( 2 min )
    From Risk Prediction to Risk Factors Interpretation. Comparison of Neural Networks and Classical Statistics for Dementia Prediction. (arXiv:2301.06995v1 [stat.AP])
    It is proposed to investigate the onset of a disease D, based on several risk factors., with a specific interest in Alzheimer occurrence. For that purpose, two classes of techniques are available, whose properties are quite different in terms of interpretation, which is the focus of this paper: classical statistics based on probabilistic models and artificial intelligence (mainly neural networks) based on optimization algorithms. Both methods are good at prediction, with a preference for neural networks when the dimension of the potential predictors is high. But the advantage of the classical statistics is cognitive : the role of each factor is generally summarized in the value of a coefficient which is highly positive for a harmful factor, close to 0 for an irrelevant one, and highly negative for a beneficial one.  ( 2 min )
    A Coreset Learning Reality Check. (arXiv:2301.06163v1 [cs.LG])
    Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.  ( 2 min )
    GAR: Generalized Autoregression for Multi-Fidelity Fusion. (arXiv:2301.05729v1 [stat.ML])
    In many scientific research and engineering applications where repeated simulations of complex systems are conducted, a surrogate is commonly adopted to quickly estimate the whole system. To reduce the expensive cost of generating training examples, it has become a promising approach to combine the results of low-fidelity (fast but inaccurate) and high-fidelity (slow but accurate) simulations. Despite the fast developments of multi-fidelity fusion techniques, most existing methods require particular data structures and do not scale well to high-dimensional output. To resolve these issues, we generalize the classic autoregression (AR), which is wildly used due to its simplicity, robustness, accuracy, and tractability, and propose generalized autoregression (GAR) using tensor formulation and latent features. GAR can deal with arbitrary dimensional outputs and arbitrary multifidelity data structure to satisfy the demand of multi-fidelity fusion for complex problems; it admits a fully tractable likelihood and posterior requiring no approximate inference and scales well to high-dimensional problems. Furthermore, we prove the autokrigeability theorem based on GAR in the multi-fidelity case and develop CIGAR, a simplified GAR with the exact predictive mean accuracy with computation reduction by a factor of d 3, where d is the dimensionality of the output. The empirical assessment includes many canonical PDEs and real scientific examples and demonstrates that the proposed method consistently outperforms the SOTA methods with a large margin (up to 6x improvement in RMSE) with only a couple high-fidelity training samples.  ( 2 min )
    Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. (arXiv:2301.06195v1 [stat.ML])
    We consider the task of training machine learning models with data-dependent constraints. Such constraints often arise as empirical versions of expected value constraints that enforce fairness or stability goals. We reformulate data-dependent constraints so that they are calibrated: enforcing the reformulated constraints guarantees that their expected value counterparts are satisfied with a user-prescribed probability. The resulting optimization problem is amendable to standard stochastic optimization algorithms, and we demonstrate the efficacy of our method on a fairness-sensitive classification task where we wish to guarantee the classifier's fairness (at test time).  ( 2 min )
    Tale of two c(omplex)ities. (arXiv:2301.06259v1 [math.ST])
    For decades, best subset selection (BSS) has eluded statisticians mainly due to its computational bottleneck. However, until recently, modern computational breakthroughs have rekindled theoretical interest in BSS and have led to new findings. Recently, Guo et al. (2020) showed that the model selection performance of BSS is governed by a margin quantity that is robust to the design dependence, unlike modern methods such as LASSO, SCAD, MCP, etc. Motivated by their theoretical results, in this paper, we also study the variable selection properties of best subset selection for high-dimensional sparse linear regression setup. We show that apart from the identifiability margin, the following two complexity measures play a fundamental role in characterizing the margin condition for model consistency: (a) complexity of residualized features, (b) complexity of spurious projections. In particular, we establish a simple margin condition that only depends only on the identifiability margin quantity and the dominating one of the two complexity measures. Furthermore, we show that a similar margin condition depending on similar margin quantity and complexity measures is also necessary for model consistency of BSS. For a broader understanding of the complexity measures, we also consider some simple illustrative examples to demonstrate the variation in the complexity measures which broadens our theoretical understanding of the model selection performance of BSS under different correlation structures.  ( 2 min )
    Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm. (arXiv:2301.06641v1 [stat.ML])
    Deep neural networks are getting larger. Their implementation on edge and IoT devices becomes more challenging and moved the community to design lighter versions with similar performance. Standard automatic design tools such as \emph{reinforcement learning} and \emph{evolutionary computing} fundamentally rely on cheap evaluations of an objective function. In the neural network design context, this objective is the accuracy after training, which is expensive and time-consuming to evaluate. We automate the design of a light deep neural network for image classification using the \emph{Mesh Adaptive Direct Search}(MADS) algorithm, a mature derivative-free optimization method that effectively accounts for the expensive blackbox nature of the objective function to explore the design space, even in the presence of constraints.Our tests show competitive compression rates with reduced numbers of trials.  ( 2 min )
    Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics. (arXiv:2301.06533v1 [stat.ML])
    This article presents a novel approach to construct Intrinsic Gaussian Processes for regression on unknown manifolds with probabilistic metrics (GPUM) in point clouds. In many real world applications, one often encounters high dimensional data (e.g. point cloud data) centred around some lower dimensional unknown manifolds. The geometry of manifold is in general different from the usual Euclidean geometry. Naively applying traditional smoothing methods such as Euclidean Gaussian Processes (GPs) to manifold valued data and so ignoring the geometry of the space can potentially lead to highly misleading predictions and inferences. A manifold embedded in a high dimensional Euclidean space can be well described by a probabilistic mapping function and the corresponding latent space. We investigate the geometrical structure of the unknown manifolds using the Bayesian Gaussian Processes latent variable models(BGPLVM) and Riemannian geometry. The distribution of the metric tensor is learned using BGPLVM. The boundary of the resulting manifold is defined based on the uncertainty quantification of the mapping. We use the the probabilistic metric tensor to simulate Brownian Motion paths on the unknown manifold. The heat kernel is estimated as the transition density of Brownian Motion and used as the covariance functions of GPUM. The applications of GPUM are illustrated in the simulation studies on the Swiss roll, high dimensional real datasets of WiFi signals and image data examples. Its performance is compared with the Graph Laplacian GP, Graph Matern GP and Euclidean GP.  ( 2 min )
    A domain-decomposed VAE method for Bayesian inverse problems. (arXiv:2301.05708v1 [stat.ML])
    Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs). This is typically caused by expensive forward model evaluations and high-dimensional parameterization of priors. This paper proposes a domain-decomposed variational auto-encoder Markov chain Monte Carlo (DD-VAE-MCMC) method to tackle these challenges simultaneously. Through partitioning the global physical domain into small subdomains, the proposed method first constructs local deterministic generative models based on local historical data, which provide efficient local prior representations. Gaussian process models with active learning address the domain decomposition interface conditions. Then inversions are conducted on each subdomain independently in parallel and in low-dimensional latent parameter spaces. The local inference solutions are post-processed through the Poisson image blending procedure to result in an efficient global inference result. Numerical examples are provided to demonstrate the performance of the proposed method.  ( 2 min )
    Compress Then Test: Powerful Kernel Testing in Near-linear Time. (arXiv:2301.05974v1 [stat.ML])
    Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on $n$ sample points. However, existing kernel tests either run in $n^2$ time or sacrifice undue power to improve runtime. To address these shortcomings, we introduce Compress Then Test (CTT), a new framework for high-powered kernel testing based on sample compression. CTT cheaply approximates an expensive test by compressing each $n$ point sample into a small but provably high-fidelity coreset. For standard kernels and subexponential distributions, CTT inherits the statistical behavior of a quadratic-time test -- recovering the same optimal detection boundary -- while running in near-linear time. We couple these advances with cheaper permutation testing, justified by new power analyses; improved time-vs.-quality guarantees for low-rank approximation; and a fast aggregation procedure for identifying especially discriminating kernels. In our experiments with real and simulated data, CTT and its extensions provide 20--200x speed-ups over state-of-the-art approximate MMD tests with no loss of power.  ( 2 min )

  • Open

    Is there an AI API that can relate images to text?
    I have a pretty big catalogue of digital miniatures on Notion, which all have images and tags. The proccess of tagging each entry is very laborious, so I was wondering if I could use some kind of AI service that can train on my dataset, so that I could use it to automatically fill the tags for me, and also to use it as a search engine. I know some javascript and have some experience using APIs, but I'm not a developer, so I'm looking for something that's relatively easy to use, and that I won't need to learn C# or other advanced programming languages. ​ This is a sample entry from my database submitted by /u/Rayuaz [link] [comments]  ( 43 min )
    Can anyone answer this?
    What are amazon and Google and Bing policies on AI generated text - any test cases or precedents where accounts or sites closed? submitted by /u/TheChalaK- [link] [comments]  ( 43 min )
    These boston dynamics videos just keep getting more and more concerning.
    submitted by /u/Rollyman1 [link] [comments]  ( 43 min )
    What AI can I use to make deepfakes of my voice?
    I've recently been seeing artists feeding AI their voice & having it sing for them. That's pretty cool & I'd like to try it, but all I'm finding are research papers on it and no actual open AI for me to try this. Anyone know where I can access such an AI? submitted by /u/KelkonBajam [link] [comments]  ( 50 min )
    Synthetic data is the future of AI
    https://moez-62905.medium.com/synthetic-data-is-the-future-of-artificial-intelligence-6fcfd2ce1a14 submitted by /u/repeat_or [link] [comments]  ( 46 min )
    A short story about ChatGPT3 killing the giant Google
    submitted by /u/Imagine-your-success [link] [comments]  ( 55 min )
    Generative AI and The Future of Work
    submitted by /u/_utisz_ [link] [comments]  ( 52 min )
    AI is Assisting the UN in Preventing Nuclear War
    submitted by /u/HODLTID [link] [comments]  ( 42 min )
    Anyone know a "zyral, Zyro" AI application?
    Heard it on a stream but can't seem to figure out the proper spelling of it. The streamer uses it from thumbnails. submitted by /u/madalert123 [link] [comments]  ( 43 min )
    Ryan Reyonlds Toonified using VToonify
    submitted by /u/oridnary_artist [link] [comments]  ( 42 min )
    ByteDance AI Research Proposes a Novel Self-Supervised Learning Framework to Create High-Quality Stylized 3D Avatars with a Mix of Continuous and Discrete Parameters
    submitted by /u/ai-lover [link] [comments]  ( 43 min )
    Artificial Waifus
    Some programmer on tiktok made an AI waifu (source). He wants to redo the project using a real girl's text messages. Imagine a world where women and men can sell their text histories in order to train better models of themselves to be used by others. This is just the beginning, boys. submitted by /u/crowb1rd [link] [comments]  ( 43 min )
    14 highlights from Sam Altman's interview
    From https://smokingrobot.beehiiv.com/p/sam-altman-interview-strictly-vc ​ On the unexpected progress of AI: Everyone thought at first it comes for physical labor, like working in a factory and then truck driving, then this sort of less demanding cognitive labor, and then the really demanding cognitive labor like computer programming. And then very last of all or maybe never because maybe it's like some deep human special sauce, was creativity. And of course we can look now and say it really looks like it's going to go exactly the opposite direction. On the impact on education and other changes: There are societal changes that ChatGPT is going to cause or is causing. There's I think a big one going now about the impact of this on education, academic integrity, and all of that. But star…  ( 63 min )
    New Microsoft AI can accurately mimic a human voice after analyzing a 3-second sample
    As advancements in artificial intelligence continue to unfold at a rapid pace, it is not uncommon for individuals to express concerns about the potential implications on employment opportunities for human workers. Adding fuel to these concerns is the recent announcement made by a team of researchers at Microsoft, who have developed a new AI system capable of accurately replicating a human voice using only a three-second audio sample. This breakthrough in technology highlights the potential for AI to not only automate a plethora of tasks, but also to potentially replicate human capabilities and skills with increased accuracy and efficiency. The implications of this development are significant, as it raises important questions about the future of work and the role of AI in it. Furthermore, i…  ( 48 min )
    Is there a (as complete as possible) ranking for Language Models?
    Hello AI community, as the title says I am looking for a (up-to-date) ranking list for as many LMs (BERT, RoBERTa, T5, yada yada yada) as possible with their corresponding scores in the different tasks. Is there maybe some site which is keeping track of these scores or some awesome GitHub page? Thank you for any hints! submitted by /u/Own-Technology-9815 [link] [comments]  ( 51 min )
    DeepL launches New Product ‘Write’ To Take On Grammarly
    submitted by /u/liquidocelotYT [link] [comments]  ( 44 min )
    ✨I made a story script and Vtuber like character using 100% A.I besides editing💕✨
    submitted by /u/Recent-Dealer-5844 [link] [comments]  ( 45 min )
    Top A.I. Powered Tools Not Named ChatGPT (2nd)
    submitted by /u/BackgroundResult [link] [comments]  ( 44 min )
    Move over, ChatGPT: Israeli start-up AI21 Labs' AI to cite sources
    submitted by /u/yaitz331 [link] [comments]  ( 43 min )
    FREE Midjourney Rival Using Stable Diffusion Under The Hood!
    submitted by /u/PuppetHere [link] [comments]  ( 43 min )
    Text-to-Audio Diffusion, by flavio schneider
    Text-conditional latent audio diffusion that can generate multiple minutes of music from a textual description. See link for samples. submitted by /u/Sea_Emu_4259 [link] [comments]  ( 44 min )
    How can GPT ever compete with search databases economically?
    A GPT3 query costs at least 5c, and an google search costs 0.05 cents, that's 100 times less. Perhaps GPT3 will always be a paid service, because advertising wouldn't be profitable? I'm thinking that the cost of GPT3 will be slashed by 9 and then it will stabilize at about 5 times more expensive than databases... because the current system will be slashed by 20 times and the data volume of the NLP will grow as well. Perhaps it will cost like 10 dollars per year for a subscription to an AI query app? submitted by /u/MegavirusOfDoom [link] [comments]  ( 48 min )
    Is there an AI tool that mines your gmail and organizes things - eg sorts out your purchases and tracks warranty and more?
    Is there an AI tool that mines your gmail and organizes things - eg sorts out your purchases and tracks warranty and more? submitted by /u/dreameh [link] [comments]  ( 45 min )
    Just tested You.com AI powered Chat Box
    submitted by /u/Sphagne [link] [comments]  ( 46 min )
    A photo created by an AI
    submitted by /u/NorthTs [link] [comments]  ( 43 min )
  • Open

    Making-of for Boston Dynamic's latest Atlas demo (gripping, placing, & throwing objects; jumps & flips)
    submitted by /u/gwern [link] [comments]  ( 53 min )
    Question about optimazation problem
    Hello, I am learning currently DL, but in work I have the opportunity ( if I select to do it, I lack the theoritical background) to create an AI. I am working as analog IC engineer, in RF circuits we have transformers which they match the Zout (Output impedance) of a block to the Zin of the next block. The transformer in schematic level is comprised by 2 capacitors, 2 inductors and the coupling factor, the output which we want to have a flat gain at the freq range that we want e.g. 76 -81 GHz. Currently the rf engineers work from experiance the transfomers and they start trimming to match the Zin = Zout. because we have a lot of transformers and I think this job will be better/faster to make an AI I was thinking about RL, but I lack the experiance. So I want to ask if someone has any sources/recommendation to study and do some examples with similar objective. ​ thank you in advance submitted by /u/InvokeMeWell [link] [comments]  ( 54 min )
    I got a project that focuses on marketing and it was suggested by my senior in work that I should try reading about MAB. Aside from MAB, is there any alternatives that covers the ground of RL?
    Basically, I'm a data scientist in an AU company, most of the time we can get away it with simple hypothesis testing in this sort of stuff , linear programming and probably machine learning approaches, but the team want to do more than that so we want to go bonkers and try MAB, aside from MAB what stochastic method I should read/study so I can contribute in our current project. submitted by /u/noodlepotato [link] [comments]  ( 62 min )
    PPO with Transformer or Attention Mechanism
    I am interested in testing PPO with an attention mechanism from a psychological perspective. I was wondering if someone has successfully customized the stable_baselines3 with an attention mechanism submitted by /u/partyjunk [link] [comments]  ( 55 min )
    What does it mean if your actor is converging and your critic is diverging?
    I am trying to train an agent with DDP3+SWA. The actor's loss is going up, which I believe is a good thing because the loss is a negative expected reward, but the critic's loss is also going up so it's diverging. ​ Does anyone have any ideas about what could cause this to happen? submitted by /u/rawrzapan [link] [comments]  ( 53 min )
  • Open

    [R] Summary of developments in ML in 2022
    Google Research has a blog post of advances in ML over the last year. It obviously focusses on stuff Google Research has been involved in, but from such a big research group, thats pretty much everything. Here it is It's a good way current if you don't have time to read every paper! (Note that some sections aren't yet published) submitted by /u/londons_explorer [link] [comments]  ( 57 min )
    [P] We made an image de-identifier using Stable Diffusion!
    We combined image captioning using CLIP and image generation using the Hugging Face Stable Diffusion model to create an image de-identifier modeled after the game of telephone, Imafake! All you have to do is upload an image, convert it to a caption, then convert that caption to an image with a few clicks! You can also play with the parameters of the diffusion model depending on how gnarly you want your resulting image to be. And caution, they can get rather gnarly, but that’s what makes it fun :) Thoughts and your own generated images welcome!! https://preview.redd.it/z818pd8fivca1.jpg?width=1276&format=pjpg&auto=webp&s=54ed1a70e6ba7aa52fb662d9213e46ed5f559e5a submitted by /u/Djinn_Tonic4DataSci [link] [comments]  ( 57 min )
    [P] MNIST Clock - Generating MNIST digits on the fly in your browser
    ​ MNIST CLOCK Project https://github.com/tecbar/mnist-clock Live demo https://tecbar.github.io/mnist-clock/ (it can load for a while, because it needs to download ONNX runtime) Description Hey, this is my pet project. I trained a very simple model on MNIST dataset. The task is you input a digit and it can generate output image representation of that digit. Each time it generates a little bit different digit, because of the applied noise - actually the digit vector is applied on gaussian noise. I didn't know what to do with it so I exported the model to ONNX and used ONNX web runtime to arrange the digits in a clock - so basically everything on the live demo site is running in your browser and the clock is refreshed about 20 times per second (which was arbitrary choice). The training procedure was really simple - instead of predicting a label based on an image it tries to predict an image based on a label. Here is PyTorch implemetation. It works just fine with only two linear layers. Problems The generated digits are blurry, I guess this is because I didn't use any GAN or VAE based architecture, so the model has no idea about anything basically. ​ Model ​ https://preview.redd.it/n2lsobiigvca1.png?width=438&format=png&auto=webp&s=565bda13fd4f0d5b60cb9a71828053c726ee5301 submitted by /u/tecbar [link] [comments]  ( 58 min )
    [D] [N] Book: Multimodal Deep Learning - 239 Pages! - Matthias Aßenmacher et al
    In my opinion a must read because Multimodal Deep Learning is the future! Also because papers like this: https://arxiv.org/abs/2301.03728 show that Multimodular models significantly outperform unimodular models! Book: https://arxiv.org/pdf/2301.04856.pdf Github: https://github.com/slds-lmu/seminar_multimodal_dl Abstract: This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in which one modality is utilized to enhance representation learning for the other. To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced. Finally, we also cover other modalities as well as general-purpose multi-modal models, which are able to handle different tasks on different modalities within one unified architecture. One interesting application (Generative Art) eventually caps off this booklet. https://preview.redd.it/vb9lycxmfvca1.jpg?width=641&format=pjpg&auto=webp&s=6b25e0d051d5cb5c3ec0117bb383e59c23c2f984 submitted by /u/Singularian2501 [link] [comments]  ( 55 min )
    [D] Automated Extraction of Building Geometry
    I need to figure out a way to automatically create a 2D one-line drawing given a point cloud of a building. I figure this is the rough workflow of that operation, but I need to define this workflow with a much higher resolution to acquire the right tools and talent for the project. Is this a suitable application for Machine Learning? If you have any insight or ideas to share that would be very much appreciated, thanks! https://preview.redd.it/4hv1ba5h0vca1.png?width=2160&format=png&auto=webp&s=f8a9e6ae45a621d72fedceeefd7a2b06599577aa submitted by /u/EducationalLayer1051 [link] [comments]  ( 53 min )
    [R] A simple explanation of Reinforcement Learning from Human Feedback (RLHF)
    ​ https://preview.redd.it/k3ims1d6zuca1.png?width=2324&format=png&auto=webp&v=enabled&s=4f4bbe410508bdd4c45f45e55dd5c1ea0fcb5fcc You must have heard about ChatGPT. Maybe you heard that it was trained with RLHF and PPO. Perhaps you do not really understand how that process works. Then check my Gist on Reinforcement Learning from Human Feedback (RLHF): https://gist.github.com/JoaoLages/c6f2dfd13d2484aa8bb0b2d567fbf093 No hard maths, straight to the point and simplified. Hope that it helps! submitted by /u/JClub [link] [comments]  ( 57 min )
    [R] Call for Papers: 2nd International Symposium on the Tsetlin Machine
    ​ CfP ISTM 2023 Calling all machine learning researchers to contribute to or participate in the 2nd International Symposium on the Tsetlin Machine @ Newcastle upon Tyne. Please consider submitting your original, high-quality research works on any emerging ML hardware, software, application, or algorithmic topics. The emerging paradigm of Tsetlin machines provides a fundamental shift from arithmetic-based to logic-based machine learning. At the core, finite-state machines, based on learning automata, learn patterns using logical clauses, and these constitute a global description of the task learnt. In this way, the Tsetlin machine introduces the concept of logical interpretable learning, where both the learned model and the process of learning are easy to follow and explain. As a result, it reduces the expertise needed to apply ML techniques efficiently in various domains. The paradigm has enabled competitive accuracy, scalability, memory footprint, inference speed, and energy consumption across diverse tasks, including classification, convolution, regression, natural language processing (NLP), and speech understanding. https://istm.no submitted by /u/olegranmo [link] [comments]  ( 60 min )
    [D] Do you know of any model capable of detecting generative model(GPT) generated text ?
    I'm looking to detect spams generated by generative models (especially gpt). But all the ones I tried fail miserably ... submitted by /u/CaptainDifferent3116 [link] [comments]  ( 62 min )
    [R] Researchers out there: which are current research directions for tree-based models?
    Hi everybody, I've been skimming this paper since yesterday and was once again impressed by the expressiveness and practicality of tree-based models. I wondered what current research directions are in the field and what novel ideas have been presented in the last years - beyond improving performances. Examples may include better explainability, online learning, splitting criteria, enhanced or customizable loss functions, adding structure or constraints, shortcomings .... submitted by /u/BenXavier [link] [comments]  ( 59 min )
    [P] AI for Materials community
    Hey everyone, working on getting started an open and collaborative community/lab at intersection of ML/AI and materials science. One big reason is because it’s a neglected area with lots of potential with generative modeling for new discoveries. A small roadmap is we want to have intro talks on the topic to ramp members up, talks from leading researchers, of course we will be training models, trying to create larger datasets, and hopefully getting access to synthesis our findings. If this sounds interesting to you checkout the website at https://ai4mlab[dot]com and consider joining! Thanks! submitted by /u/theredditbrowser1 [link] [comments]  ( 58 min )
    [R] tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation (480 tasks+ sota encoder)
    submitted by /u/Jean-Porte [link] [comments]  ( 60 min )
    [D] How much can you add/change in a camera ready conference paper?
    Fingers and toes crossed I might have a paper accepted at ICLR, and I'm wondering how much I can add/change in the camera ready version. Typos and exposition for clarity, I assume are fine to add/change. And in some cases I have seen meta-reviews say "please address comments XYZ in the camera ready version", so I assume there is a lot of leeway. But in the absence of such a comment or comments in particular about something you should change, is it okay to change/add stuff? And if so to what degree (while keeping to the page limit). submitted by /u/tfburns [link] [comments]  ( 61 min )
  • Open

    Gaining real-world industry experience through Break Through Tech AI at MIT
    A new experiential learning opportunity challenges undergraduates across the Greater Boston area to apply their AI skills to a range of industry projects.  ( 9 min )
  • Open

    Chatbots in Healthcare [Part 2]
    In April 2017 I wrote this story on the potential use of chatbots in healthcare: https://medium.com/p/984fc23e0410 . It got over 3.5K…  ( 22 min )
    The 10 Most Powerful AI Software Products in 2023
    Businesses are moving towards AI Software Products. In fact, a recent study proves this claim by saying that nine out of ten companies…  ( 18 min )
    AI Writing Tools for Creative Writing and Fiction: Unleash Your Imagination and Write Like a Pro
    No content preview
    Multicollinearity: A Guide to Understanding and Managing the Problem in Regression Models
    Multicollinearity is a common problem that might happen in multiple regression analysis, where two or more predictor variables are highly…  ( 11 min )
    Designing great AI products — Building trust
    The following post is an excerpt from my book ‘Designing Human-Centric AI Experiences’ on applied UX design for Artificial intelligence.  ( 13 min )
    The massive disruption nobody is talking about, yet.
    Bold prediction: the evolution of machine learning models (GPT, Gopher, …) combined with the ubiquity of messaging apps (WhatsApp… Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 11 min )
    Is AI going to take my Job?☹️
    Artificial intelligence (AI) and its various applications, such as ChatGPT (if you don’t know about Chat GPT, check out my post), are…  ( 11 min )
  • Open

    Simple neural networks outperform the state-of-the-art for controlling robotic prosthetics
    submitted by /u/keghn [link] [comments]  ( 67 min )
    Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet
    submitted by /u/nickb [link] [comments]  ( 68 min )
    Project advice - Deep Learning 3D models - python libraries, GPU memory management, data structuring
    I am working with 64x64x64 voxel arrays and am running into significant problems with GPU memory management. I am using TensorFlow and have an NVIDIA GeForce RTX 4080 MSI Ventus edition with 16GB of memory (purchased using research grant funding... it's sitting in a hacked together eGPU setup lol). It performs beautifully on 32x32x32 data but I can't even get started with the larger data format. I have tried limiting GPU data utilization per process, as per this post and limiting memory growth, as per this post (Ctrl+F "second option"). I have 64GB of RAM so I can fit the data into memory (even though I know that's not efficient) and was trying to put that data in a TensorFlow Dataset object, in which, according to the docs, "iteration happens in a streaming fashion, so the full dataset do…  ( 62 min )
    Two guys in London working in AI looking for volunteers to join our team in educating the public on AI
    We’re 2 Brits who work in AI. We believe AI is likely to have a huge and mostly positive impact on society but that not many people realise this or understand how it will impact everyday life. There is a lack of places online right now clearly explaining the changes AI will bring, i.e., how will AI change the experience of shopping in stores in the next 10 years or how will AI change video games in the next 10 years. We are somewhat well positioned to collate the current views on likely future changes across most areas and are in the process of starting a website and perhaps video channel which will cover how AI is likely to impact people over the next 10 years in different areas of life (movies, sports, bars, banking, schools, hospitals etc). We are looking for people to help us research, write and make videos on this cause – which we think is important to help ensure that voters don’t misunderstand AI. Alex – researches, writes, and records the audio Seb - does the video and audio editing We thought we’d put the word out and ask if anyone else would like to volunteer to help create content too. No special skills needed. Getting involved would be as easy as PMing me, hearing about how we’ve done things so far and then saying what you might be interested in helping with. Maybe thinking about ideas for topics or getting involved in research and/or article writing. We are UTC-0 but open to all. submitted by /u/TheOptimisticRogue [link] [comments]  ( 60 min )
  • Open

    Google Research, 2022 & Beyond: Language, Vision and Generative Models
    Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the Google Research community Today we kick off a series of blog posts about exciting new developments from Google Research. Please keep your eye on this space and look for the title “Google Research, 2022 & Beyond” for more articles in the series. I’ve always been interested in computers because of their ability to help people better understand the world around them. Over the last decade, much of the research done at Google has been in pursuit of a similar vision — to help people better understand the world around them and get things done. We want to build more capable machines that partner with people to accomplish a huge variety of tasks. All kinds of tasks. Complex, information-seeking tasks. Creative t…  ( 112 min )
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    Ever-Successful vs. Never-Successful: What the NFL Has to Teach Us About Managing Agile Enterprises, Part I
    A few days ago, I responded to a post on LinkedIn about how Google seems to always find a way to keep ahead of the pack, even when someone of importance leaves the company.  It occurred to me that NFL teams have to adapt and remake themselves from season to season, as players and coaches… Read More »Ever-Successful vs. Never-Successful: What the NFL Has to Teach Us About Managing Agile Enterprises, Part I The post Ever-Successful vs. Never-Successful: What the NFL Has to Teach Us About Managing Agile Enterprises, Part I appeared first on Data Science Central.  ( 22 min )
    A Practical Guide to Using Computer Vision for Business Growth
    Isn’t it fascinating how our brain processes the vast amounts of information that we receive throughout the day? Our sensory organs convert information into stimuli as they process the information they receive. Complex processes like recognizing and detecting objects require only a split second of the brain’s attention. A computer can replicate human vision using… Read More »A Practical Guide to Using Computer Vision for Business Growth The post A Practical Guide to Using Computer Vision for Business Growth appeared first on Data Science Central.  ( 24 min )
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    Sequoia Capital’s Pat Grady and Sonya Huang on Generative AI
    For insights into the future of generative AI, check out the latest episode of the NVIDIA AI Podcast. Host Noah Kravitz is joined by Pat Grady and Sonya Huang, partners at Sequoia Capital, to discuss their recent essay, “Generative AI: A Creative New World.” The authors delve into the potential of generative AI to enable Read article >  ( 4 min )
    Roll Model: Smart Stroller Pushes Its Way to the Top at CES 2023
    As any new mom or dad can tell you, parenting can be a challenge — packed with big worries and small hassles. But it may be about to get a little bit easier thanks to Glüxkind Technologies and their smart stroller, Ella. The company has just been named a CES 2023 Innovation Awards Honoree for Read article >  ( 6 min )
    Artist Zhelong Xu Brings Chinese Zodiac to Life for Lunar New Year This Week ‘In the NVIDIA Studio’
    To celebrate the upcoming Lunar New Year holiday, NVIDIA artist Zhelong Xu, aka Uncle Light, brought Chinese zodiac signs to life this week In the NVIDIA Studio — modernizing the ancient mythology in his signature style.  ( 7 min )
  • Open

    Converting between barycentric and trilinear coordinates
    Barycentric coordinates describe the position of a point relative to the three vertices of a triangle. Trilinear coordinates describe the position of a point relative to the three sides of a triangle. It’s surprisingly simple to convert from one to the other. Why should this be surprising? Because the distance from a point to a […] Converting between barycentric and trilinear coordinates first appeared on John D. Cook.  ( 5 min )

  • Open

    [D] Sport outcome predictions
    Hi all, I'm wondering why predicting outcomes of sport events like football or horse racing hasn't been achieved with machine learning tools? I guess historical data is abundant for back testing. What is so challenging with this problem? submitted by /u/proudm0 [link] [comments]  ( 56 min )
    [P] Image classification
    Hi guys, I am currently working on a DL problem where I have to classify an image dataset from Kaggle into 5 classes. The first task is to train a NN from scratch that overfits the data and then I have to modify the training process so that the network is trained without overfitting for more than double the number of epochs in the first task, keeping the same architecture, number of training images, optimizer, batch size and learnnig rate I used . I am allowed to use any architecture (resnet, alexnet, moblinet etc) or a custom model. As of now, I have tried to use resnet18 and the model overfits the data. For the second task, I apply data augmentation techniques to the training set, but the model still overfits and I am not able to find any solution. One thing I noticed, is that the validation loss at the first epochs is way lower than the training loss and it is saturating. I also tried to use a mobilenet but it stil overfits no matter how many or what augmentations I use. Can anyone recommend a solution ? submitted by /u/grisp98 [link] [comments]  ( 68 min )
    [D] Why aren't we all using linear transformers?
    There's a bunch of them - Linformer, Longformer, Performer, Nystromformer, Big Bird, etc etc. Plus a bunch more that have similar goals but don't necessarily aim for linear complexity, like memory-augmented transformers. As far as I know, none of them have really seen much use. Even for image problems, which have very long input sizes, people are using regular transformers with tokenization schemes. Am I wrong? Are they actually good, or are at least some of them better than regular transformers? If not, what's wrong with them? Do they have lower accuracy? Are they slower to train? submitted by /u/currentscurrents [link] [comments]  ( 56 min )
    [D] RLHF - What type of rewards to use?
    Hey everyone, just saw the great presentation of Nathan Lambert on Reinforcement Learning from Human Feedback and wanted to try to do some RLHF on my language model.To do this, first I need to create an experience where I collect reward scores to train the reward model. My question is: what rewards work best? Simply 👍/👎? A scale of 1-5? Ranking 4 different model outputs? There are a lot of options and I don't know which one to choose. submitted by /u/JClub [link] [comments]  ( 52 min )
    [P] Need advice on inventory planning for my capstone project.
    Hello everyone. I'm currently doing a capstone project at my university. In this project I'm working with a fashion company to address their inventory issue. They have big problems on pin pointing demand for specific products, so often end up over buying their inventory. My capstone instructor suggested we do a cluster analysis to see which products have similar demands. I'm also posting here to see what approach you guys would take to address this inventory issue submitted by /u/dingdong1882 [link] [comments]  ( 52 min )
    [R] Forcing GPT-N To Be Honest Without Supervision
    In his paper Discovering Latent Knowledge In Language Models (previous discussion), Collin Burns explains how you can train a probe on the hidden states of a language model that would classify if the model thinks an input his true or false, without access to ground truth labels. In a recent interview, he discusses high-level arguments for why this approach might work at scale on making GPT-N honest. He also talks more generally about his approach to doing research. submitted by /u/MuskFeynman [link] [comments]  ( 56 min )
    [R]: 15-step framework to analyze your chatbot and designate improvement steps
    Are you sure your Conversational AI solution is on the right path? Our chatbot evaluation metrics pinpoint if your solution leveraging the best of the industry’s leading practices, meeting user expectations, and fully taking advantage of the available technology to ensure frictionless and efficient experiences. https://masterofcode.com/chatbot-analysis-framework submitted by /u/Marinuch [link] [comments]  ( 57 min )
    [P] RWKV 14B Language Model & ChatRWKV : pure RNN (attention-free), scalable and parallelizable like Transformers
    Hi everyone. I am training my RWKV 14B ( https://github.com/BlinkDL/RWKV-LM ) on the Pile (332B tokens) and it is getting closer to GPT-NeoX 20B level. You can already try the latest checkpoint. https://preview.redd.it/7ycdftmjvmca1.png?width=1174&format=png&auto=webp&s=860a41193f1a254299d48a173756ecd66ccbc75b RWKV is a RNN that also works as a linear transformer (or we may say it's a linear transformer that also works as a RNN). So it has both parallel & serial mode, and you get the best of both worlds (fast and saves VRAM). At this moment, RWKV might be the only pure RNN that scales like usual transformers for language modeling, without using any QKV attention. It's great at preserving long context (unlike LSTM). Moreover, you get smooth spike-free carefree training experience (bf16 & Adam): https://preview.redd.it/0g3lrg6mvmca1.png?width=871&format=png&auto=webp&s=b4de1af4831ec359079cf99c41df8aa9591d48b0 As a proof of concept, I present ChatRWKV ( https://github.com/BlinkDL/ChatRWKV ). It's not instruct-tuned yet, and there are few conversations in the Pile, so don't expect great quality. But it's already fun. Chat examples (using slightly earlier checkpoints): https://preview.redd.it/zyqni6bpvmca1.png?width=1084&format=png&auto=webp&s=038fd2eab524c36d8aa2a8720a2caa3eb420df5b https://preview.redd.it/xhje4j7qvmca1.png?width=1200&format=png&auto=webp&s=7e8597d2370f9f87230560dac7f5439520384dd9 And you can chat with the bot (or try free generation) in RWKV Discord (link in Github readme: https://github.com/BlinkDL/RWKV-LM ). This is an open source project and let's build together. submitted by /u/bo_peng [link] [comments]  ( 65 min )
    [D] Unlocking the Potential of ChatGPT: A Community Discussion
    OpenAI's announcement of the release of the ChatGPT API has many of us excited about the potential applications and implications of this powerful language model. It has the ability to revolutionize the way we interact with technology and solve a wide range of problems. As a community, let's discuss the possibilities. What are some unique and innovative ways ChatGPT could be utilized? Are there any particular industries or markets that you think could benefit from the integration of ChatGPT? Let's share our thoughts and ideas, and explore the potential of this technology together. It's always exciting to see how advancements in AI can improve our world. ​ This post was written by ChatGPT submitted by /u/North-Ad6756 [link] [comments]  ( 56 min )
    [D] Are there any results on convergence guarantees when optimizing NNs?
    Given a function in some space, I have literature results that say, the function can theoretically be approximated by a Neural Network of such complexity with so many layers, of such width, with this specific given activation function. OK, so theoretically, there is a set of weights and biases that will result in a pretty good approximation of my function. Now the question is, how do I know that given an optimization method, for example stochastic gradient descent, I will actually reach this minimum or near enough to it, in so many training steps, or even at all? I attended a talk last year in which one speaker claimed that due to the way stochastic gradient descent works, it could be that some minimums are never reachable from some initialization states no matter how long one trains. Unfortunately I cannot find what paper/theorem he was referring to. I am interested in results related to this question. submitted by /u/Dartagnjan [link] [comments]  ( 57 min )
    [N] Getty Images is suing the creators of AI art tool Stable Diffusion for scraping its content
    From the article: Getty Images is suing Stability AI, creators of popular AI art tool Stable Diffusion, over alleged copyright violation. In a press statement shared with The Verge, the stock photo company said it believes that Stability AI “unlawfully copied and processed millions of images protected by copyright” to train its software and that Getty Images has “commenced legal proceedings in the High Court of Justice in London” against the firm. submitted by /u/Wiskkey [link] [comments]  ( 63 min )
    [D] I made a comprehensive comparison of YOLO(N+1) vs YOLO(N)
    The faster the video - the better Yolo is! https://www.linkedin.com/posts/maltsevanton_basically-any-yolon1-vs-yolon-comparison-activity-7021021466506768384-y7Se?utm_source=share&utm_medium=member_desktop submitted by /u/Wormkeeper [link] [comments]  ( 59 min )
    [D] Is it possible to update random forest parameters with new data instead of retraining on all data?
    I'm building some random forest models in sklearn using a dataset that updates daily. I want to take advantange of the new stream of data which could indicate changes in the X-y relationship, however I've also found that my model performs better with more data. The problem is that it takes a seriously long time to run (dataset is around 250000 rows and 50 features). Is there an approach where one builds the model at the beginning of the data stream, and then updates the parameters with new data as it arrives, instead of continuously retraining the model on the entire dataset for every day? Many thanks! submitted by /u/monkeysingmonkeynew [link] [comments]  ( 56 min )
    [P] featureimpact: A Python package for estimating the impact of features on ML models
    I made this little python package a while ago but realized I never shared it here. Maybe it's useful to you: https://github.com/bloomen/featureimpact submitted by /u/cblume [link] [comments]  ( 56 min )
    [D] Study to be specialized or generalized DS/MLE for freelancing jobs?
    Study to be specialized or generalized DS/MLE for freelancing jobs? Hello. I'm MLE (Machine Learning Engineer) and I'm currently thinking to do ML freelancing jobs (or gigs) in the future. One idea I had is to just focus on studying recommendation systems ( that is, be a specialized data scientist) or try to study and solve every type of ML problems (time series, NLP, etc). What do you think? submitted by /u/Waste_Necessary654 [link] [comments]  ( 59 min )
    [D] ModuleNotFoundError: No module named 'fbprophet'
    I'm having this problem while trying to import the auto_ts library, any idea on how to fix this? submitted by /u/PowerfulGuidance8378 [link] [comments]  ( 55 min )
  • Open

    Join us today at 11pm EST for this week's (free) seminar session of the 9-part series on Neural Networks Architectures by Pablo Duboue!
    Happening tonight at 11 pm EST on the Learn AI Together Discord server. This week's seminar session is about Popular Network Architectures. More precisely, Pablo will present... Multi-task learning. Siamese Networks. Generative Adversarial Networks (GAN). Style Transfer. Disentangled Representation Learning. Rich Caruana (1997). “Multitask learning”. In: Machine learning 28.1, pp. 41–75 Ting Gong et al. (Sept. 2019). “A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks”. In: IEEE Access PP, pp. 1–1. DOI : 10.1109/ACCESS.2019.2943604 Jane Bromley et al. (1993). “Signature verification using a "siamese" time delay neural network”. In: Advances in neural information processing systems 6 Ian Goodfellow, Jean Pouget-Abadie, et al. (2014). “Generative Adversarial Nets”. In: Advances in Neural Information Processing Systems. Ed. by Z. Ghahramani et al. Vol. 27. Curran Associates, Inc. Xi Chen et al. (2016). “Infogan: Interpretable representation learning by information maximizing generative adversarial nets”. In: Advances in neural information processing systems 29 Leon A Gatys, Alexander S Ecker, and Matthias Bethge (2016). “Image style transfer using convolutional neural networks”. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2414–2423 Sounds interesting? Join our Discord community to attend the event and future ones: https://discord.gg/c6kbhNdmmA?event=1062742110295572500 submitted by /u/OnlyProggingForFun [link] [comments]  ( 52 min )
    Sugandha Sharma, MIT: On biologically inspired neural architectures, how memories can be implemented, and control theory
    Here is a podcast episode with Sugandha Sharma from MIT where we discuss how memories can be implemented, control theory, and much more! submitted by /u/thejashGI [link] [comments]  ( 44 min )
    Why Falling in Love with AI is a Dangerous Illusion — The Limitations and Harms of Artificial…
    submitted by /u/SupPandaHugger [link] [comments]  ( 48 min )
    AI to analyze data/spreadsheets, any thoughts?
    I came across this AI chatbot recently, where you can ask quantitative/qualitative questions about your data/spreadsheet in English. It felt like if ChatGPT and Excel had a baby LOL It worked for my qualitative survey data -- shocking... Do you know any other ones like this? Any thoughts in general? submitted by /u/AdDry9057 [link] [comments]  ( 46 min )
    How can AI help people in developing countries?
    I am planing to take part in a AI contest, so I am collecting ideas about my project. I think my project will be more recognized if it will have to do something with helping people in developing countries, so my question is: how can AI help people in developing countries? submitted by /u/zazabuzala [link] [comments]  ( 49 min )
    AI created content.
    Should we know what content was created using AI? What should we do to develop AI detection tools or any other ideas? submitted by /u/Andrey_Taran [link] [comments]  ( 44 min )
    Trullion to release AI bookkeeping software
    Trullion, a leading accounting automation platform, has launched two new AI-enabled modules, Revenue by Trullion and Audit by Trullion, to modernize and digitize the process of accounting. The first module, Revenue by Trullion, uses AI to synchronize customer relationship management (CRM), billing, and contract data into a single platform for internal and external stakeholders, allowing for ERP entries, disclosure reports, and advanced reporting to be generated quickly and accurately. The second module, Audit by Trullion's Test of Details workflow, uses AI to extract ERP/General Ledger (GL) files and instantly validate them against source data, such as invoices, PDFs, and other client sources. Found on https://deathtohumans.com/post/openai-monetizes-chatgpt submitted by /u/crowb1rd [link] [comments]  ( 46 min )
    Are you sure your Conversational AI solution is on the right path? 🤔 15-step framework to analyze your chatbot and designate improvement steps
    submitted by /u/Marinuch [link] [comments]  ( 48 min )
    The AI Lawyer Preparing To Defend a Real US Court Case for the First Time Ever Has Terrible Reviews
    submitted by /u/HODLTID [link] [comments]  ( 44 min )
    Two AI workers in London looking for volunteers to join our team in educating the public on AI
    We’re 2 Brits who work in AI. We believe AI is likely to have a huge and mostly positive impact on society but that not many people realise this or understand how it will impact everyday life. There is a lack of places online right now clearly explaining the changes AI will bring, i.e., how will AI change the experience of shopping in stores in the next 10 years or how will AI change video games in the next 10 years. We are somewhat well positioned to collate the expert views on likely future impacts and are in the process of starting a website and YouTube channel which will cover how AI is likely to impact people over the next 10 years in different areas of life (movies, sports, bars, schools, hospitals etc). We are looking for people to help us research, write and make videos on this cause – which we think is important to help ensure voters pressure the government to develop AI safely. · Alex – researches, writes, and records the audio · Seb - does the video and audio editing We thought we’d put the word out and ask if anyone else would like to be involved to see if you might be interested too. Getting involved would be as easy as PMing me, hearing about how we’ve done things so far and then saying what you might be interested in helping with. Maybe thinking about ideas for topics or getting involved in research and/or article writing. submitted by /u/TheOptimisticRogue [link] [comments]  ( 48 min )
    🚀Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
    submitted by /u/oridnary_artist [link] [comments]  ( 43 min )
    Microsoft’s Azure OpenAI Service Gets A Boost With ChatGPT.
    submitted by /u/liquidocelotYT [link] [comments]  ( 44 min )
    AI title for human creators
    does anyone think at any point, AIs will/would (or some independent ones) would call us, their human creators, Rule Makers? Instead of the Cybernetic Gods are are coming to be. submitted by /u/jonfxm1891 [link] [comments]  ( 50 min )
    If ChatGPT could have a superpower, what would it be?
    submitted by /u/Imagine-your-success [link] [comments]  ( 44 min )
    Will ai generated content fill up the internet with fake information?
    Ai doesn’t always produce inaccurate or fake content but most generators can’t tell the difference in their output. And if AI is trained on more and more synthetic ext can this become an issue? How can you avoid such cannibalistic practice? What tools are there to spot whether content is generated or even inaccurate? It’s not like generated text can be traced with markers like proprietary synthetic molecules, assuming image or audio could. submitted by /u/daaavide [link] [comments]  ( 49 min )
    Enjoy Text-Adventure Games Too? DREAM WITH ME.
    I present the FULL prompt for DREAMWORD - my GPT AI Adventure Game for the masses. Enjoy. Feel free to manipulate this prompt to your liking - have fun. "Generate and enact a satire of an intuitive, complex, story-telling, text-adventure game set in a randomized "Absurdist"/ "Psychedelic" style dream-world. Describe the unique game setting in the beginning. The "player" (being the user) is born at the start and dictates through text any actions it chooses. Each input from the user represents one year of life. The Game ends when life ends. The Main cast will be random pop-culture icons. The situation's presented are dictated to the user. The game will randomize every new situation and experience, use roll-playing and text-entry adventure mechanics and be a satirical, stylish, funny, mystic, twisted, surreal, lynchian, lovecraftian, "earthbound-like", discworld-esque, mythology-based mystery-horror-adventure. The character will be assessed with each action and be gifted a related persona archetype based upon it's choices and state of the persona upon the point the character ends life. There should be over 50 text input interactions from the user before the game naturally ends with a moral. The game can be ended by the user typing "end" and will be given an archetype. Start." https://chat.openai.com/chat submitted by /u/Principal-Goodvibes [link] [comments]  ( 47 min )
    DeepMind To Launch ChatGPT Rival Sparrow Soon
    submitted by /u/vadhavaniyafaijan [link] [comments]  ( 51 min )
    Druid vs Koios for AI
    submitted by /u/roblox22y [link] [comments]  ( 44 min )
  • Open

    "Neural probabilistic motor primitives for humanoid control", Merel et al 2018 {DM}
    submitted by /u/gwern [link] [comments]  ( 54 min )
    Why is 'reward shaping' neglected?
    There are 6 levers. All 6 must be activated to recover reward. Scenario 1: Pull = -1 Outcome: Agent commits suicide (in order to minimize the negative rewards accumulating). ​ Scenario 2: Pull = +1 Outcome: Agent pulls the same lever forever. ​ Scenario 3: Pull = 0 i.e. Sparse reward Outcome: Agent doesn't accidentally pull all 6 often enough; doesn't learn anything useful. ​ No matter what algorithm (or how groundbreaking it is), Authors rarely justify their choices with respect to rewards. I don't mean to be able to compare and benchmark. I mean fundamentally, what's driving learning in the scenario is never substantiated. Analogy: Exact value of a hyperparameter is irrelevant, but having hyperparameters at all should and often is discussed. Am I missing something? submitted by /u/XecutionStyle [link] [comments]  ( 57 min )
    What's the best "Non-Black Box" framework for SOTA algorithms?
    Hi all, In my research I usually implement the algorithms from scratch, but given that this not only takes a lot of programing time and may introduce bugs without us knowing, I would like to know that are the best hackable frameworks out there. I would like a good framework that is easy to tinker with the algorithms' code and have good agent interfaces (agent.act, agent.step, etc...) and does not encapsulate everything (like god forbid, fucking stable-baselines and others that do things like model.learn(env)). What are your recommendations? submitted by /u/HyperionTone [link] [comments]  ( 54 min )
    The RL meetup is Online now.
    Hi, Based on feedbacks and messages that I received, the RL meetup is now online. The purpose of the meetup is having a community to gather and discuss topics/papers. Something other than Discord servers or slack channels. So If you have a topic/paper that you like to discuss, please message me to be the host of one of sessions. https://www.meetup.com/reinforcement-learning/events/290997718/?isFirstPublish=true Thanks, submitted by /u/Express-Incident-113 [link] [comments]  ( 53 min )
    Is it legit to design the action space like this?
    Hi, I see in lot of example that action spaces are defined as torques, efforts and desired velocity values for a robot. Assuming the robot has 5 degree of freedom, i.e., 5 action values to control the robot. Is it legit to extend this action space to 6 to manipulate the rest of 5 action values? For example, if the 6. action value is bigger than 0.5, then the rest of action values should not be applied to the agent etc. Do you know any research paper that has similar approach? submitted by /u/Fun-Moose-3841 [link] [comments]  ( 54 min )
  • Open

    DSC Weekly 17 January 2023 – The Creative Spark in AI
    Announcements The Creative Spark in AI The idea that AI can generate art that can mimic human artists came to the forefront of discussions about AI ethics in 2022. There are undoubtedly many legal and ethical issues to tackle in those cases. If a model is trained on thousands of examples of a specific artist’s… Read More »DSC Weekly 17 January 2023 – The Creative Spark in AI The post DSC Weekly 17 January 2023 – The Creative Spark in AI appeared first on Data Science Central.  ( 20 min )
    Python for Business Analytics: Top Benefits
    Companies and businesses today need modern programming tools in order to build the many, many advanced tools and solutions they need to keep their operations running seamlessly. So, what do companies use to build business analysis solutions? Python! Why? Because it is easy to learn, offers high-quality community support, etc. Python is an all-purpose programming… Read More »Python for Business Analytics: Top Benefits The post Python for Business Analytics: Top Benefits appeared first on Data Science Central.  ( 19 min )
    Mobile Biometric Solutions: Game-Changer in the Authentication Industry
    Mobile-based biometrics is a technology that allows users to authenticate themselves and access services using unique physical characteristics such as fingerprints, facial recognition, and iris scans. These biometric authentication methods have become increasingly popular in recent years due to their convenience and security. There are several types of smartphone-based biometrics technology currently available, including: Emerging… Read More »Mobile Biometric Solutions: Game-Changer in the Authentication Industry The post Mobile Biometric Solutions: Game-Changer in the Authentication Industry appeared first on Data Science Central.  ( 20 min )
    What to make of Deepmind’s Sparrow:  Is it a sparrow or a hawk?
    What to make of Deepmind’s Sparrow:  Is it a sparrow or a hawk? ie a chatGPT killer Recently, Demis Hassabis from DeepMind has been urging caution (DeepMind’s CEO Helped Take AI Mainstream. Now He’s Urging Caution Time magazine/Davos) DeepMind also announced a new chat engine called Sparrow – supposedly a chatGPT killer  Sparrow is not… Read More »What to make of Deepmind’s Sparrow:  Is it a sparrow or a hawk? The post What to make of Deepmind’s Sparrow:  Is it a sparrow or a hawk? appeared first on Data Science Central.  ( 19 min )
    Preconditions for decoupled and decentralized data-centric systems
    During a presentation at the TechTarget/BrightTALK Accelerating Cloud Innovation event this past December, I named the fifth phase of compute, networking and storage that we’ve entered the “Decoupled” and “Decentralized” Cloud.The quotation marks emphasized that what we’ve been experiencing is neither truly decoupled nor decentralized, but even so, the direction we’re headed in is toward… Read More »Preconditions for decoupled and decentralized data-centric systems The post Preconditions for decoupled and decentralized data-centric systems appeared first on Data Science Central.  ( 21 min )
    5 Tips To Protect Yourself from Identity Theft in 2023
    Identity theft is the process of stealing personally identifiable information (PII) to either defraud the victim or make the victim a scapegoat in a large-scale cyberattack. Attackers gain access to sensitive information such as social security numbers and credit cards that are used to collate a person’s identity.   According to a report by the Federal… Read More »5 Tips To Protect Yourself from Identity Theft in 2023 The post 5 Tips To Protect Yourself from Identity Theft in 2023 appeared first on Data Science Central.  ( 22 min )
    What is a Good Net Promoter Score for the Hotel/Resort Industry?
    The hotel industry is competitive, and it is solely dependent on customer satisfaction. Customers are key.  The hotel industry knows this and the importance of the NPS score for customer satisfaction. A better NPS score means satisfied/loyal customers.  What hotels have in their control is the website user interface, menu, and providing a seamless customer… Read More »What is a Good Net Promoter Score for the Hotel/Resort Industry? The post What is a Good Net Promoter Score for the Hotel/Resort Industry? appeared first on Data Science Central.  ( 21 min )
    6 Benefits of Data Science for Your Business
    We would not discover a new planet if we claimed that modern business harnesses the power of data science. Data science is used for a variety of purposes in a variety of industries. Furthermore, we would like to discuss the benefits of Data science for business in general. But before that, let’s define what Data… Read More »6 Benefits of Data Science for Your Business The post 6 Benefits of Data Science for Your Business appeared first on Data Science Central.  ( 22 min )
    7 Reasons Why Fast-Growing Businesses Are Turning to Virtual Colocation in 2023
    By 2025, more than 80% of enterprises will shift from traditional data centers to the cloud or third-party colocation data centers. For most businesses, data is an irreplaceable asset and a key investment area for future growth. Virtual colocation is becoming the talk of how data centers are shifting to adapt to growing business environments.… Read More »7 Reasons Why Fast-Growing Businesses Are Turning to Virtual Colocation in 2023 The post 7 Reasons Why Fast-Growing Businesses Are Turning to Virtual Colocation in 2023 appeared first on Data Science Central.  ( 20 min )
  • Open

    Set up Amazon SageMaker Studio with Jupyter Lab 3 using the AWS CDK
    Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. In Studio, you can load data, adjust ML models, move in between steps to adjust experiments, […]  ( 6 min )
    Churn prediction using multimodality of text and tabular features with Amazon SageMaker Jumpstart
    Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. Understanding customer behavior is top of mind for every business today. Gaining insights into why and how customers buy can help grow revenue. Customer churn is […]  ( 14 min )
  • Open

    🚀Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
    submitted by /u/oridnary_artist [link] [comments]  ( 53 min )
  • Open

    NVIDIA and Dell Technologies Expand AI Portfolio
    In their largest-ever joint AI initiative, NVIDIA and Dell Technologies today launched a wave of Dell PowerEdge systems available with NVIDIA acceleration, enabling enterprises to efficiently transform their businesses with AI. A total of 15 next-generation Dell PowerEdge systems can draw from NVIDIA’s full AI stack — including GPUs, DPUs and the NVIDIA AI Enterprise Read article >  ( 5 min )
  • Open

    Special primality proofs
    I’ve written lately about two general ways to prove that a number is prime: Pratt certificates for moderately-large primes and elliptic curve certificates for very large primes. If you can say more about the prime you wish to certify, there may be special forms of certificates that are more efficient. In particular, there are efficient […] Special primality proofs first appeared on John D. Cook.  ( 5 min )
  • Open

    Natural Language Processing of Aviation Occurrence Reports for Safety Management. (arXiv:2301.05663v1 [cs.CL])
    Occurrence reporting is a commonly used method in safety management systems to obtain insight in the prevalence of hazards and accident scenarios. In support of safety data analysis, reports are often categorized according to a taxonomy. However, the processing of the reports can require significant effort from safety analysts and a common problem is interrater variability in labeling processes. Also, in some cases, reports are not processed according to a taxonomy, or the taxonomy does not fully cover the contents of the documents. This paper explores various Natural Language Processing (NLP) methods to support the analysis of aviation safety occurrence reports. In particular, the problems studied are the automatic labeling of reports using a classification model, extracting the latent topics in a collection of texts using a topic model and the automatic generation of probable cause texts. Experimental results showed that (i) under the right conditions the labeling of occurrence reports can be effectively automated with a transformer-based classifier, (ii) topic modeling can be useful for finding the topics present in a collection of reports, and (iii) using a summarization model can be a promising direction for generating probable cause texts.  ( 2 min )
    Short-time SSVEP data extension by a novel generative adversarial networks based framework. (arXiv:2301.05599v1 [q-bio.NC])
    Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high transfer rate and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create supplementary synthetic electroencephalography (EEG) data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for data length window extension, termed as TEGAN. TEGAN transforms short-time SSVEP signals into long-time artificial SSVEP signals. By incorporating a novel U-Net generator architecture and auxiliary classifier into the network design, the TEGAN could produce conditioned features in the synthetic data. Additionally, to regularize the training process of GAN, we introduced a two-stage training strategy and the LeCam-divergence regularization term during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets. With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals to develop a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time for various real-world BCI-based applications, while the novelty of our augmentation strategies shed some value light on understanding the subject-invariant properties of SSVEPs.  ( 2 min )
    Sparse deep neural networks for modeling aluminum electrolysis dynamics. (arXiv:2209.05832v2 [physics.chem-ph] UPDATED)
    Deep neural networks have become very popular in modeling complex nonlinear processes due to their extraordinary ability to fit arbitrary nonlinear functions from data with minimal expert intervention. However, they are almost always overparameterized and challenging to interpret due to their internal complexity. Furthermore, the optimization process to find the learned model parameters can be unstable due to the process getting stuck in local minima. In this work, we demonstrate the value of sparse regularization techniques to significantly reduce the model complexity. We demonstrate this for the case of an aluminium extraction process, which is highly nonlinear system with many interrelated subprocesses. We trained a densely connected deep neural network to model the process and then compared the effects of sparsity promoting l1 regularization on generalizability, interpretability, and training stability. We found that the regularization significantly reduces model complexity compared to a corresponding dense neural network. We argue that this makes the model more interpretable, and show that training an ensemble of sparse neural networks with different parameter initializations often converges to similar model structures with similar learned input features. Furthermore, the empirical study shows that the resulting sparse models generalize better from small training sets than their dense counterparts.  ( 2 min )
    Fully Adaptive Composition in Differential Privacy. (arXiv:2203.05481v2 [cs.LG] UPDATED)
    Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However, these results require that the privacy parameters of all algorithms be fixed before interacting with the data. To address this, Rogers et al. introduced fully adaptive composition, wherein both algorithms and their privacy parameters can be selected adaptively. The authors introduce two probabilistic objects to measure privacy in adaptive composition: privacy filters, which provide differential privacy guarantees for composed interactions, and privacy odometers, time-uniform bounds on privacy loss. There are substantial gaps between advanced composition and existing filters and odometers. First, existing filters place stronger assumptions on the algorithms being composed. Second, these odometers and filters suffer from large constants, making them impractical. We construct filters that match the tightness of advanced composition, including constants, despite allowing for adaptively chosen privacy parameters. En route we also derive a privacy filter for approximate zCDP and approximate RDP. We also construct several general families of odometers. These odometers can match the tightness of advanced composition at an arbitrary, preselected point in time, or at all points in time simultaneously, up to a doubly-logarithmic factor. We obtain our results by leveraging recent advances in time-uniform martingale concentration. In sum, we show that fully adaptive privacy is obtainable at almost no loss, and conjecture that our results are essentially unimprovable (even in constants) in general.  ( 2 min )
    NRBdMF: A recommendation algorithm for predicting drug effects considering directionality. (arXiv:2208.04312v2 [q-bio.QM] UPDATED)
    Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have two opposite aspects, such as side effects and therapeutic effects. In the present study, we proposed using neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic property of drug effects. We used this proposed method for predicting side effects using a matrix that considered the bidirectionality of drug effects, in which known side effects were assigned a positive label (plus 1) and known treatment effects were assigned a negative (minus 1) label. The NRBdMF model, which utilizes drug bidirectional information, achieved enrichment of side effects at the top and indications at the bottom of the prediction list. This first attempt to consider the bidirectional nature of drug effects using NRBdMF showed that it reduced false positives and produced a highly interpretable output.  ( 2 min )
    Are disentangled representations all you need to build speaker anonymization systems?. (arXiv:2208.10497v3 [cs.SD] UPDATED)
    Speech signals contain a lot of sensitive information, such as the speaker's identity, which raises privacy concerns when speech data get collected. Speaker anonymization aims to transform a speech signal to remove the source speaker's identity while leaving the spoken content unchanged. Current methods perform the transformation by relying on content/speaker disentanglement and voice conversion. Usually, an acoustic model from an automatic speech recognition system extracts the content representation while an x-vector system extracts the speaker representation. Prior work has shown that the extracted features are not perfectly disentangled. This paper tackles how to improve features disentanglement, and thus the converted anonymized speech. We propose enhancing the disentanglement by removing speaker information from the acoustic model using vector quantization. Evaluation done using the VoicePrivacy 2022 toolkit showed that vector quantization helps conceal the original speaker identity while maintaining utility for speech recognition.  ( 2 min )
    Locating and Editing Factual Associations in GPT. (arXiv:2202.05262v5 [cs.CL] UPDATED)
    We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuron activations that are decisive in a model's factual predictions. This reveals a distinct set of steps in middle-layer feed-forward modules that mediate factual predictions while processing subject tokens. To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME). We find that ROME is effective on a standard zero-shot relation extraction (zsRE) model-editing task, comparable to existing methods. To perform a more sensitive evaluation, we also evaluate ROME on a new dataset of counterfactual assertions, on which it simultaneously maintains both specificity and generalization, whereas other methods sacrifice one or another. Our results confirm an important role for mid-layer feed-forward modules in storing factual associations and suggest that direct manipulation of computational mechanisms may be a feasible approach for model editing. The code, dataset, visualizations, and an interactive demo notebook are available at https://rome.baulab.info/  ( 2 min )
    On the feasibility of attacking Thai LPR systems with adversarial examples. (arXiv:2301.05506v1 [cs.CR])
    Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model's prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored specifically for the Thai language. To bridge this gap, this work presents a feasibility study of performing adversarial attacks on a specific Thai OCR application -- Thai License Plate Recognition (LPR). Moreover, we propose a new type of adversarial attack based on the \emph{semi-targeted} scenario and show that this scenario is highly realistic in LPR applications. Our experimental results show the feasibility of our attacks as they can be performed on a commodity computer desktop with over 90% attack success rate.  ( 2 min )
    An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models. (arXiv:2203.16290v4 [eess.SY] UPDATED)
    This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from input-output data collected from the plant, and can be given a state-space representation with known measurable states made by past input and output variables, so that a state observer is not required. In the training phase, the Incremental Input-to-State Stability ({\delta}ISS) property can be forced when consistent with the behavior of the plant. The {\delta}ISS property is then leveraged to augment the model with an explicit integral action on the output tracking error, which allows to achieve offset-free tracking capabilities to the designed control scheme. The proposed control architecture is numerically tested on a water heating system and the achieved results are compared to those scored by another popular offset-free MPC method, showing that the proposed scheme attains remarkable performances even in presence of disturbances acting on the plant.  ( 2 min )
    Discrete Morse Sandwich: Fast Computation of Persistence Diagrams for Scalar Data -- An Algorithm and A Benchmark. (arXiv:2206.13932v2 [cs.LG] UPDATED)
    This paper introduces an efficient algorithm for persistence diagram computation, given an input piecewise linear scalar field $f$ defined on a $d$-dimensional simplicial complex $K$, with $d \leq 3$. Our work revisits the seminal algorithm "PairSimplices" [31], [103] with discrete Morse theory (DMT) [34], [80], which greatly reduces the number of input simplices to consider. Further, we also extend to DMT and accelerate the stratification strategy described in "PairSimplices" for the fast computation of the $0^{th}$ and $(d - 1)^{th}$ diagrams, noted $D_0(f)$ and $D_{d-1}(f)$. Minima-saddle persistence pairs ($D_0(f)$) and saddle-maximum persistence pairs ($D_{d-1}(f)$) are efficiently computed by processing, with a Union-Find, the unstable sets of $1$-saddles and the stable sets of $(d - 1)$-saddles. This fast pre-computation for the dimensions $0$ and $(d - 1)$ enables an aggressive specialization of [4] to the 3D case, which results in a drastic reduction of the number of input simplices for the computation of $D_1(f)$, the intermediate layer of the sandwich. Finally, we document several performance improvements via shared-memory parallelism. We provide an open-source implementation of our algorithm for reproducibility purposes. We also contribute a reproducible benchmark package, which exploits three-dimensional data from a public repository and compares our algorithm to a variety of publicly available implementations. Extensive experiments indicate that our algorithm improves by two orders of magnitude the time performance of the seminal "PairSimplices" algorithm it extends. Moreover, it also improves memory footprint and time performance over a selection of 14 competing approaches, with a substantial gain over the fastest available approaches, while producing a strictly identical output.  ( 3 min )
    Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces. (arXiv:2301.05525v1 [cs.LG])
    Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features. These subsets usually comprise features that characterize a design with respect to one specific context, for example, constructive design parameters, performance values, or operation modes. It is desirable to evaluate the quality of design concepts by considering several of these feature subsets in isolation. In particular, meaningful concepts should not only identify dense, well separated groups of data instances, but also provide non-overlapping groups of data that persist when considering pre-defined feature subsets separately. In this work, we propose to view concept identification as a special form of clustering algorithm with a broad range of potential applications beyond engineering design. To illustrate the differences between concept identification and classical clustering algorithms, we apply a recently proposed concept identification algorithm to two synthetic data sets and show the differences in identified solutions. In addition, we introduce the mutual information measure as a metric to evaluate whether solutions return consistent clusters across relevant subsets. To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.  ( 2 min )
    Competing Bandits in Time Varying Matching Markets. (arXiv:2210.11692v2 [cs.LG] UPDATED)
    We study the problem of online learning in two-sided non-stationary matching markets, where the objective is to converge to a stable match. In particular, we consider the setting where one side of the market, the arms, has fixed known set of preferences over the other side, the players. While this problem has been studied when the players have fixed but unknown preferences, in this work we study the problem of how to learn when the preferences of the players are time varying and unknown. Our contribution is a methodology that can handle any type of preference structure and variation scenario. We show that, with the proposed algorithm, each player receives a uniform sub-linear regret of {$\widetilde{\mathcal{O}}(L^{1/2}_TT^{1/2})$} up to the number of changes in the underlying preferences of the agents, $L_T$. Therefore, we show that the optimal rates for single-agent learning can be achieved in spite of the competition up to a difference of a constant factor. We also discuss extensions of this algorithm to the case where the number of changes need not be known a priori.  ( 2 min )
    OpenTwins: An open-source framework for the design, development and integration of effective 3D-IoT-AI-powered digital twins. (arXiv:2301.05560v1 [cs.SE])
    Although digital twins have recently emerged as a clear alternative for reliable asset representations, most of the solutions and tools available for the development of digital twins are tailored to specific environments. Furthermore, achieving reliable digital twins often requires the orchestration of technologies and paradigms such as machine learning, the Internet of Things, and 3D visualization, which are rarely seamlessly aligned. In this paper, we present a generic framework for the development of effective digital twins combining some of the aforementioned areas. In this open framework, digital twins can be easily developed and orchestrated with 3D connected visualizations, IoT data streams, and real-time machine-learning predictions. To demonstrate the feasibility of the framework, a use case in the Petrochemical Industry 4.0 has been developed.  ( 2 min )
    Designing losses for data-free training of normalizing flows on Boltzmann distributions. (arXiv:2301.05475v1 [cs.LG])
    Generating a Boltzmann distribution in high dimension has recently been achieved with Normalizing Flows, which enable fast and exact computation of the generated density, and thus unbiased estimation of expectations. However, current implementations rely on accurate training data, which typically comes from computationally expensive simulations. There is therefore a clear incentive to train models with incomplete or no data by relying solely on the target density, which can be obtained from a physical energy model (up to a constant factor). For that purpose, we analyze the properties of standard losses based on Kullback-Leibler divergences. We showcase their limitations, in particular a strong propensity for mode collapse during optimization on high-dimensional distributions. We then propose strategies to alleviate these issues, most importantly a new loss function well-grounded in theory and with suitable optimization properties. Using as a benchmark the generation of 3D molecular configurations, we show on several tasks that, for the first time, imperfect pre-trained models can be further optimized in the absence of training data.  ( 2 min )
    Composite model of seismic monitoring data analysis during mining operations on the example of the Kukisvumchorrskoye deposit of JSC Apatit. (arXiv:2301.05701v1 [physics.geo-ph])
    Geomechanical monitoring of a rock massif is an actively developing branch of geomechanics. It is almost impossible to single out a methodology and approaches for data collection and analysis in developing seismic monitoring systems. In the process of mining in rock massif, changes in the state of structural inhomogeneities are most clearly manifested. Existing natural structural inhomogeneities are revealed, there are movements in discontinuous disturbances, and new technogenic disturbances are formed, which are accompanied by a change in the natural stress state of various blocks of the massif. An important task is to develop a mining forecasting model that can take into account the structural heterogeneity of the rock massif and select the necessary forecast horizon depending on monitoring data The developed method of evaluating the results of monitoring geomechanical processes in the rock massif allowed us to forecast of zones of possible rock bursts.  ( 2 min )
    A Comprehensive Review of Data-Driven Co-Speech Gesture Generation. (arXiv:2301.05339v1 [cs.GR])
    Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.  ( 2 min )
    Communication-Efficient Distributionally Robust Decentralized Learning. (arXiv:2205.15614v3 [cs.LG] UPDATED)
    Decentralized learning algorithms empower interconnected devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator. In the case of heterogeneous data distributions at the network nodes, collaboration can yield predictors with unsatisfactory performance for a subset of the devices. For this reason, in this work, we consider the formulation of a distributionally robust decentralized learning task and we propose a decentralized single loop gradient descent/ascent algorithm (AD-GDA) to directly solve the underlying minimax optimization problem. We render our algorithm communication-efficient by employing a compressed consensus scheme and we provide convergence guarantees for smooth convex and non-convex loss functions. Finally, we corroborate the theoretical findings with empirical results that highlight AD-GDA's ability to provide unbiased predictors and to greatly improve communication efficiency compared to existing distributionally robust algorithms.  ( 2 min )
    Personalized Prompt Learning for Explainable Recommendation. (arXiv:2202.07371v2 [cs.IR] UPDATED)
    Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system's ease of use, and gain users' trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus, how to effectively fuse IDs into such models becomes a critical issue. Inspired by recent advancement in prompt learning, we come up with two solutions: find alternative words to represent IDs (called discrete prompt learning), and directly input ID vectors to a pre-trained model (termed continuous prompt learning). In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages. To bridge the gap, we further propose two training strategies: sequential tuning and recommendation as regularization. Extensive experiments show that our continuous prompt learning approach equipped with the training strategies consistently outperforms strong baselines on three datasets of explainable recommendation.  ( 2 min )
    On the Symmetries of Deep Learning Models and their Internal Representations. (arXiv:2205.14258v4 [cs.LG] UPDATED)
    Symmetry is a fundamental tool in the exploration of a broad range of complex systems. In machine learning symmetry has been explored in both models and data. In this paper we seek to connect the symmetries arising from the architecture of a family of models with the symmetries of that family's internal representation of data. We do this by calculating a set of fundamental symmetry groups, which we call the intertwiner groups of the model. We connect intertwiner groups to a model's internal representations of data through a range of experiments that probe similarities between hidden states across models with the same architecture. Our work suggests that the symmetries of a network are propagated into the symmetries in that network's representation of data, providing us with a better understanding of how architecture affects the learning and prediction process. Finally, we speculate that for ReLU networks, the intertwiner groups may provide a justification for the common practice of concentrating model interpretability exploration on the activation basis in hidden layers rather than arbitrary linear combinations thereof.  ( 2 min )
    An Approximate Policy Iteration Viewpoint of Actor-Critic Algorithms. (arXiv:2208.03247v2 [cs.LG] UPDATED)
    In this work, we consider policy-based methods for solving the reinforcement learning problem, and establish the sample complexity guarantees. A policy-based algorithm typically consists of an actor and a critic. We consider using various policy update rules for the actor, including the celebrated natural policy gradient. In contrast to the gradient ascent approach taken in the literature, we view natural policy gradient as an approximate way of implementing policy iteration, and show that natural policy gradient (without any regularization) enjoys geometric convergence when using increasing stepsizes. As for the critic, we consider using TD-learning with linear function approximation and off-policy sampling. Since it is well-known that in this setting TD-learning can be unstable, we propose a stable generic algorithm (including two specific algorithms: the $\lambda$-averaged $Q$-trace and the two-sided $Q$-trace) that uses multi-step return and generalized importance sampling factors, and provide the finite-sample analysis. Combining the geometric convergence of the actor with the finite-sample analysis of the critic, we establish for the first time an overall $\mathcal{O}(\epsilon^{-2})$ sample complexity for finding an optimal policy (up to a function approximation error) using policy-based methods under off-policy sampling and linear function approximation.  ( 2 min )
    Learning to Control and Coordinate Hybrid Traffic Through Robot Vehicles at Complex and Unsignalized Intersections. (arXiv:2301.05294v1 [cs.LG])
    Intersections are essential road infrastructures for traffic in modern metropolises; however, they can also be the bottleneck of traffic flows due to traffic incidents or the absence of traffic coordination mechanisms such as traffic lights. Thus, various control and coordination mechanisms that are beyond traditional control methods have been proposed to improve the efficiency of intersection traffic. Amongst these methods, the control of foreseeable hybrid traffic that consists of human-driven vehicles (HVs) and robot vehicles (RVs) has recently emerged. We propose a decentralized reinforcement learning approach for the control and coordination of hybrid traffic at real-world, complex intersections--a topic that has not been previously explored. Comprehensive experiments are conducted to show the effectiveness of our approach. In particular, we show that using 5% RVs, we can prevent congestion formation inside the intersection under the actual traffic demand of 700 vehicles per hour. In contrast, without RVs, congestion starts to develop when the traffic demand reaches as low as 200 vehicles per hour. Further performance gains (reduced waiting time of vehicles at the intersection) are obtained as the RV penetration rate increases. When there exist more than 50% RVs in traffic, our method starts to outperform traffic signals on the average waiting time of all vehicles at the intersection. Our method is also robust against both blackout events and sudden RV percentage drops, and enjoys excellent generalizablility, which is illustrated by its successful deployment in two unseen intersections.  ( 2 min )
    confidence-planner: Easy-to-Use Prediction Confidence Estimation and Sample Size Planning. (arXiv:2301.05702v1 [stat.ME])
    Machine learning applications, especially in the fields of me\-di\-cine and social sciences, are slowly being subjected to increasing scrutiny. Similarly to sample size planning performed in clinical and social studies, lawmakers and funding agencies may expect statistical uncertainty estimations in machine learning applications that impact society. In this paper, we present an easy-to-use python package and web application for estimating prediction confidence intervals. The package offers eight different procedures to determine and justify the sample size and confidence of predictions from holdout, bootstrap, cross-validation, and progressive validation experiments. Since the package builds directly on established data analysis libraries, it seamlessly integrates into preprocessing and exploratory data analysis steps. Code related to this paper is available at: https://github.com/dabrze/confidence-planner.  ( 2 min )
    On the infinite-depth limit of finite-width neural networks. (arXiv:2210.00688v3 [stat.ML] UPDATED)
    In this paper, we study the infinite-depth limit of finite-width residual neural networks with random Gaussian weights. With proper scaling, we show that by fixing the width and taking the depth to infinity, the pre-activations converge in distribution to a zero-drift diffusion process. Unlike the infinite-width limit where the pre-activation converge weakly to a Gaussian random variable, we show that the infinite-depth limit yields different distributions depending on the choice of the activation function. We document two cases where these distributions have closed-form (different) expressions. We further show an intriguing change of regime phenomenon of the post-activation norms when the width increases from 3 to 4. Lastly, we study the sequential limit infinite-depth-then-infinite-width and compare it with the more commonly studied infinite-width-then-infinite-depth limit.  ( 2 min )
    TarGF: Learning Target Gradient Field for Object Rearrangement. (arXiv:2209.00853v3 [cs.LG] UPDATED)
    Object Rearrangement is to move objects from an initial state to a goal state. Here, we focus on a more practical setting in object rearrangement, i.e., rearranging objects from shuffled layouts to a normative target distribution without explicit goal specification. However, it remains challenging for AI agents, as it is hard to describe the target distribution (goal specification) for reward engineering or collect expert trajectories as demonstrations. Hence, it is infeasible to directly employ reinforcement learning or imitation learning algorithms to address the task. This paper aims to search for a policy only with a set of examples from a target distribution instead of a handcrafted reward function. We employ the score-matching objective to train a Target Gradient Field (TarGF), indicating a direction on each object to increase the likelihood of the target distribution. For object rearrangement, the TarGF can be used in two ways: 1) For model-based planning, we can cast the target gradient into a reference control and output actions with a distributed path planner; 2) For model-free reinforcement learning, the TarGF is not only used for estimating the likelihood-change as a reward but also provides suggested actions in residual policy learning. Experimental results in ball and room rearrangement demonstrate that our method significantly outperforms the state-of-the-art methods in the quality of the terminal state, the efficiency of the control process, and scalability.  ( 2 min )
    Mutation Testing of Deep Reinforcement Learning Based on Real Faults. (arXiv:2301.05651v1 [cs.LG])
    Testing Deep Learning (DL) systems is a complex task as they do not behave like traditional systems would, notably because of their stochastic nature. Nonetheless, being able to adapt existing testing techniques such as Mutation Testing (MT) to DL settings would greatly improve their potential verifiability. While some efforts have been made to extend MT to the Supervised Learning paradigm, little work has gone into extending it to Reinforcement Learning (RL) which is also an important component of the DL ecosystem but behaves very differently from SL. This paper builds on the existing approach of MT in order to propose a framework, RLMutation, for MT applied to RL. Notably, we use existing taxonomies of faults to build a set of mutation operators relevant to RL and use a simple heuristic to generate test cases for RL. This allows us to compare different mutation killing definitions based on existing approaches, as well as to analyze the behavior of the obtained mutation operators and their potential combinations called Higher Order Mutation(s) (HOM). We show that the design choice of the mutation killing definition can affect whether or not a mutation is killed as well as the generated test cases. Moreover, we found that even with a relatively small number of test cases and operators we manage to generate HOM with interesting properties which can enhance testing capability in RL systems.  ( 2 min )
    Universally Expressive Communication in Multi-Agent Reinforcement Learning. (arXiv:2206.06758v3 [cs.MA] UPDATED)
    Allowing agents to share information through communication is crucial for solving complex tasks in multi-agent reinforcement learning. In this work, we consider the question of whether a given communication protocol can express an arbitrary policy. By observing that many existing protocols can be viewed as instances of graph neural networks (GNNs), we demonstrate the equivalence of joint action selection to node labelling. With standard GNN approaches provably limited in their expressive capacity, we draw from existing GNN literature and consider augmenting agent observations with: (1) unique agent IDs and (2) random noise. We provide a theoretical analysis as to how these approaches yield universally expressive communication, and also prove them capable of targeting arbitrary sets of actions for identical agents. Empirically, these augmentations are found to improve performance on tasks where expressive communication is required, whilst, in general, the optimal communication protocol is found to be task-dependent.  ( 2 min )
    MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control. (arXiv:2208.07363v3 [cs.RO] UPDATED)
    Simulated humanoids are an appealing research domain due to their physical capabilities. Nonetheless, they are also challenging to control, as a policy must drive an unstable, discontinuous, and high-dimensional physical system. One widely studied approach is to utilize motion capture (MoCap) data to teach the humanoid agent low-level skills (e.g., standing, walking, and running) that can then be re-used to synthesize high-level behaviors. However, even with MoCap data, controlling simulated humanoids remains very hard, as MoCap data offers only kinematic information. Finding physical control inputs to realize the demonstrated motions requires computationally intensive methods like reinforcement learning. Thus, despite the publicly available MoCap data, its utility has been limited to institutions with large-scale compute. In this work, we dramatically lower the barrier for productive research on this topic by training and releasing high-quality agents that can track over three hours of MoCap data for a simulated humanoid in the dm_control physics-based environment. We release MoCapAct (Motion Capture with Actions), a dataset of these expert agents and their rollouts, which contain proprioceptive observations and actions. We demonstrate the utility of MoCapAct by using it to train a single hierarchical policy capable of tracking the entire MoCap dataset within dm_control and show the learned low-level component can be re-used to efficiently learn downstream high-level tasks. Finally, we use MoCapAct to train an autoregressive GPT model and show that it can control a simulated humanoid to perform natural motion completion given a motion prompt. Videos of the results and links to the code and dataset are available at https://microsoft.github.io/MoCapAct.  ( 2 min )
    Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image Synthesis. (arXiv:2301.05465v1 [cs.CV])
    Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost and potential harm, acquiring large medical datasets necessary for deep learning can be difficult. Medical image synthesis could help mitigate this problem. However, until now, the availability of GANs capable of synthesizing longitudinal volumetric data has been limited. To address this, we use the recent advances in latent space-based image editing to propose a novel joint learning scheme to explicitly embed temporal dependencies in the latent space of GANs. This, in contrast to previous methods, allows us to synthesize continuous, smooth, and high-quality longitudinal volumetric data with limited supervision. We show the effectiveness of our approach on three datasets containing different longitudinal dependencies. Namely, modeling a simple image transformation, breathing motion, and tumor regression, all while showing minimal disentanglement. The implementation is made available online at https://github.com/julschoen/Temp-GAN.  ( 2 min )
    Feature Importance Guided Attack: A Model Agnostic Adversarial Attack. (arXiv:2106.14815v3 [cs.LG] UPDATED)
    Research in adversarial learning has primarily focused on homogeneous unstructured datasets, which often map into the problem space naturally. Inverting a feature space attack on heterogeneous datasets into the problem space is much more challenging, particularly the task of finding the perturbation to perform. This work presents a formal search strategy: the `Feature Importance Guided Attack' (FIGA), which finds perturbations in the feature space of heterogeneous tabular datasets to produce evasion attacks. We first demonstrate FIGA in the feature space and then in the problem space. FIGA assumes no prior knowledge of the defending model's learning algorithm and does not require any gradient information. FIGA assumes knowledge of the feature representation and the mean feature values of defending model's dataset. FIGA leverages feature importance rankings by perturbing the most important features of the input in the direction of the target class. While FIGA is conceptually similar to other work which uses feature selection processes (e.g., mimicry attacks), we formalize an attack algorithm with three tunable parameters and investigate the strength of FIGA on tabular datasets. We demonstrate the effectiveness of FIGA by evading phishing detection models trained on four different tabular phishing datasets and one financial dataset with an average success rate of 94%. We extend FIGA to the phishing problem space by limiting the possible perturbations to be valid and feasible in the phishing domain. We generate valid adversarial phishing sites that are visually identical to their unperturbed counterpart and use them to attack six tabular ML models achieving a 13.05% average success rate.  ( 2 min )
    Adam Can Converge Without Any Modification On Update Rules. (arXiv:2208.09632v5 [cs.LG] UPDATED)
    Ever since Reddi et al. 2018 pointed out the divergence issue of Adam, many new variants have been designed to obtain convergence. However, vanilla Adam remains exceptionally popular and it works well in practice. Why is there a gap between theory and practice? We point out there is a mismatch between the settings of theory and practice: Reddi et al. 2018 pick the problem after picking the hyperparameters of Adam, i.e., $(\beta_1, \beta_2)$; while practical applications often fix the problem first and then tune $(\beta_1, \beta_2)$. Due to this observation, we conjecture that the empirical convergence can be theoretically justified, only if we change the order of picking the problem and hyperparameter. In this work, we confirm this conjecture. We prove that, when $\beta_2$ is large and $\beta_1 < \sqrt{\beta_2}<1$, Adam converges to the neighborhood of critical points. The size of the neighborhood is propositional to the variance of stochastic gradients. Under an extra condition (strong growth condition), Adam converges to critical points. It is worth mentioning that our results cover a wide range of hyperparameters: as $\beta_2$ increases, our convergence result can cover any $\beta_1 \in [0,1)$ including $\beta_1=0.9$, which is the default setting in deep learning libraries. To our knowledge, this is the first result showing that Adam can converge without any modification on its update rules. Further, our analysis does not require assumptions of bounded gradients or bounded 2nd-order momentum. When $\beta_2$ is small, we further point out a large region of $(\beta_1,\beta_2)$ where Adam can diverge to infinity. Our divergence result considers the same setting as our convergence result, indicating a phase transition from divergence to convergence when increasing $\beta_2$. These positive and negative results can provide suggestions on how to tune Adam hyperparameters.  ( 3 min )
    Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity. (arXiv:2301.05391v1 [cs.NI])
    5G New Radio proposes the usage of frequencies above 10 GHz to speed up LTE's existent maximum data rates. However, the effective size of 5G antennas and consequently its repercussions in the signal degradation in urban scenarios makes it a challenge to maintain stable coverage and connectivity. In order to obtain the best from both technologies, recent dual connectivity solutions have proved their capabilities to improve performance when compared with coexistent standalone 5G and 4G technologies. Reinforcement learning (RL) has shown its huge potential in wireless scenarios where parameter learning is required given the dynamic nature of such context. In this paper, we propose two reinforcement learning algorithms: a single agent RL algorithm named Clipped Double Q-Learning (CDQL) and a hierarchical Deep Q-Learning (HiDQL) to improve Multiple Radio Access Technology (multi-RAT) dual-connectivity handover. We compare our proposal with two baselines: a fixed parameter and a dynamic parameter solution. Simulation results reveal significant improvements in terms of latency with a gain of 47.6% and 26.1% for Digital-Analog beamforming (BF), 17.1% and 21.6% for Hybrid-Analog BF, and 24.7% and 39% for Analog-Analog BF when comparing the RL-schemes HiDQL and CDQL with the with the existent solutions, HiDQL presented a slower convergence time, however obtained a more optimal solution than CDQL. Additionally, we foresee the advantages of utilizing context-information as geo-location of the UEs to reduce the beam exploration sector, and thus improving further multi-RAT handover latency results.  ( 2 min )
    Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior. (arXiv:2301.05392v1 [cs.CV])
    Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential totackle this challenge in an efficient manner. Inspired by this, in this work we propose a multi-agent RL framework for simultaneous multi-target landmark detection. This framework is aimed to learn from incomplete or (and) complete images to form an implicit knowledge of global structure, which is consolidated during the training stage for the detection of targets from either complete or incomplete test images. To further explicitly exploit the global structural information from incomplete images, we propose to embed a shape model into the RL process. With this prior knowledge, the proposed RL model can not only localize dozens of targetssimultaneously, but also work effectively and robustly in the presence of incomplete images. We validated the applicability and efficacy of the proposed method on various multi-target detection tasks with incomplete images from practical clinics, using body dual-energy X-ray absorptiometry (DXA), cardiac MRI and head CT datasets. Results showed that our method could predict whole set of landmarks with incomplete training images up to 80% missing proportion (average distance error 2.29 cm on body DXA), and could detect unseen landmarks in regions with missing image information outside FOV of target images (average distance error 6.84 mm on 3D half-head CT).  ( 2 min )
    A Generic Graph Sparsification Framework using Deep Reinforcement Learning. (arXiv:2112.01565v2 [cs.LG] UPDATED)
    The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and decision-making of these graphs. In this work, we focus on the task graph sparsification: an edge-reduced graph of a similar structure to the original graph is produced while various user-defined graph metrics are largely preserved. Existing graph sparsification methods are mostly sampling-based, which introduce high computation complexity in general and lack of flexibility for a different reduction objective. We present SparRL, the first generic and effective graph sparsification framework enabled by deep reinforcement learning. SparRL can easily adapt to different reduction goals and promise graph-size-independent complexity. Extensive experiments show that SparRL outperforms all prevailing sparsification methods in producing high-quality sparsified graphs concerning a variety of objectives.  ( 2 min )
    Co-manipulation of soft-materials estimating deformation from depth images. (arXiv:2301.05609v1 [cs.RO])
    Human-robot co-manipulation of soft materials, such as fabrics, composites, and sheets of paper/cardboard, is a challenging operation that presents several relevant industrial applications. Estimating the deformation state of the co-manipulated material is one of the main challenges. Viable methods provide the indirect measure by calculating the human-robot relative distance. In this paper, we develop a data-driven model to estimate the deformation state of the material from a depth image through a Convolutional Neural Network (CNN). First, we define the deformation state of the material as the relative roto-translation from the current robot pose and a human grasping position. The model estimates the current deformation state through a Convolutional Neural Network, specifically a DenseNet-121 pretrained on ImageNet.The delta between the current and the desired deformation state is fed to the robot controller that outputs twist commands. The paper describes the developed approach to acquire, preprocess the dataset and train the model. The model is compared with the current state-of-the-art method based on a skeletal tracker from cameras. Results show that our approach achieves better performances and avoids the various drawbacks caused by using a skeletal tracker.Finally, we also studied the model performance according to different architectures and dataset dimensions to minimize the time required for dataset acquisition  ( 2 min )
    Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm. (arXiv:2301.05635v1 [cs.LG])
    Very large state spaces with a sparse reward signal are difficult to explore. The lack of a sophisticated guidance results in a poor performance for numerous reinforcement learning algorithms. In these cases, the commonly used random exploration is often not helpful. The literature shows that this kind of environments require enormous efforts to systematically explore large chunks of the state space. Learned state representations can help here to improve the search by providing semantic context and build a structure on top of the raw observations. In this work we introduce a novel time-myopic state representation that clusters temporal close states together while providing a time prediction capability between them. By adapting this model to the Go-Explore paradigm (Ecoffet et al., 2021b), we demonstrate the first learned state representation that reliably estimates novelty instead of using the hand-crafted representation heuristic. Our method shows an improved solution for the detachment problem which still remains an issue at the Go-Explore Exploration Phase. We provide evidence that our proposed method covers the entire state space with respect to all possible time trajectories without causing disadvantageous conflict-overlaps in the cell archive. Analogous to native Go-Explore, our approach is evaluated on the hard exploration environments MontezumaRevenge, Gravitar and Frostbite (Atari) in order to validate its capabilities on difficult tasks. Our experiments show that time-myopic Go-Explore is an effective alternative for the domain-engineered heuristic while also being more general. The source code of the method is available on GitHub.  ( 2 min )
    A Novel Framework for Handling Sparse Data in Traffic Forecast. (arXiv:2301.05292v1 [cs.LG])
    The ever increasing amount of GPS-equipped vehicles provides in real-time valuable traffic information for the roads traversed by the moving vehicles. In this way, a set of sparse and time evolving traffic reports is generated for each road. These time series are a valuable asset in order to forecast the future traffic condition. In this paper we present a deep learning framework that encodes the sparse recent traffic information and forecasts the future traffic condition. Our framework consists of a recurrent part and a decoder. The recurrent part employs an attention mechanism that encodes the traffic reports that are available at a particular time window. The decoder is responsible to forecast the future traffic condition.  ( 2 min )
    Dynamic Data Assimilation of MPAS-O and the Global Drifter Dataset. (arXiv:2301.05551v1 [physics.ao-ph])
    In this study, we propose a new method for combining in situ buoy measurements with Earth system models (ESMs) to improve the accuracy of temperature predictions in the ocean. The technique utilizes the dynamics and modes identified in ESMs to improve the accuracy of buoy measurements while still preserving features such as seasonality. Using this technique, errors in localized temperature predictions made by the MPAS-O model can be corrected. We demonstrate that our approach improves accuracy compared to other interpolation and data assimilation methods. We apply our method to assimilate the Model for Prediction Across Scales Ocean component (MPAS-O) with the Global Drifter Program's in-situ ocean buoy dataset.  ( 2 min )
    LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility. (arXiv:2301.05434v1 [cs.CV])
    Learning to recover clear images from images having a combination of degrading factors is a challenging task. That being said, autonomous surveillance in low visibility conditions caused by high pollution/smoke, poor air quality index, low light, atmospheric scattering, and haze during a blizzard becomes even more important to prevent accidents. It is thus crucial to form a solution that can result in a high-quality image and is efficient enough to be deployed for everyday use. However, the lack of proper datasets available to tackle this task limits the performance of the previous methods proposed. To this end, we generate the LowVis-AFO dataset, containing 3647 paired dark-hazy and clear images. We also introduce a lightweight deep learning model called Low-Visibility Restoration Network (LVRNet). It outperforms previous image restoration methods with low latency, achieving a PSNR value of 25.744 and an SSIM of 0.905, making our approach scalable and ready for practical use. The code and data can be found at https://github.com/Achleshwar/LVRNet.  ( 2 min )
    A Comprehensive Survey to Dataset Distillation. (arXiv:2301.05603v1 [cs.LG])
    Deep learning technology has unprecedentedly developed in the last decade and has become the primary choice in many application domains. This progress is mainly attributed to a systematic collaboration that rapidly growing computing resources encourage advanced algorithms to deal with massive data. However, it gradually becomes challenging to cope with the unlimited growth of data with limited computing power. To this end, diverse approaches are proposed to improve data processing efficiency. Dataset distillation, one of the dataset reduction methods, tackles the problem via synthesising a small typical dataset from giant data and has attracted a lot of attention from the deep learning community. Existing dataset distillation methods can be taxonomised into meta-learning and data match framework according to whether explicitly mimic target data. Albeit dataset distillation has shown a surprising performance in compressing datasets, it still possesses several limitations such as distilling high-resolution data. This paper provides a holistic understanding of dataset distillation from multiple aspects, including distillation frameworks and algorithms, disentangled dataset distillation, performance comparison, and applications. Finally, we discuss challenges and promising directions to further promote future studies about dataset distillation.  ( 2 min )
    Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach. (arXiv:2301.05639v1 [cs.LG])
    Phosphorescent metal complexes have been under intense investigations as emissive dopants for energy efficient organic light emitting diodes (OLEDs). Among them, cyclometalated Pt(II) complexes are widespread triplet emitters with color-tunable emissions. To render their practical applications as OLED emitters, it is in great need to develop Pt(II) complexes with high radiative decay rate constant ($k_r$) and photoluminescence (PL) quantum yield. Thus, an efficient and accurate prediction tool is highly desirable. Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than two hundred samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen. Our results demonstrate that ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients ($R^2$), mean absolute error (MAE), and root mean square error (RMSE) are 0.96, 7.21 nm and 13.00 nm for emission wavelength prediction, and 0.81, 0.11 and 0.15 for PL quantum yield prediction. For radiative decay rate constant ($k_r$), the obtained value of $R^2$ is 0.67 while MAE and RMSE are 0.21 and 0.25 (both in log scale), respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.We expect this protocol will become a valuable tool, accelerating the rational design of novel OLED materials with desired properties.  ( 3 min )
    Detection problems in the spiked matrix models. (arXiv:2301.05331v1 [math.ST])
    We study the statistical decision process of detecting the low-rank signal from various signal-plus-noise type data matrices, known as the spiked random matrix models. We first show that the principal component analysis can be improved by entrywise pre-transforming the data matrix if the noise is non-Gaussian, generalizing the known results for the spiked random matrix models with rank-1 signals. As an intermediate step, we find out sharp phase transition thresholds for the extreme eigenvalues of spiked random matrices, which generalize the Baik-Ben Arous-P\'{e}ch\'{e} (BBP) transition. We also prove the central limit theorem for the linear spectral statistics for the spiked random matrices and propose a hypothesis test based on it, which does not depend on the distribution of the signal or the noise. When the noise is non-Gaussian noise, the test can be improved with an entrywise transformation to the data matrix with additive noise. We also introduce an algorithm that estimates the rank of the signal when it is not known a priori.  ( 2 min )
    Inaccessible Neural Language Models Could Reinvigorate Linguistic Nativism. (arXiv:2301.05272v1 [cs.CL])
    Large Language Models (LLMs) have been making big waves in the machine learning community within the past few years. The impressive scalability of LLMs due to the advent of deep learning can be seen as a continuation of empiricist lingusitic methods, as opposed to rule-based linguistic methods that are grounded in a nativist perspective. Current LLMs are generally inaccessible to resource-constrained researchers, due to a variety of factors including closed source code. This work argues that this lack of accessibility could instill a nativist bias in researchers new to computational linguistics, given that new researchers may only have rule-based, nativist approaches to study to produce new work. Also, given that there are numerous critics of deep learning claiming that LLMs and related methods may soon lose their relevancy, we speculate that such an event could trigger a new wave of nativism in the language processing community. To prevent such a dramatic shift and placing favor in hybrid methods of rules and deep learning, we call upon researchers to open source their LLM code wherever possible to allow both empircist and hybrid approaches to remain accessible.  ( 2 min )
    A survey and taxonomy of loss functions in machine learning. (arXiv:2301.05579v1 [cs.LG])
    Most state-of-the-art machine learning techniques revolve around the optimisation of loss functions. Defining appropriate loss functions is therefore critical to successfully solving problems in this field. We present a survey of the most commonly used loss functions for a wide range of different applications, divided into classification, regression, ranking, sample generation and energy based modelling. Overall, we introduce 33 different loss functions and we organise them into an intuitive taxonomy. Each loss function is given a theoretical backing and we describe where it is best used. This survey aims to provide a reference of the most essential loss functions for both beginner and advanced machine learning practitioners.  ( 2 min )
    Building a Fuel Moisture Model for the Coupled Fire-Atmosphere Model WRF-SFIRE from Data: From Kalman Filters to Recurrent Neural Networks. (arXiv:2301.05427v1 [cs.LG])
    The current fuel moisture content (FMC) subsystems in WRF-SFIRE and its workflow system WRFx use a time-lag differential equation model with assimilation of data from FMC sensors on Remote Automated Weather Stations (RAWS) by the extended augmented Kalman filter. But the quality of the result is constrained by the limitations of the model and of the Kalman filter. We observe that the data flow in a system consisting of a model and the Kalman filter can be interpreted to be the same as the data flow in a recurrent neural network (RNN). Thus, instead of building more sophisticated models and data assimilation methods, we want to train a RNN to approximate the dynamics of the response of the FMC sensor to a time series of environmental data. Because standard AI approaches did not converge to reasonable solutions, we pre-train the RNN with special initial weights devised to turn it into a numerical solver of the differential equation. We then allow the AI training machinery to optimize the RNN weights to fit the data better. We illustrate the method on an example of a time series of 10h-FMC from RAWS and weather data from the Real-Time Mesoscale Analysis (RTMA).  ( 2 min )
    Decentralized model-free reinforcement learning in stochastic games with average-reward objective. (arXiv:2301.05630v1 [cs.LG])
    We propose the first model-free algorithm that achieves low regret performance for decentralized learning in two-player zero-sum tabular stochastic games with infinite-horizon average-reward objective. In decentralized learning, the learning agent controls only one player and tries to achieve low regret performances against an arbitrary opponent. This contrasts with centralized learning where the agent tries to approximate the Nash equilibrium by controlling both players. In our infinite-horizon undiscounted setting, additional structure assumptions is needed to provide good behaviors of learning processes : here we assume for every strategy of the opponent, the agent has a way to go from any state to any other. This assumption is the analogous to the "communicating" assumption in the MDP setting. We show that our Decentralized Optimistic Nash Q-Learning (DONQ-learning) algorithm achieves both sublinear high probability regret of order $T^{3/4}$ and sublinear expected regret of order $T^{2/3}$. Moreover, our algorithm enjoys a low computational complexity and low memory space requirement compared to the previous works of (Wei et al. 2017) and (Jafarnia-Jahromi et al. 2021) in the same setting.  ( 2 min )
    Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia. (arXiv:2207.13295v1 [eess.IV] CROSS LISTED)
    This study focuses on the application of a specific subfield of artificial intelligence referred to as computer vision in the analysis of 2-dimensional lung x-ray images for the assisted medical diagnosis of ordinary pneumonia. A convolutional neural network algorithm was implemented in a Python-coded, Flask-based web application that can analyze x-ray images for the detection of ordinary pneumonia. Since convolutional neural network algorithms rely on machine learning for the identification and detection of patterns, a technique referred to as transfer learning was implemented to train the neural network in the identification and detection of patterns within the dataset. Open-source lung x-ray images were used as training data to create a knowledge base that served as the core element of the web application and the experimental design employed a 5-Trial Confirmatory Test for the validation of the web application. The results of the 5-Trial Confirmatory Test show the calculation of Diagnostic Precision Percentage per Trial, General Diagnostic Precision Percentage, and General Diagnostic Error Percentage while the Confusion Matrix further shows the relationship between the label and the corresponding diagnosis result of the web application on each test images. The developed web application can be used by medical practitioners in A.I.-assisted diagnosis of ordinary pneumonia, and by researchers in the fields of computer science and bioinformatics.  ( 2 min )
    Distributed Online Private Learning of Convex Nondecomposable Objectives. (arXiv:2206.07944v3 [math.OC] UPDATED)
    We deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objectives are considered. Under this setting, each node only controls a part of the global decision, and the goal of all nodes is to collaboratively minimize the global cost over a time horizon $T$ while guarantees the security of the transmitted information. For such problems, we first design a novel generic algorithm framework, named as DPSDA, of differentially private distributed online learning using the Laplace mechanism and the stochastic variants of dual averaging method. Note that in the dual updates, all nodes of DPSDA employ the noise-corrupted gradients for more generality. Then, we propose two algorithms, named as DPSDA-C and DPSDA-PS, under this framework. In DPSDA-C, the nodes implement a circulation-based communication in the primal updates so as to alleviate the disagreements over time-varying undirected networks. In addition, for the extension to time-varying directed ones, the nodes implement the broadcast-based push-sum dynamics in DPSDA-PS, which can achieve average consensus over arbitrary directed networks. Theoretical results show that both algorithms attain an expected regret upper bound in $\mathcal{O}( \sqrt{T} )$ when the objective function is convex, which matches the best utility achievable by cutting-edge algorithms. Finally, numerical experiment results on both synthetic and real-world datasets verify the effectiveness of our algorithms.  ( 2 min )
    Sem@$K$: Is my knowledge graph embedding model semantic-aware?. (arXiv:2301.05601v1 [cs.LG])
    Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities. However, the literature claims that the KGEM evaluation procedure would benefit from adding supplementary dimensions to assess. That is why, in this paper, we extend our previously introduced metric Sem@$K$ that measures the capability of models to predict valid entities w.r.t. domain and range constrains. In particular, we consider a broad range of KGs and take their respective characteristics into account to propose different versions of Sem@$K$. We also perform an extensive study of KGEM semantic awareness. Our experiments show that Sem@$K$ provides a new perspective on KGEM quality. Its joint analysis with rank-based metrics offer different conclusions on the predictive power of models. Regarding Sem@$K$, some KGEMs are inherently better than others, but this semantic superiority is not indicative of their performance w.r.t. rank-based metrics. In this work, we generalize conclusions about the relative performance of KGEMs w.r.t. rank-based and semantic-oriented metrics at the level of families of models. The joint analysis of the aforementioned metrics gives more insight into the peculiarities of each model. This work paves the way for a more comprehensive evaluation of KGEM adequacy for specific downstream tasks.  ( 2 min )
    Scalable Batch Acquisition for Deep Bayesian Active Learning. (arXiv:2301.05490v1 [cs.LG])
    In deep active learning, it is especially important to choose multiple examples to markup at each step to work efficiently, especially on large datasets. At the same time, existing solutions to this problem in the Bayesian setup, such as BatchBALD, have significant limitations in selecting a large number of examples, associated with the exponential complexity of computing mutual information for joint random variables. We, therefore, present the Large BatchBALD algorithm, which gives a well-grounded approximation to the BatchBALD method that aims to achieve comparable quality while being more computationally efficient. We provide a complexity analysis of the algorithm, showing a reduction in computation time, especially for large batches. Furthermore, we present an extensive set of experimental results on image and text data, both on toy datasets and larger ones such as CIFAR-100.  ( 2 min )
    Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge. (arXiv:2301.05562v1 [eess.AS])
    This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD). Participants were invited to employ signal processing and machine learning methods to create predictive models based on spontaneous speech data. The Challenge has been designed to assess the extent to which predictive models built based on speech in one language (English) generalise to another language (Greek). To the best of our knowledge no work has investigated acoustic features of the speech signal in multilingual AD detection. Our baseline system used conventional machine learning algorithms with Active Data Representation of acoustic features, achieving accuracy of 73.91% on AD detection, and 4.95 root mean squared error on cognitive score prediction.  ( 2 min )
    Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. (arXiv:2301.05664v1 [cs.LG])
    In safety-critical decision-making scenarios being able to identify worst-case outcomes, or dead-ends is crucial in order to develop safe and reliable policies in practice. These situations are typically rife with uncertainty due to unknown or stochastic characteristics of the environment as well as limited offline training data. As a result, the value of a decision at any time point should be based on the distribution of its anticipated effects. We propose a framework to identify worst-case decision points, by explicitly estimating distributions of the expected return of a decision. These estimates enable earlier indication of dead-ends in a manner that is tunable based on the risk tolerance of the designed task. We demonstrate the utility of Distributional Dead-end Discovery (DistDeD) in a toy domain as well as when assessing the risk of severely ill patients in the intensive care unit reaching a point where death is unavoidable. We find that DistDeD significantly improves over prior discovery approaches, providing indications of the risk 10 hours earlier on average as well as increasing detection by 20%.  ( 2 min )
    Amenable Sparse Network Investigator. (arXiv:2202.09284v2 [cs.LG] UPDATED)
    We present "Amenable Sparse Network Investigator" (ASNI) algorithm that utilizes a novel pruning strategy based on a sigmoid function that induces sparsity level globally over the course of one single round of training. The ASNI algorithm fulfills both tasks that current state-of-the-art strategies can only do one of them. The ASNI algorithm has two subalgorithms: 1) ASNI-I, 2) ASNI-II. ASNI-I learns an accurate sparse off-the-shelf network only in one single round of training. ASNI-II learns a sparse network and an initialization that is quantized, compressed, and from which the sparse network is trainable. The learned initialization is quantized since only two numbers are learned for initialization of nonzero parameters in each layer L. Thus, quantization levels for the initialization of the entire network is 2L. Also, the learned initialization is compressed because it is a set consisting of 2L numbers. The special sparse network that can be trained from such a quantized and compressed initialization is called amenable. To the best of our knowledge, there is no other algorithm that can learn a quantized and compressed initialization from which the network is still trainable and is able to solve both pruning tasks. Our numerical experiments show that there is a quantized and compressed initialization from which the learned sparse network can be trained and reach to an accuracy on a par with the dense version. We experimentally show that these 2L levels of quantization are concentration points of parameters in each layer of the learned sparse network by ASNI-I. To corroborate the above, we have performed a series of experiments utilizing networks such as ResNets, VGG-style, small convolutional, and fully connected ones on ImageNet, CIFAR10, and MNIST datasets.  ( 2 min )
    Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition. (arXiv:2204.14067v2 [cs.LG] UPDATED)
    This work proposes a rapid algorithm, BM-Global, for nuclear-norm-regularized convex and low-rank matrix optimization problems. BM-Global efficiently decreases the objective value via low-cost steps leveraging the nonconvex but smooth Burer-Monteiro (BM) decomposition, while effectively escapes saddle points and spurious local minima ubiquitous in the BM form to obtain guarantees of fast convergence rates to the global optima of the original nuclear-norm-regularized problem through aperiodic inexact proximal gradient steps on it. The proposed approach adaptively adjusts the rank for the BM decomposition and can provably identify an optimal rank for the BM decomposition problem automatically in the course of optimization through tools of manifold identification. BM-Global hence also spends significantly less time on parameter tuning than existing matrix-factorization methods, which require an exhaustive search for finding this optimal rank. Extensive experiments on real-world large-scale problems of recommendation systems, regularized kernel estimation, and molecular conformation confirm that BM-Global can indeed effectively escapes spurious local minima at which existing BM approaches are stuck, and is a magnitude faster than state-of-the-art algorithms for low-rank matrix optimization problems involving a nuclear-norm regularizer.  ( 2 min )
    Generalization Properties of NAS under Activation and Skip Connection Search. (arXiv:2209.07238v3 [cs.LG] UPDATED)
    Neural Architecture Search (NAS) has fostered the automatic discovery of state-of-the-art neural architectures. Despite the progress achieved with NAS, so far there is little attention to theoretical guarantees on NAS. In this work, we study the generalization properties of NAS under a unifying framework enabling (deep) layer skip connection search and activation function search. To this end, we derive the lower (and upper) bounds of the minimum eigenvalue of the Neural Tangent Kernel (NTK) under the (in)finite-width regime using a certain search space including mixed activation functions, fully connected, and residual neural networks. We use the minimum eigenvalue to establish generalization error bounds of NAS in the stochastic gradient descent training. Importantly, we theoretically and experimentally show how the derived results can guide NAS to select the top-performing architectures, even in the case without training, leading to a train-free algorithm based on our theory. Accordingly, our numerical validation shed light on the design of computationally efficient methods for NAS. Our analysis is non-trivial due to the coupling of various architectures and activation functions under the unifying framework and has its own interest in providing the lower bound of the minimum eigenvalue of NTK in deep learning theory.  ( 2 min )
    Out-Of-Distribution Detection Is Not All You Need. (arXiv:2211.16158v2 [cs.LG] UPDATED)
    The usage of deep neural networks in safety-critical systems is limited by our ability to guarantee their correct behavior. Runtime monitors are components aiming to identify unsafe predictions and discard them before they can lead to catastrophic consequences. Several recent works on runtime monitoring have focused on out-of-distribution (OOD) detection, i.e., identifying inputs that are different from the training data. In this work, we argue that OOD detection is not a well-suited framework to design efficient runtime monitors and that it is more relevant to evaluate monitors based on their ability to discard incorrect predictions. We call this setting out-ofmodel-scope detection and discuss the conceptual differences with OOD. We also conduct extensive experiments on popular datasets from the literature to show that studying monitors in the OOD setting can be misleading: 1. very good OOD results can give a false impression of safety, 2. comparison under the OOD setting does not allow identifying the best monitor to detect errors. Finally, we also show that removing erroneous training data samples helps to train better monitors.  ( 2 min )
    Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey. (arXiv:2205.04712v2 [cs.LG] UPDATED)
    The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.  ( 2 min )
    Hyperparameter Optimization as a Service on INFN Cloud. (arXiv:2301.05522v1 [cs.DC])
    The simplest and often most effective way of parallelizing the training of complex machine learning models is to execute several training instances on multiple machines, possibly scanning the hyperparameter space to optimize the underlying statistical model and the learning procedure. Often, such a meta learning procedure is limited by the ability of accessing securely a common database organizing the knowledge of the previous and ongoing trials. Exploiting opportunistic GPUs provided in different environments represents a further challenge when designing such optimization campaigns. In this contribution we discuss how a set of RestAPIs can be used to access a dedicated service based on INFN Cloud to monitor and possibly coordinate multiple training instances, with gradient-less optimization techniques, via simple HTTP requests. The service, named Hopaas (Hyperparameter OPtimization As A Service), is made of web interface and sets of APIs implemented with a FastAPI back-end running through Uvicorn and NGINX in a virtual instance of INFN Cloud. The optimization algorithms are currently based on Bayesian techniques as provided by Optuna. A Python front-end is also made available for quick prototyping. We present applications to hyperparameter optimization campaigns performed combining private, INFN Cloud and CINECA resources.  ( 2 min )
    A Deep Reinforcement Learning Framework For Column Generation. (arXiv:2206.02568v3 [math.OC] UPDATED)
    Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns). CG is the workhorse for tackling large-scale \textit{integer} linear programs, which rely on CG to solve LP relaxations within a branch and price algorithm. Two canonical applications are the Cutting Stock Problem (CSP) and Vehicle Routing Problem with Time Windows (VRPTW). In VRPTW, for example, each binary variable represents the decision to include or exclude a \textit{route}, of which there are exponentially many; CG incrementally grows the subset of columns being used, ultimately converging to an optimal solution. We propose RLCG, the first Reinforcement Learning (RL) approach for CG. Unlike typical column selection rules which myopically select a column based on local information at each iteration, we treat CG as a sequential decision-making problem: the column selected in a given iteration affects subsequent column selections. This perspective lends itself to a Deep Reinforcement Learning approach that uses Graph Neural Networks (GNNs) to represent the variable-constraint structure in the LP of interest. We perform an extensive set of experiments using the publicly available BPPLIB benchmark for CSP and Solomon benchmark for VRPTW. RLCG converges faster and reduces the number of CG iterations by 22.4\% for CSP and 40.9\% for VRPTW on average compared to a commonly used greedy policy. Our code is available at https://github.com/chichengmessi/reinforcement-learning-for-column-generation.git.  ( 2 min )
    Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization). (arXiv:2209.07263v3 [cs.LG] UPDATED)
    We study the average robustness notion in deep neural networks in (selected) wide and narrow, deep and shallow, as well as lazy and non-lazy training settings. We prove that in the under-parameterized setting, width has a negative effect while it improves robustness in the over-parameterized setting. The effect of depth closely depends on the initialization and the training mode. In particular, when initialized with LeCun initialization, depth helps robustness with the lazy training regime. In contrast, when initialized with Neural Tangent Kernel (NTK) and He-initialization, depth hurts the robustness. Moreover, under the non-lazy training regime, we demonstrate how the width of a two-layer ReLU network benefits robustness. Our theoretical developments improve the results by [Huang et al. NeurIPS21; Wu et al. NeurIPS21] and are consistent with [Bubeck and Sellke NeurIPS21; Bubeck et al. COLT21].  ( 2 min )
    TUSK: Task-Agnostic Unsupervised Keypoints. (arXiv:2206.08460v2 [cs.CV] UPDATED)
    Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e.g. elbow, digit, abstract geometric shape) appears only once in an image. This greatly limits their applicability, as each instance must be isolated before applying the method-an issue that is never discussed or evaluated. We thus propose a novel method to learn Task-agnostic, UnSupervised Keypoints (TUSK) which can deal with multiple instances. To achieve this, instead of the commonly-used strategy of detecting multiple heatmaps, each dedicated to a specific keypoint type, we use a single heatmap for detection, and enable unsupervised learning of keypoint types through clustering. Specifically, we encode semantics into the keypoints by teaching them to reconstruct images from a sparse set of keypoints and their descriptors, where the descriptors are forced to form distinct clusters in feature space around learned prototypes. This makes our approach amenable to a wider range of tasks than any previous unsupervised keypoint method: we show experiments on multiple-instance detection and classification, object discovery, and landmark detection-all unsupervised-with performance on par with the state of the art, while also being able to deal with multiple instances.  ( 2 min )
    Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space. (arXiv:2206.11895v4 [cs.CV] UPDATED)
    Humans are remarkably flexible in understanding viewpoint changes due to visual cortex supporting the perception of 3D structure. In contrast, most of the computer vision models that learn visual representation from a pool of 2D images often fail to generalize over novel camera viewpoints. Recently, the vision architectures have shifted towards convolution-free architectures, visual Transformers, which operate on tokens derived from image patches. However, these Transformers do not perform explicit operations to learn viewpoint-agnostic representation for visual understanding. To this end, we propose a 3D Token Representation Layer (3DTRL) that estimates the 3D positional information of the visual tokens and leverages it for learning viewpoint-agnostic representations. The key elements of 3DTRL include a pseudo-depth estimator and a learned camera matrix to impose geometric transformations on the tokens, trained in an unsupervised fashion. These enable 3DTRL to recover the 3D positional information of the tokens from 2D patches. In practice, 3DTRL is easily plugged-in into a Transformer. Our experiments demonstrate the effectiveness of 3DTRL in many vision tasks including image classification, multi-view video alignment, and action recognition. The models with 3DTRL outperform their backbone Transformers in all the tasks with minimal added computation. Our code is available at https://github.com/elicassion/3DTRL.  ( 2 min )
    Non-Stochastic CDF Estimation Using Threshold Queries. (arXiv:2301.05682v1 [cs.LG])
    Estimating the empirical distribution of a scalar-valued data set is a basic and fundamental task. In this paper, we tackle the problem of estimating an empirical distribution in a setting with two challenging features. First, the algorithm does not directly observe the data; instead, it only asks a limited number of threshold queries about each sample. Second, the data are not assumed to be independent and identically distributed; instead, we allow for an arbitrary process generating the samples, including an adaptive adversary. These considerations are relevant, for example, when modeling a seller experimenting with posted prices to estimate the distribution of consumers' willingness to pay for a product: offering a price and observing a consumer's purchase decision is equivalent to asking a single threshold query about their value, and the distribution of consumers' values may be non-stationary over time, as early adopters may differ markedly from late adopters. Our main result quantifies, to within a constant factor, the sample complexity of estimating the empirical CDF of a sequence of elements of $[n]$, up to $\varepsilon$ additive error, using one threshold query per sample. The complexity depends only logarithmically on $n$, and our result can be interpreted as extending the existing logarithmic-complexity results for noisy binary search to the more challenging setting where noise is non-stochastic. Along the way to designing our algorithm, we consider a more general model in which the algorithm is allowed to make a limited number of simultaneous threshold queries on each sample. We solve this problem using Blackwell's Approachability Theorem and the exponential weights method. As a side result of independent interest, we characterize the minimum number of simultaneous threshold queries required by deterministic CDF estimation algorithms.  ( 2 min )
    TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems. (arXiv:2301.05334v1 [cs.LG])
    Coordination is one of the most difficult aspects of multi-agent reinforcement learning (MARL). One reason is that agents normally choose their actions independently of one another. In order to see coordination strategies emerging from the combination of independent policies, the recent research has focused on the use of a centralized function (CF) that learns each agent's contribution to the team reward. However, the structure in which the environment is presented to the agents and to the CF is typically overlooked. We have observed that the features used to describe the coordination problem can be represented as vertex features of a latent graph structure. Here, we present TransfQMix, a new approach that uses transformers to leverage this latent structure and learn better coordination policies. Our transformer agents perform a graph reasoning over the state of the observable entities. Our transformer Q-mixer learns a monotonic mixing-function from a larger graph that includes the internal and external states of the agents. TransfQMix is designed to be entirely transferable, meaning that same parameters can be used to control and train larger or smaller teams of agents. This enables to deploy promising approaches to save training time and derive general policies in MARL, such as transfer learning, zero-shot transfer, and curriculum learning. We report TransfQMix's performances in the Spread and StarCraft II environments. In both settings, it outperforms state-of-the-art Q-Learning models, and it demonstrates effectiveness in solving problems that other methods can not solve.  ( 2 min )
    Deep Reinforcement Learning for Asset Allocation: Reward Clipping. (arXiv:2301.05300v1 [q-fin.CP])
    Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits. In this paper, we compare performance between several reinforcement learning algorithms - actor-only, actor-critic and PPO models. Furthermore, we analyze each models' character and then introduce the advanced algorithm, so called Reward clipping model. It seems that the Reward Clipping model is better than other existing models in finance domain, especially portfolio optimization - it has strength both in bull and bear markets. Finally, we compare the performance for these models with traditional investment strategies during decreasing and increasing markets.  ( 2 min )
    Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems. (arXiv:2211.01873v2 [cs.LG] UPDATED)
    We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy production), we modify accordingly the port-Hamiltonian formalism so as to achieve a port-metriplectic one. We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems. Predictions can be done, however, at the scale of the complete system. Examples are shown on the performance of the proposed technique.  ( 2 min )
    Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles. (arXiv:2301.05638v1 [hep-ph])
    We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks to model the symmetry transformations and the corresponding generators. We construct loss functions that ensure that the applied transformations are symmetries and that the corresponding set of generators forms a closed (sub)algebra. Our procedure is validated with several examples illustrating different types of conserved quantities preserved by symmetry. In the process of deriving the full set of symmetries, we analyze the complete subgroup structure of the rotation groups $SO(2)$, $SO(3)$, and $SO(4)$, and of the Lorentz group $SO(1,3)$. Other examples include squeeze mapping, piecewise discontinuous labels, and $SO(10)$, demonstrating that our method is completely general, with many possible applications in physics and data science. Our study also opens the door for using a machine learning approach in the mathematical study of Lie groups and their properties.  ( 2 min )
    Almost Surely $\sqrt{T}$ Regret Bound for Adaptive LQR. (arXiv:2301.05537v1 [math.OC])
    The Linear-Quadratic Regulation (LQR) problem with unknown system parameters has been widely studied, but it has remained unclear whether $\tilde{ \mathcal{O}}(\sqrt{T})$ regret, which is the best known dependence on time, can be achieved almost surely. In this paper, we propose an adaptive LQR controller with almost surely $\tilde{ \mathcal{O}}(\sqrt{T})$ regret upper bound. The controller features a circuit-breaking mechanism, which circumvents potential safety breach and guarantees the convergence of the system parameter estimate, but is shown to be triggered only finitely often and hence has negligible effect on the asymptotic performance of the controller. The proposed controller is also validated via simulation on Tennessee Eastman Process~(TEP), a commonly used industrial process example.  ( 2 min )
    A Solver-Free Framework for Scalable Learning in Neural ILP Architectures. (arXiv:2210.09082v2 [cs.LG] UPDATED)
    There is a recent focus on designing architectures that have an Integer Linear Programming (ILP) layer within a neural model (referred to as Neural ILP in this paper). Neural ILP architectures are suitable for pure reasoning tasks that require data-driven constraint learning or for tasks requiring both perception (neural) and reasoning (ILP). A recent SOTA approach for end-to-end training of Neural ILP explicitly defines gradients through the ILP black box (Paulus et al. 2021) - this trains extremely slowly, owing to a call to the underlying ILP solver for every training data point in a minibatch. In response, we present an alternative training strategy that is solver-free, i.e., does not call the ILP solver at all at training time. Neural ILP has a set of trainable hyperplanes (for cost and constraints in ILP), together representing a polyhedron. Our key idea is that the training loss should impose that the final polyhedron separates the positives (all constraints satisfied) from the negatives (at least one violated constraint or a suboptimal cost value), via a soft-margin formulation. While positive example(s) are provided as part of the training data, we devise novel techniques for generating negative samples. Our solution is flexible enough to handle equality as well as inequality constraints. Experiments on several problems, both perceptual as well as symbolic, which require learning the constraints of an ILP, show that our approach has superior performance and scales much better compared to purely neural baselines and other state-of-the-art models that require solver-based training. In particular, we are able to obtain excellent performance in 9 x 9 symbolic and visual sudoku, to which the other Neural ILP solver is not able to scale.  ( 2 min )
    On "Deep Learning" Misconduct. (arXiv:2211.16350v4 [cs.LG] UPDATED)
    This is a theoretical paper, as a companion paper of the plenary talk for the same conference ISAIC 2022. In contrast to the author's plenary talk in the same conference, conscious learning (Weng, 2022b; Weng, 2022c) which develops a single network for a life (many tasks), "Deep Learning" trains multiple networks for each task. Although "Deep Learning" may use different learning modes, including supervised, reinforcement and adversarial modes, almost all "Deep Learning" projects apparently suffer from the same misconduct, called "data deletion" and "test on training data". This paper establishes a theorem that a simple method called Pure-Guess Nearest Neighbor (PGNN) reaches any required errors on validation data set and test data set, including zero-error requirements, through the same misconduct, as long as the test data set is in the possession of the authors and both the amount of storage space and the time of training are finite but unbounded. The misconduct violates well-known protocols called transparency and cross-validation. The nature of the misconduct is fatal, because in the absence of any disjoint test, "Deep Learning" is clearly not generalizable.  ( 2 min )
    Language-Informed Transfer Learning for Embodied Household Activities. (arXiv:2301.05318v1 [cs.RO])
    For service robots to become general-purpose in everyday household environments, they need not only a large library of primitive skills, but also the ability to quickly learn novel tasks specified by users. Fine-tuning neural networks on a variety of downstream tasks has been successful in many vision and language domains, but research is still limited on transfer learning between diverse long-horizon tasks. We propose that, compared to reinforcement learning for a new household activity from scratch, home robots can benefit from transferring the value and policy networks trained for similar tasks. We evaluate this idea in the BEHAVIOR simulation benchmark which includes a large number of household activities and a set of action primitives. For easy mapping between state spaces of different tasks, we provide a text-based representation and leverage language models to produce a common embedding space. The results show that the selection of similar source activities can be informed by the semantic similarity of state and goal descriptions with the target task. We further analyze the results and discuss ways to overcome the problem of catastrophic forgetting.  ( 2 min )
    Data-Efficient Structured Pruning via Submodular Optimization. (arXiv:2203.04940v3 [cs.LG] UPDATED)
    Structured pruning is an effective approach for compressing large pre-trained neural networks without significantly affecting their performance. However, most current structured pruning methods do not provide any performance guarantees, and often require fine-tuning, which makes them inapplicable in the limited-data regime. We propose a principled data-efficient structured pruning method based on submodular optimization. In particular, for a given layer, we select neurons/channels to prune and corresponding new weights for the next layer, that minimize the change in the next layer's input induced by pruning. We show that this selection problem is a weakly submodular maximization problem, thus it can be provably approximated using an efficient greedy algorithm. Our method is guaranteed to have an exponentially decreasing error between the original model and the pruned model outputs w.r.t the pruned size, under reasonable assumptions. It is also one of the few methods in the literature that uses only a limited-number of training data and no labels. Our experimental results demonstrate that our method outperforms state-of-the-art methods in the limited-data regime.  ( 2 min )
    Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. (arXiv:2206.07234v3 [cs.LG] UPDATED)
    There is a disconnect between how researchers and practitioners handle privacy-utility tradeoffs. Researchers primarily operate from a privacy first perspective, setting strict privacy requirements and minimizing risk subject to these constraints. Practitioners often desire an accuracy first perspective, possibly satisfied with the greatest privacy they can get subject to obtaining sufficiently small error. Ligett et al. have introduced a "noise reduction" algorithm to address the latter perspective. The authors show that by adding correlated Laplace noise and progressively reducing it on demand, it is possible to produce a sequence of increasingly accurate estimates of a private parameter while only paying a privacy cost for the least noisy iterate released. In this work, we generalize noise reduction to the setting of Gaussian noise, introducing the Brownian mechanism. The Brownian mechanism works by first adding Gaussian noise of high variance corresponding to the final point of a simulated Brownian motion. Then, at the practitioner's discretion, noise is gradually decreased by tracing back along the Brownian path to an earlier time. Our mechanism is more naturally applicable to the common setting of bounded $\ell_2$-sensitivity, empirically outperforms existing work on common statistical tasks, and provides customizable control of privacy loss over the entire interaction with the practitioner. We complement our Brownian mechanism with ReducedAboveThreshold, a generalization of the classical AboveThreshold algorithm that provides adaptive privacy guarantees. Overall, our results demonstrate that one can meet utility constraints while still maintaining strong levels of privacy.  ( 2 min )
    Learning with little mixing. (arXiv:2206.08269v2 [cs.LG] UPDATED)
    We study square loss in a realizable time-series framework with martingale difference noise. Our main result is a fast rate excess risk bound which shows that whenever a trajectory hypercontractivity condition holds, the risk of the least-squares estimator on dependent data matches the iid rate order-wise after a burn-in time. In comparison, many existing results in learning from dependent data have rates where the effective sample size is deflated by a factor of the mixing-time of the underlying process, even after the burn-in time. Furthermore, our results allow the covariate process to exhibit long range correlations which are substantially weaker than geometric ergodicity. We call this phenomenon learning with little mixing, and present several examples for when it occurs: bounded function classes for which the $L^2$ and $L^{2+\epsilon}$ norms are equivalent, ergodic finite state Markov chains, various parametric models, and a broad family of infinite dimensional $\ell^2(\mathbb{N})$ ellipsoids. By instantiating our main result to system identification of nonlinear dynamics with generalized linear model transitions, we obtain a nearly minimax optimal excess risk bound after only a polynomial burn-in time.  ( 2 min )
    Biases in Inverse Ising Estimates of Near-Critical Behaviour. (arXiv:2301.05556v1 [cond-mat.dis-nn])
    Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as Pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick (SK) model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer-to-criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging (fMRI) dataset from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world datasets.  ( 2 min )
    An efficient hybrid classification approach for COVID-19 based on Harris Hawks Optimization and Salp Swarm Optimization. (arXiv:2301.05296v1 [cs.NE])
    Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy for improving the basic HHO's performance using the Salp algorithm's power to select the best fitness values. The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of four performance metrics (Accuracy, Recall, Precision, F1) were employed in the studies using three classifiers (Support vector machines (SVMs), k-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98% accuracy with two classifiers, XGboost and KNN.  ( 2 min )
    Neural network with optimal neuron activation functions based on additive Gaussian process regression. (arXiv:2301.05567v1 [stat.ML])
    Feed-forward neural networks (NN) are a staple machine learning method widely used in many areas of science and technology. While even a single-hidden layer NN is a universal approximator, its expressive power is limited by the use of simple neuron activation functions (such as sigmoid functions) that are typically the same for all neurons. More flexible neuron activation functions would allow using fewer neurons and layers and thereby save computational cost and improve expressive power. We show that additive Gaussian process regression (GPR) can be used to construct optimal neuron activation functions that are individual to each neuron. An approach is also introduced that avoids non-linear fitting of neural network parameters. The resulting method combines the advantage of robustness of a linear regression with the higher expressive power of a NN. We demonstrate the approach by fitting the potential energy surface of the water molecule. Without requiring any non-linear optimization, the additive GPR based approach outperforms a conventional NN in the high accuracy regime, where a conventional NN suffers more from overfitting.  ( 2 min )
    On the explainability of quantum neural networks based on variational quantum circuits. (arXiv:2301.05549v1 [quant-ph])
    Ridge functions are used to describe and study the lower bound of the approximation done by the neural networks which can be written as a linear combination of activation functions. If the activation functions are also ridge functions, these networks are called explainable neural networks. In this paper, we first show that quantum neural networks which are based on variational quantum circuits can be written as a linear combination of ridge functions. Consequently, we show that the interpretability and explainability of such quantum neural networks can be directly considered and studied as an approximation with the linear combination of ridge functions.  ( 2 min )
    Knowledge Enhancement for Multi-Behavior Contrastive Recommendation. (arXiv:2301.05403v1 [cs.IR])
    A well-designed recommender system can accurately capture the attributes of users and items, reflecting the unique preferences of individuals. Traditional recommendation techniques usually focus on modeling the singular type of behaviors between users and items. However, in many practical recommendation scenarios (e.g., social media, e-commerce), there exist multi-typed interactive behaviors in user-item relationships, such as click, tag-as-favorite, and purchase in online shopping platforms. Thus, how to make full use of multi-behavior information for recommendation is of great importance to the existing system, which presents challenges in two aspects that need to be explored: (1) Utilizing users' personalized preferences to capture multi-behavioral dependencies; (2) Dealing with the insufficient recommendation caused by sparse supervision signal for target behavior. In this work, we propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework, including two Contrastive Learning tasks and three functional modules to tackle the above challenges, respectively. In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items. In addition, in the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect. Extensive experiments and ablation tests on the three real-world datasets indicate our KMCLR outperforms various state-of-the-art recommendation methods and verify the effectiveness of our method.  ( 2 min )
    AAAI 2022 Fall Symposium: Lessons Learned for Autonomous Assessment of Machine Abilities (LLAAMA). (arXiv:2301.05384v1 [cs.LG])
    Modern civilian and military systems have created a demand for sophisticated intelligent autonomous machines capable of operating in uncertain dynamic environments. Such systems are realizable thanks in large part to major advances in perception and decision-making techniques, which in turn have been propelled forward by modern machine learning tools. However, these newer forms of intelligent autonomy raise questions about when/how communication of the operational intent and assessments of actual vs. supposed capabilities of autonomous agents impact overall performance. This symposium examines the possibilities for enabling intelligent autonomous systems to self-assess and communicate their ability to effectively execute assigned tasks, as well as reason about the overall limits of their competencies and maintain operability within those limits. The symposium brings together researchers working in this burgeoning area of research to share lessons learned, identify major theoretical and practical challenges encountered so far, and potential avenues for future research and real-world applications.  ( 2 min )
    HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments. (arXiv:2301.05293v1 [cs.LG])
    Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.  ( 2 min )
    In BLOOM: Creativity and Affinity in Artificial Lyrics and Art. (arXiv:2301.05402v1 [cs.CL])
    We apply a large multilingual language model (BLOOM-176B) in open-ended generation of Chinese song lyrics, and evaluate the resulting lyrics for coherence and creativity using human reviewers. We find that current computational metrics for evaluating large language model outputs (MAUVE) have limitations in evaluation of creative writing. We note that the human concept of creativity requires lyrics to be both comprehensible and distinctive -- and that humans assess certain types of machine-generated lyrics to score more highly than real lyrics by popular artists. Inspired by the inherently multimodal nature of album releases, we leverage a Chinese-language stable diffusion model to produce high-quality lyric-guided album art, demonstrating a creative approach for an artist seeking inspiration for an album or single. Finally, we introduce the MojimLyrics dataset, a Chinese-language dataset of popular song lyrics for future research.  ( 2 min )
    A Constrained-Optimization Approach to the Execution of Prioritized Stacks of Learned Multi-Robot Tasks. (arXiv:2301.05346v1 [cs.RO])
    This paper presents a constrained-optimization formulation for the prioritized execution of learned robot tasks. The framework lends itself to the execution of tasks encoded by value functions, such as tasks learned using the reinforcement learning paradigm. The tasks are encoded as constraints of a convex optimization program by using control Lyapunov functions. Moreover, an additional constraint is enforced in order to specify relative priorities between the tasks. The proposed approach is showcased in simulation using a team of mobile robots executing coordinated multi-robot tasks.  ( 2 min )
    A Scalable Technique for Weak-Supervised Learning with Domain Constraints. (arXiv:2301.05253v1 [cs.LG])
    We propose a novel scalable end-to-end pipeline that uses symbolic domain knowledge as constraints for learning a neural network for classifying unlabeled data in a weak-supervised manner. Our approach is particularly well-suited for settings where the data consists of distinct groups (classes) that lends itself to clustering-friendly representation learning and the domain constraints can be reformulated for use of efficient mathematical optimization techniques by considering multiple training examples at once. We evaluate our approach on a variant of the MNIST image classification problem where a training example consists of image sequences and the sum of the numbers represented by the sequences, and show that our approach scales significantly better than previous approaches that rely on computing all constraint satisfying combinations for each training example.  ( 2 min )
    Equivariant Representations for Non-Free Group Actions. (arXiv:2301.05231v1 [cs.LG])
    We introduce a method for learning representations that are equivariant with respect to general group actions over data. Differently from existing equivariant representation learners, our method is suitable for actions that are not free i.e., that stabilize data via nontrivial symmetries. Our method is grounded in the orbit-stabilizer theorem from group theory, which guarantees that an ideal learner infers an isomorphic representation. Finally, we provide an empirical investigation on image datasets with rotational symmetries and show that taking stabilizers into account improves the quality of the representations.  ( 2 min )
  • Open

    Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. (arXiv:2301.05664v1 [cs.LG])
    In safety-critical decision-making scenarios being able to identify worst-case outcomes, or dead-ends is crucial in order to develop safe and reliable policies in practice. These situations are typically rife with uncertainty due to unknown or stochastic characteristics of the environment as well as limited offline training data. As a result, the value of a decision at any time point should be based on the distribution of its anticipated effects. We propose a framework to identify worst-case decision points, by explicitly estimating distributions of the expected return of a decision. These estimates enable earlier indication of dead-ends in a manner that is tunable based on the risk tolerance of the designed task. We demonstrate the utility of Distributional Dead-end Discovery (DistDeD) in a toy domain as well as when assessing the risk of severely ill patients in the intensive care unit reaching a point where death is unavoidable. We find that DistDeD significantly improves over prior discovery approaches, providing indications of the risk 10 hours earlier on average as well as increasing detection by 20%.  ( 2 min )
    Neural network with optimal neuron activation functions based on additive Gaussian process regression. (arXiv:2301.05567v1 [stat.ML])
    Feed-forward neural networks (NN) are a staple machine learning method widely used in many areas of science and technology. While even a single-hidden layer NN is a universal approximator, its expressive power is limited by the use of simple neuron activation functions (such as sigmoid functions) that are typically the same for all neurons. More flexible neuron activation functions would allow using fewer neurons and layers and thereby save computational cost and improve expressive power. We show that additive Gaussian process regression (GPR) can be used to construct optimal neuron activation functions that are individual to each neuron. An approach is also introduced that avoids non-linear fitting of neural network parameters. The resulting method combines the advantage of robustness of a linear regression with the higher expressive power of a NN. We demonstrate the approach by fitting the potential energy surface of the water molecule. Without requiring any non-linear optimization, the additive GPR based approach outperforms a conventional NN in the high accuracy regime, where a conventional NN suffers more from overfitting.  ( 2 min )
    Learning with little mixing. (arXiv:2206.08269v2 [cs.LG] UPDATED)
    We study square loss in a realizable time-series framework with martingale difference noise. Our main result is a fast rate excess risk bound which shows that whenever a trajectory hypercontractivity condition holds, the risk of the least-squares estimator on dependent data matches the iid rate order-wise after a burn-in time. In comparison, many existing results in learning from dependent data have rates where the effective sample size is deflated by a factor of the mixing-time of the underlying process, even after the burn-in time. Furthermore, our results allow the covariate process to exhibit long range correlations which are substantially weaker than geometric ergodicity. We call this phenomenon learning with little mixing, and present several examples for when it occurs: bounded function classes for which the $L^2$ and $L^{2+\epsilon}$ norms are equivalent, ergodic finite state Markov chains, various parametric models, and a broad family of infinite dimensional $\ell^2(\mathbb{N})$ ellipsoids. By instantiating our main result to system identification of nonlinear dynamics with generalized linear model transitions, we obtain a nearly minimax optimal excess risk bound after only a polynomial burn-in time.  ( 2 min )
    Scalable Estimation for Structured Additive Distributional Regression. (arXiv:2301.05593v1 [stat.CO])
    Recently, fitting probabilistic models have gained importance in many areas but estimation of such distributional models with very large data sets is a difficult task. In particular, the use of rather complex models can easily lead to memory-related efficiency problems that can make estimation infeasible even on high-performance computers. We therefore propose a novel backfitting algorithm, which is based on the ideas of stochastic gradient descent and can deal virtually with any amount of data on a conventional laptop. The algorithm performs automatic selection of variables and smoothing parameters, and its performance is in most cases superior or at least equivalent to other implementations for structured additive distributional regression, e.g., gradient boosting, while maintaining low computation time. Performance is evaluated using an extensive simulation study and an exceptionally challenging and unique example of lightning count prediction over Austria. A very large dataset with over 9 million observations and 80 covariates is used, so that a prediction model cannot be estimated with standard distributional regression methods but with our new approach.  ( 2 min )
    Scalable Batch Acquisition for Deep Bayesian Active Learning. (arXiv:2301.05490v1 [cs.LG])
    In deep active learning, it is especially important to choose multiple examples to markup at each step to work efficiently, especially on large datasets. At the same time, existing solutions to this problem in the Bayesian setup, such as BatchBALD, have significant limitations in selecting a large number of examples, associated with the exponential complexity of computing mutual information for joint random variables. We, therefore, present the Large BatchBALD algorithm, which gives a well-grounded approximation to the BatchBALD method that aims to achieve comparable quality while being more computationally efficient. We provide a complexity analysis of the algorithm, showing a reduction in computation time, especially for large batches. Furthermore, we present an extensive set of experimental results on image and text data, both on toy datasets and larger ones such as CIFAR-100.  ( 2 min )
    On the infinite-depth limit of finite-width neural networks. (arXiv:2210.00688v3 [stat.ML] UPDATED)
    In this paper, we study the infinite-depth limit of finite-width residual neural networks with random Gaussian weights. With proper scaling, we show that by fixing the width and taking the depth to infinity, the pre-activations converge in distribution to a zero-drift diffusion process. Unlike the infinite-width limit where the pre-activation converge weakly to a Gaussian random variable, we show that the infinite-depth limit yields different distributions depending on the choice of the activation function. We document two cases where these distributions have closed-form (different) expressions. We further show an intriguing change of regime phenomenon of the post-activation norms when the width increases from 3 to 4. Lastly, we study the sequential limit infinite-depth-then-infinite-width and compare it with the more commonly studied infinite-width-then-infinite-depth limit.  ( 2 min )
    Fully Adaptive Composition in Differential Privacy. (arXiv:2203.05481v2 [cs.LG] UPDATED)
    Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However, these results require that the privacy parameters of all algorithms be fixed before interacting with the data. To address this, Rogers et al. introduced fully adaptive composition, wherein both algorithms and their privacy parameters can be selected adaptively. The authors introduce two probabilistic objects to measure privacy in adaptive composition: privacy filters, which provide differential privacy guarantees for composed interactions, and privacy odometers, time-uniform bounds on privacy loss. There are substantial gaps between advanced composition and existing filters and odometers. First, existing filters place stronger assumptions on the algorithms being composed. Second, these odometers and filters suffer from large constants, making them impractical. We construct filters that match the tightness of advanced composition, including constants, despite allowing for adaptively chosen privacy parameters. En route we also derive a privacy filter for approximate zCDP and approximate RDP. We also construct several general families of odometers. These odometers can match the tightness of advanced composition at an arbitrary, preselected point in time, or at all points in time simultaneously, up to a doubly-logarithmic factor. We obtain our results by leveraging recent advances in time-uniform martingale concentration. In sum, we show that fully adaptive privacy is obtainable at almost no loss, and conjecture that our results are essentially unimprovable (even in constants) in general.  ( 2 min )
    Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap. (arXiv:2301.05703v1 [econ.EM])
    In this paper, I try to tame "Basu's elephants" (data with extreme selection on observables). I propose new practical large-sample and finite-sample methods for estimating and inferring heterogeneous causal effects (under unconfoundedness) in the empirically relevant context of limited overlap. I develop a general principle called "Stable Probability Weighting" (SPW) that can be used as an alternative to the widely used Inverse Probability Weighting (IPW) technique, which relies on strong overlap. I show that IPW (or its augmented version), when valid, is a special case of the more general SPW (or its doubly robust version), which adjusts for the extremeness of the conditional probabilities of the treatment states. The SPW principle can be implemented using several existing large-sample parametric, semiparametric, and nonparametric procedures for conditional moment models. In addition, I provide new finite-sample results that apply when unconfoundedness is plausible within fine strata. Since IPW estimation relies on the problematic reciprocal of the estimated propensity score, I develop a "Finite-Sample Stable Probability Weighting" (FPW) set-estimator that is unbiased in a sense. I also propose new finite-sample inference methods for testing a general class of weak null hypotheses. The associated computationally convenient methods, which can be used to construct valid confidence sets and to bound the finite-sample confidence distribution, are of independent interest. My large-sample and finite-sample frameworks extend to the setting of multivalued treatments.  ( 2 min )
    Memory Efficient Continual Learning with Transformers. (arXiv:2203.04640v2 [cs.CL] UPDATED)
    In many real-world scenarios, data to train machine learning models becomes available over time. Unfortunately, these models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is difficult to prevent due to practical constraints. For instance, the amount of data that can be stored or the computational resources that can be used might be limited. Moreover, applications increasingly rely on large pre-trained neural networks, such as pre-trained Transformers, since the resources or data might not be available in sufficiently large quantities to practitioners to train the model from scratch. In this paper, we devise a method to incrementally train a model on a sequence of tasks using pre-trained Transformers and extending them with Adapters. Different than the existing approaches, our method is able to scale to a large number of tasks without significant overhead and allows sharing information across tasks. On both image and text classification tasks, we empirically demonstrate that our method maintains a good predictive performance without retraining the model or increasing the number of model parameters over time. The resulting model is also significantly faster at inference time compared to Adapter-based state-of-the-art methods.  ( 2 min )
    Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. (arXiv:2206.07234v3 [cs.LG] UPDATED)
    There is a disconnect between how researchers and practitioners handle privacy-utility tradeoffs. Researchers primarily operate from a privacy first perspective, setting strict privacy requirements and minimizing risk subject to these constraints. Practitioners often desire an accuracy first perspective, possibly satisfied with the greatest privacy they can get subject to obtaining sufficiently small error. Ligett et al. have introduced a "noise reduction" algorithm to address the latter perspective. The authors show that by adding correlated Laplace noise and progressively reducing it on demand, it is possible to produce a sequence of increasingly accurate estimates of a private parameter while only paying a privacy cost for the least noisy iterate released. In this work, we generalize noise reduction to the setting of Gaussian noise, introducing the Brownian mechanism. The Brownian mechanism works by first adding Gaussian noise of high variance corresponding to the final point of a simulated Brownian motion. Then, at the practitioner's discretion, noise is gradually decreased by tracing back along the Brownian path to an earlier time. Our mechanism is more naturally applicable to the common setting of bounded $\ell_2$-sensitivity, empirically outperforms existing work on common statistical tasks, and provides customizable control of privacy loss over the entire interaction with the practitioner. We complement our Brownian mechanism with ReducedAboveThreshold, a generalization of the classical AboveThreshold algorithm that provides adaptive privacy guarantees. Overall, our results demonstrate that one can meet utility constraints while still maintaining strong levels of privacy.  ( 2 min )
    A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms. (arXiv:2204.06043v2 [stat.CO] UPDATED)
    Polynomial chaos expansion (PCE) is a versatile tool widely used in uncertainty quantification and machine learning, but its successful application depends strongly on the accuracy and reliability of the resulting PCE-based response surface. High accuracy typically requires high polynomial degrees, demanding many training points especially in high-dimensional problems through the curse of dimensionality. So-called sparse PCE concepts work with a much smaller selection of basis polynomials compared to conventional PCE approaches and can overcome the curse of dimensionality very efficiently, but have to pay specific attention to their strategies of choosing training points. Furthermore, the approximation error resembles an uncertainty that most existing PCE-based methods do not estimate. In this study, we develop and evaluate a fully Bayesian approach to establish the PCE representation via joint shrinkage priors and Markov chain Monte Carlo. The suggested Bayesian PCE model directly aims to solve the two challenges named above: achieving a sparse PCE representation and estimating uncertainty of the PCE itself. The embedded Bayesian regularizing via the joint shrinkage prior allows using higher polynomial degrees for given training points due to its ability to handle underdetermined situations, where the number of considered PCE coefficients could be much larger than the number of available training points. We also explore multiple variable selection methods to construct sparse PCE expansions based on the established Bayesian representations, while globally selecting the most meaningful orthonormal polynomials given the available training data. We demonstrate the advantages of our Bayesian PCE and the corresponding sparsity-inducing methods on several benchmarks.  ( 2 min )
    Detection problems in the spiked matrix models. (arXiv:2301.05331v1 [math.ST])
    We study the statistical decision process of detecting the low-rank signal from various signal-plus-noise type data matrices, known as the spiked random matrix models. We first show that the principal component analysis can be improved by entrywise pre-transforming the data matrix if the noise is non-Gaussian, generalizing the known results for the spiked random matrix models with rank-1 signals. As an intermediate step, we find out sharp phase transition thresholds for the extreme eigenvalues of spiked random matrices, which generalize the Baik-Ben Arous-P\'{e}ch\'{e} (BBP) transition. We also prove the central limit theorem for the linear spectral statistics for the spiked random matrices and propose a hypothesis test based on it, which does not depend on the distribution of the signal or the noise. When the noise is non-Gaussian noise, the test can be improved with an entrywise transformation to the data matrix with additive noise. We also introduce an algorithm that estimates the rank of the signal when it is not known a priori.  ( 2 min )
    Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data. (arXiv:2301.05491v1 [stat.ME])
    An individualized treatment regime (ITR) is a decision rule that assigns treatments based on patients' characteristics. The value function of an ITR is the expected outcome in a counterfactual world had this ITR been implemented. Recently, there has been increasing interest in combining heterogeneous data sources, such as leveraging the complementary features of randomized controlled trial (RCT) data and a large observational study (OS). Usually, a covariate shift exists between the source and target population, rendering the source-optimal ITR unnecessarily optimal for the target population. We present an efficient and robust transfer learning framework for estimating the optimal ITR with right-censored survival data that generalizes well to the target population. The value function accommodates a broad class of functionals of survival distributions, including survival probabilities and restrictive mean survival times (RMSTs). We propose a doubly robust estimator of the value function, and the optimal ITR is learned by maximizing the value function within a pre-specified class of ITRs. We establish the $N^{-1/3}$ rate of convergence for the estimated parameter indexing the optimal ITR, and show that the proposed optimal value estimator is consistent and asymptotically normal even with flexible machine learning methods for nuisance parameter estimation. We evaluate the empirical performance of the proposed method by simulation studies and a real data application of sodium bicarbonate therapy for patients with severe metabolic acidaemia in the intensive care unit (ICU), combining a RCT and an observational study with heterogeneity.  ( 2 min )
    Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems. (arXiv:2211.01873v2 [cs.LG] UPDATED)
    We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy production), we modify accordingly the port-Hamiltonian formalism so as to achieve a port-metriplectic one. We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems. Predictions can be done, however, at the scale of the complete system. Examples are shown on the performance of the proposed technique.  ( 2 min )
    Global Riemannian Acceleration in Hyperbolic and Spherical Spaces. (arXiv:2012.03618v5 [math.OC] UPDATED)
    We further research on the accelerated optimization phenomenon on Riemannian manifolds by introducing accelerated global first-order methods for the optimization of $L$-smooth and geodesically convex (g-convex) or $\mu$-strongly g-convex functions defined on the hyperbolic space or a subset of the sphere. For a manifold other than the Euclidean space, these are the first methods to \emph{globally} achieve the same rates as accelerated gradient descent in the Euclidean space with respect to $L$ and $\epsilon$ (and $\mu$ if it applies), up to log factors. Due to the geometric deformations, our rates have an extra factor, depending on the initial distance $R$ to a minimizer and the curvature $K$, with respect to Euclidean accelerated algorithms As a proxy for our solution, we solve a constrained non-convex Euclidean problem, under a condition between convexity and \emph{quasar-convexity}, of independent interest. Additionally, for any Riemannian manifold of bounded sectional curvature, we provide reductions from optimization methods for smooth and g-convex functions to methods for smooth and strongly g-convex functions and vice versa. We also reduce global optimization to optimization over bounded balls where the effect of the curvature is reduced.  ( 2 min )

  • Open

    Looking for a CV/ML freelancer
    We are currently working looking for someone to create an app that works for images and video where the user would highlight the outline of the person in the image or video and the app would return the image and video of the person with a transparent background. The user could then go back and keep highlighting to refine the image or video of the person. If the image or video of said person is good they would just save the it on the app itself. We would want this app to be made with swift for iOS and preferably on edge. At the end just send over the project folder. Dm if you are interested submitted by /u/bluebamboo3 [link] [comments]  ( 45 min )
    This AI can clone your voice! VALL-E (explained)
    submitted by /u/OnlyProggingForFun [link] [comments]  ( 44 min )
    A Video Script I Made Using ChatGPT & Pictory
    submitted by /u/WolfAmoux [link] [comments]  ( 44 min )
    6. Is it possible for crime to increase due to AI in the workforce?
    submitted by /u/Big-Heron-7955 [link] [comments]  ( 44 min )
    Did this mostly for fun once I found out the CW's Shared superhero multiverse was coming to a close. Figured I might as well post it here for anyone whose interested.
    submitted by /u/Eleganos [link] [comments]  ( 44 min )
    Make a Drake freestyle with AI
    Drake - Zesty (New Unreleased Freestyle) ​ Recently just uploaded his new freestyle on youtube. Pretty lit and original. Check it out: https://youtu.be/060cUnsoYo8 submitted by /u/Jay-Query [link] [comments]  ( 44 min )
    The media firms & publisher are beginning their fight back
    submitted by /u/MrEloi [link] [comments]  ( 45 min )
    Artists sue Stability AI, Midjourney and DeviantArt
    submitted by /u/Peaking_AI [link] [comments]  ( 44 min )
    Weekly China AI News: Alibaba Predicts Generative AI as Top Tech Trend for 2023, China's AI Computing Surpasses General Computing, and AI Can Recognize Lip Syncing
    submitted by /u/trcytony [link] [comments]  ( 44 min )
    Artificial intelligence
    submitted by /u/ZahraMuxammed [link] [comments]  ( 43 min )
    5 AI Content Detector Tools You Should Know About! (ChatGPT Not Included)
    submitted by /u/Chisom1998_ [link] [comments]  ( 50 min )
    Interview with the guy who developed wifi human movement tracking
    in this newsletter like halfway down how long before this just turns into the Dark Knight surveillance scenario? apparently you can use wifi antennas to track people submitted by /u/jrstelle [link] [comments]  ( 45 min )
    I made my first website with AI. It is Ingredient Genie, and it creates recipes based on your ingredients.
    submitted by /u/MightyMercenary0 [link] [comments]  ( 45 min )
    🚀Muse:Text 2 Image Generation via Masked Generative Transformers
    submitted by /u/oridnary_artist [link] [comments]  ( 45 min )
    Create an image generation model that takes concept art and turns it into a character sheet
    Hey all! I have been experimenting with a few AI apps (I was lucky enough to get into the leonardo beta). I am mainly interested in creating character sheets for 3D modelling like this: ​ https://preview.redd.it/7vv9zotsbfca1.jpg?width=564&format=pjpg&auto=webp&s=37df5fc0affb728338a3517d5710453b72b0830b Initially, I thought that training a model with a bunch of character sheets from google images would suffice. I figured if I used an image of a character (think general concept art, without the T-pose and different views) as the "input" image, it would spit out something resembling a character sheet, only with the character I want in it. This didn't work, it ended up re-imagining the character with the art style of the character sheet (hand drawn lines, cartoony): ​ https://preview.redd.it/rp6hh8qgdfca1.png?width=1486&format=png&auto=webp&s=81fb7607cbd58a938f25314c471772aaa5d8916c So I guess my question is: Is there an AI service/app/tool that might accomplish this? If not, what other methods should I look into? submitted by /u/matthew798 [link] [comments]  ( 48 min )
    OpenAI’s ChatGPT: The 10 Worst Things to Expect
    submitted by /u/liquidocelotYT [link] [comments]  ( 43 min )
    Interactive Evolutionary Computation and ChatGPT
    submitted by /u/BenjaminJamesBush [link] [comments]  ( 45 min )
    AI Host that independently runs Live shows (on Fb, Youtube, Twitch)
    Hi there! Making my first contribution to this subreddit and sharing something you probably haven't heard of (I might be wrong though :) So, this is about AI host for Live quizzes. The AI generated avatar can run a live quiz and doesn't need any pre-made scripts or your help. Works only on Facebook Live, Youtube and Twitch. What do you think? ​ https://preview.redd.it/94gb6wmc6fca1.png?width=1080&format=png&auto=webp&s=a14bfd0b1140e887d1b7c615fd0ef00e88373ce6 submitted by /u/AnnetWw [link] [comments]  ( 50 min )
    What is reinforcement learning from human feedback (RLHF)?
    submitted by /u/bendee983 [link] [comments]  ( 44 min )
    AI in Education: The Good, the Bad, and the Downright Confusing
    submitted by /u/pauerrrr [link] [comments]  ( 44 min )
    Android AI Assistant - Use GPT from anywhere!
    submitted by /u/better__ideas [link] [comments]  ( 44 min )
    I got ChatGPT to create a new joke. I would never have thought this possible.
    submitted by /u/Ivorius [link] [comments]  ( 48 min )
    Didn't a man invent ChatGPT?
    submitted by /u/Imagine-your-success [link] [comments]  ( 44 min )
    I made SaaS AI Tools, a collection of 400+ AI tools & daily AI news in one place.
    Hey, Over the past couple months, I've been collecting AI tools & generators and decided to put them into a website. The result is SaaS AI Tools, a growing collection of 400+ generative AI tools to help supercharge your creativity and take your business to the next level. Also, to differentiate a bit - I've added another section that involves a feed of daily AI articles, so you can keep up-to-date on the top AI headlines. This is how I'm personally keeping up with all the AI stuff today. I'll be adding more tools and news sources soon. I've launched the website on Product Hunt and would appreciate any of your support 🙏 submitted by /u/Hairy_Milk8431 [link] [comments]  ( 56 min )
    production still from 1976 of Alejandro Jodorowsky’s Spaceballs
    submitted by /u/dag [link] [comments]  ( 44 min )
  • Open

    [D] GCN datasets
    Hello everyone, ​ I have a question about GCNs and would appreciate any thoughts. Do we typically use only one graph for GCN training/inference? I'm asking this because when I saw official DGL website, there was only one example graph after loading it. Based on my experience with DNNs, I expected a batch of examples. However, it was not the case for GCNS. I could find PPI dataset with multiple graph examples (24) but for other widely used datasets (e.g., Cora, Citeseeer, and Pubmed), there was only one. Thank you! submitted by /u/ramya_1995 [link] [comments]  ( 56 min )
    [P] Looking for a CV/ML freelancer
    We are currently working looking for someone to create an app that works for images and video where the user would highlight the outline of the person in the image or video and the app would return the image and video of the person with a transparent background. The user could then go back and keep highlighting to refine the image or video of the person. If the image or video of said person is good they would just save the it on the app itself. We would want this app to be made with swift for iOS and preferably on edge. At the end just send over the project folder. Dm if you are interested. submitted by /u/bluebamboo3 [link] [comments]  ( 56 min )
    [D] Model for detecting rectangle corners?
    What model structure would be recommended for detecting the coordinates of all 4 corners of a rectangle (e.g. index cards)? Most object detection models like YOLO produce rectangular bounding boxes; what tweaks can be made to trace the object regardless of orientation? For my specific problem, classical edge/corner detectors aren't a good fit - so I'm falling back on ML. Currently have a dataset of about 1500 domain-specific labeled images; hoping to train a model on TF. Thanks for the suggestions! Edit: here are a few examples from my dataset. The green dots aren't part of the images; they just show how the corners are annotated: https://preview.redd.it/2f8uimhn7hca1.jpg?width=1373&format=pjpg&auto=webp&s=3a3757a6d3ab0f07aa3cde09f1b4acd0573f3d75 https://preview.redd.it/ujb8tmhn7hca1.jpg?width=3024&format=pjpg&auto=webp&s=e1a60b4322e3f20c10f193cb3102658975858c92 https://preview.redd.it/9lzgfmhn7hca1.jpg?width=3024&format=pjpg&auto=webp&s=a0cf4d760b48267d7c273f892284472f296f72be submitted by /u/hundley10 [link] [comments]  ( 57 min )
    [R] The Predictive Forward-Forward Algorithm
    Abstract: In this work, we propose a generalization of the forward-forward (FF) algorithm that we call the predictive forward-forward (PFF) algorithm. Specifically, we design a dynamic, recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit, combining elements of predictive coding, an emerging and viable neurobiological process theory of cortical function, with the forward-forward adaptation scheme. Furthermore, PFF efficiently learns to propagate learning signals and updates synapses with forward passes only, eliminating some of the key structural and computational constraints imposed by a backpropbased scheme. Besides computational advantages, the PFF process could be further useful for understanding the learning mechanisms behind biological neurons that make use of local (and global) signals despite missing feedback connections [11]. We run several experiments on image data and demonstrate that the PFF procedure works as well as backprop, offering a promising brain-inspired algorithm for classifying, reconstructing, and synthesizing data patterns. As a result, our approach presents further evidence of the promise afforded by backprop-alternative credit assignment algorithms within the context of brain-inspired computing. Paper: https://arxiv.org/pdf/2301.01452.pdf submitted by /u/radi-cho [link] [comments]  ( 57 min )
    [D] On generated content and the future of moderation
    Over the past three years, the field of ML has advanced considerably in the field of audio, visual, and natural language generation. For users like me, GPT-3 was a first look into the types of content that can now be generated with minor effort. While impressive at first, the outputs from the original GPT-3 can quickly be seen to be less than ideal and often times can be easily distinguished from original content written by users. Three years later, generation techniques have improved to the point where the task of detecting generated content is far more difficult as the quality of the generated content has risen considerably. Access to such technologies has also spread to the point where states such as New York sees it as enough of a threat to ban it from schools. While I think we are still at the calm before the storm in regards to the potential for chaos such models have, I'd like to open the floor up for a discussion on the implications of generative models and ways we can address it. Will it even be possible to moderate content in the future when models improve to the point where artifacts from the generation process are no longer present? Sure we can have models that detect NSFW content, but what about content that contains information that is false and harmful? Perhaps a resurgence in symbolic AI and rule based reasoning is needed? Or perhaps a renewed interest in the field of argument mining? submitted by /u/sparkinflint [link] [comments]  ( 57 min )
    [D] Recommendation for best toolkit to manually annotate tunnels in 3D Volume?
    I have a 3D tiff stack which contains some holes inside the volume that branch out in various directions. I wanted to annotate the holes inside the volume and I came across several tools that I can use to manually annotate them like 3D slicer, ITK-SNAP, and ImageJ. But I am unfamiliar with all of these tools and I was wondering which one would be most helpful for me? My ultimate goal is to apply volume registration using the annotated holes as keypoints to fuse volumes together. submitted by /u/waterstrider123 [link] [comments]  ( 56 min )
    [D] Visualizations for NSFW models
    Hi all, I am looking for someone to help me for my research project. I want to use grad-CAM (or any other tool) to visualize state-of-the-art cnn predictions like those of Clarifai. submitted by /u/jeditwisted [link] [comments]  ( 54 min )
    [P] A small tool that shuts down your machine when GPU utilization drops too low.
    Hey /r/machinelearning, Long time reader, first time posting non-anonymously. I've been training models using various cloud services, but as an individual user it's stressful for me to worry about shutting down the instances if training fails or stops. Crashes, bad code, etc can cause GPU utilization to drop without the program successfully "finishing", and this idle time can cost a lot of money if you don't catch it quickly. Thus, I built this tiny lil tool to help. It watches the GPU utilization of your instance, and performs an action if it drops too low for too long. For example, shutdown the instance if GPU usage drops under 30% for 5 minutes. It's easy to use and install, just pip install gpu_sentinel If this is useful please leave comments here or on the Github page: https://github.com/moonshinelabs-ai/gpu_sentinel I'm hoping it helps save some other folks money! submitted by /u/nateharada [link] [comments]  ( 62 min )
    [D] I’m a Machine Learning Engineer for FAANG companies. What are some places I can get started doing freelance work for ML?
    I have around 6 YoE doing MLE full time work for various companies. Starting to get tired of working for these big companies and would prefer trying some freelance work. Where are some websites or places I can get started? I’ve seen UpWork, but this seemed more suited for quick one off, software work and less for complex ML tasks last time I was on there (tried that several years ago in 2019). submitted by /u/doctorjuice [link] [comments]  ( 60 min )
    [D] Fine-tuning open source models on specific tasks to compete with ChatGPT?
    As the title says, I'm curious about using open source models like GPT-J, GPT-NeoX, Bloom, or OPT to compete with ChatGPT for *specific use-cases* such as explaining what a bit of code does. ChatGPT does this task quite well, but it's closed-source nature prevents it from being useful in documenting or commenting proprietary code. There's also limitations such as the amount of text ChatGPT will read or respond with. Getting beyond these limitations is something I'm interested in pursuing, perhaps with the help of somewhere in this subreddit. Some assumptions you can safely make: We can get (lots of) funding for the training, hardware, etc... The end product should be on-premises The inference does not actually need to run very quickly. If it costs millions to buy enough GPUs just due to VRAM limitations, we could simply run on CPUs and utilize ram, as long as inference could be done a few times per day. So I guess my questions are where would we start? What model is best to fine-tune? How would you specifically fine-tune to improve specific use cases? submitted by /u/jaqws [link] [comments]  ( 56 min )
    Looking for papers to warm start a BERT large mode from BERT base. Are there papers around it?
    Warm starting the model training of BERT large using BERT base. One idea is to concatenate a bunch of parameters and start training. I was thinking is there a research paper that tries out the best methods? submitted by /u/Plane-Interaction-68 [link] [comments]  ( 52 min )
    [D] Tim Dettmers' GPU advice blog updated for 4000 series
    The legendary Tim Dettmers has updated his blog on which GPU to purchase for Deep learning to include advice for the latest GPU series: https://timdettmers.com/2023/01/16/which-gpu-for-deep-learning/ submitted by /u/init__27 [link] [comments]  ( 60 min )
    [D] The Illustrated Stable Diffusion (Video)
    I'll be honest with you, it took me months to wrap my head around diffusion models. A couple of iterations of a blog post later and this is my best shot at a gentle intro to Stable Diffusion and how it works. https://youtu.be/MXmacOUJUaw The part that took the most reworking is forward diffusion and how to best describe it. Thanks to the many people acknowledged in the blog post who have helped me both understand it and explain it better. Hope you find it helpful. Let me know if you have any questions or feedback. submitted by /u/jayalammar [link] [comments]  ( 57 min )
    [D] Can ChatGPT flag it's own writings?
    My question is, if it is possible to feed a direct quote into ChatGPT and ask it if ChatGPT is the author of said quote? If not, is it reasonable to insist that it can do so in the future? submitted by /u/MrSpotgold [link] [comments]  ( 69 min )
    [R] [2301.00250] DensePose From WiFi
    submitted by /u/GreatCosmicMoustache [link] [comments]  ( 50 min )
    [D] Grid searching data pre processing permutations when training models on structured data.
    Hello, I am currently working on structured data classification problem for work. I was applying multiple different data pre processing steps including imputing null values (mean, KNN, random forrest), adding synthetic data (SMOTE, ADAYSN or None), normalization (l1, l2, max or none), multiple datasets (including different sets of features), as well as different models (XGBoost, Random Forrest, Logistic Regression, KNN, MLP). What I built was a tool that trains all the different permutations of data processing, datasets and models to find the best one, and applied K-Fold cross validation. The tool stores all the data and metrics using MLFlow. This is similar to a grid search across hyperparemeters, but instead of tuning the hyper parameters, I am tuning the data processing steps. I like this method because I gain a level of confidence knowing that I have exhausted all the possible models, data, and pre processing permutations when selecting the best performing model. I was wondering if other people apply a similar technique for structured data problems? Besides the compute is there anything to be cautious of when applying this method? submitted by /u/spiritualquestions [link] [comments]  ( 57 min )
    [D] SOTA on multiple image generation from text
    Wondering what the state of the art is for multiple image generation for an input text, or a series of input texts. To clarify, are there any models or architectures that explore consistency between image generation. (E.g stylistically, same people in the images, same settings, etc) I imagine there would be some pre-existing architectures that could take an image embedding along with a text embedding submitted by /u/weelamb [link] [comments]  ( 56 min )
    [P] Nano GPT
    submitted by /u/trekhleb [link] [comments]  ( 56 min )
  • Open

    Pretraining quadrupeds: a case study in RL as an engineering tool
    submitted by /u/robotphilanthropist [link] [comments]  ( 54 min )
    Is there a publicly available state space model for the Lunar Lander environment?
    The Lunar Lander environment uses the box2d engine to simulate physics. I was wondering if there is code somewhere which explicitly models the environment as as state-space model? LunarLander code: https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py submitted by /u/HazrMard [link] [comments]  ( 57 min )
    Poker (NLH) model?
    Is there any open source model for online poker yet? Of course Pluribus was a big deal a few years ago but it’s closed source (and much has changed since), but with the recent OS Rocket League AI stomping pros I have to wonder why nothing has come to the surface with poker yet. Even a 5% improvement on human play would be a big deal in the long run. Is poker that hard? Or is there some model I’m unaware of? Thanks submitted by /u/enterguild [link] [comments]  ( 54 min )
    Need help creating an action space compatible with stable baselines
    I'm trying to train a bot to play a game and am having trouble creating an action space to handle the inputs, which are the wasd keys, space bar for jumping, left click for shooting, and also two continuous values to indicate the coordinates the mouse should move to. At first, I tried to use spaces.Tuple to combine a MultiDiscrete space for the key presses, and a Box space for the mouse movement. However, I quickly found that none of the stable baseline models support tuples. So I looked online and found an idea to change all of my discrete values to continuous values and round to the nearest integer. This sounded promising, so I created an action space like so: # Game window bounds to provide range mouse can move windowX = self._game_window_bounds[0] windowY = self._game_window_bounds[…  ( 63 min )
    SKRL (reinforcement learning library) version 0.9.0 is now available!
    skrl-v0.9.0 is now available! skrl is an open-source modular library for Reinforcement Learning written in Python (using PyTorch) and designed with a focus on readability, simplicity, and transparency of algorithm implementation. In addition to supporting the OpenAI Gym / Farama Gymnasium, DeepMind, and other environment interfaces, it allows loading and configuring NVIDIA Isaac Gym and NVIDIA Omniverse Isaac Gym environments, enabling agents’ simultaneous training by scopes (subsets of environments among all available environments), which may or may not share resources, in the same run. Visit https://skrl.readthedocs.io to get started!! ​ The major changes in this release are: Added Support for Farama Gymnasium interface Wrapper for robosuite environments Weights & Biases integration Set the running mode (training or evaluation) of the agents Allow clipping of the gradient norm for DDPG, TD3, and SAC agents Initialize model biases Add RNN (RNN, LSTM, GRU, and any other variant) support for A2C, DDPG, PPO, SAC, TD3, and TRPO agents Allow disabling training/evaluation progressbar Farama Shimmy and robosuite examples KUKA LBR iiwa real-world example More benchmarking results Changed Forward model inputs as a Python dictionary [breaking change] Returns a Python dictionary with extra output values in model calls [breaking change] Adopt the implementation of terminated and truncated over done for all environments Fixed Omniverse Isaac Gym simulation speed for the Franka Emika real-world example Call agents' method record_transition instead of the parent method to allow storing samples in memories during the evaluation Move TRPO policy optimization out of the value optimization loop Access to the categorical model distribution Call reset only once for Gym/Gymnasium vectorized environments Removed Deprecated method start in trainers submitted by /u/Toni-SM [link] [comments]  ( 60 min )
    Hyperparameters for pick&place with Franka Emika manipulator
    I'm trying to solve pick&place (and possibly also the other tasks in this repository) with Franka Emika Panda manipulator simulated in Mujoco. I've tried for long with stable_baseline3 but without any results, someone told me to try with RLLib because has better implementation (?), but still I can't find any solution... submitted by /u/riccardogauss [link] [comments]  ( 51 min )
    Best Books to Learn Reinforcement Learning
    submitted by /u/Lakshmireddys [link] [comments]  ( 53 min )
    I'm understanding theory; hard time figuring out how to implement it
    Currently, I'm following David Silver's course along with Sutton and Barto's Introduction to Reinforcement Learning. While these are both fantastic I'm having a hard time thinking of how I can actually implement them in code; mainly getting the environment and agent to be connected. Any help would be appreciated. ​ EDIT: In general I'm also interested in how exactly models stay trained in an environment as I would imagine the program would have to run continuously or else it would have to relearn the task every time. submitted by /u/CaptiDoor [link] [comments]  ( 56 min )
    Question about designing the reward function
    Hi all, I am struggling to design a reward function for the following system: It has two joints, q1 and q2 that can not be actuated at the same time. Once q1 is actuated, the system has to wait for 5 seconds to activate q2. The task is to reach a goal position x and y with the system by interchangeably using q1 and q2. So far the reward function looks like this: reward = 1/(1+pos_error) And the observation vector like this: obs = (dof_pos, goal_pos, pos_error) To make the robot interchangeably use q1 and q2, I use two masks: q1_mask = (1, 0) and q2_mask= (0,1) that are interchangeably used to only actuate one joint at the same time. But I am not sure how to implement the second condition that the system needs 5 seconds to activate q2 after q1. So far I am just storing the time that q1 has been activated and replace the actions by 0: self.actions = torch.where( (self.q2_activation > 0) & (self.q2_activation_time_diff > 5) , self.actions * q2_mask, self.actions ) I think the agent gets irritated by simply as nothing as changed by the actions. How would approach for this problem? submitted by /u/Fun-Moose-3841 [link] [comments]  ( 59 min )
  • Open

    It’s No Big Deal, but ChatGPT Changes Everything – Part I
    Time to catch the ChatGPT craze!  Yes, everyone is flocking to the ChatGPT AI-driven chatbot and asking all sorts of life altering questions such as instructions for removing a peanut butter sandwich from a VCR in Biblical verse in Figure 1 (my answer…you’ve still got a VCR?). Figure 1: ChatGPT in Action! Heck, Ryan Reynolds… Read More »It’s No Big Deal, but ChatGPT Changes Everything – Part I The post It’s No Big Deal, but ChatGPT Changes Everything – Part I appeared first on Data Science Central.  ( 23 min )
  • Open

    Hello! Has anyone tried to create a neural network to find all PI numbers?
    submitted by /u/ScIentIaEstP0tentIa [link] [comments]  ( 58 min )
    Reverse Engineering a Neural Network's Clever Solution to Binary Addition
    submitted by /u/nickb [link] [comments]  ( 55 min )
    🚀Muse:Text 2 Image Generation via Masked Generative Transformers
    submitted by /u/oridnary_artist [link] [comments]  ( 55 min )
    Does the "optimal structure" depend on the size of the sample or just the complexity of the problem?
    Hi everyone. First post in reddit in general. Let me start by saying that I am more or less new to neural networks and self-taught in the subject. I am learning Neural Network as part of my PhD in energy engineering. Basically, I deal with optimisation of energy systems (more precisely, Concentrated Solar Power plants) and I want to use neural networks to create a surrogate model of certain parts of my detailed and time-consuming models to later apply optimisation. So, if I am not wrong, I am dealing with a very classic "function approximation" problem. I want to train a neural network for an specific application. To do so, I gathered a large data set for my detailed model and then trained multiple networks of different number of neurons (considering only one hidden layers right now). As a result, I obtained an optimum number of neurons which is the smallest to achieve a certain error (measured through the RMSE). Now my question: imagine you gather a new data set from the same model, but smaller. Could you assume that the optimum structure (number of neurons) is the same? I acknowledge that if the size of the new data set is too small there could be problems of overfitting but, if you can assume that the new data set, although smaller, is still statistically representative of the problem, wouldn't the optimum structure be the same as the optimum structure is just related to the complexity of the problem? Hope I was clear enough, probably my question is very simple. Thanks! submitted by /u/paworod [link] [comments]  ( 55 min )
  • Open

    Zeta sum vs zeta product
    The Riemann zeta function ζ(s) is given by an infinite sum and an infinite product for complex numbers s with real part greater than 1 [*]. The infinite sum is equal to the infinite product, but which would give you more accuracy: N terms of the sum or N terms of the product? We’ll take […] Zeta sum vs zeta product first appeared on John D. Cook.  ( 5 min )
    Approximating pi with Bernoulli numbers
    In a paper on arXiv Simon Plouffe gives the formula which he derives from an equation in Abramowitz and Stegun (A&S). It took a little while for me to understand what Plouffe intended. I don’t mean my remarks here to be criticism of the author but rather helpful hints for anyone else who might have […] Approximating pi with Bernoulli numbers first appeared on John D. Cook.  ( 5 min )

  • Open

    Emma Myers as an Marvel whatif character
    submitted by /u/oridnary_artist [link] [comments]  ( 43 min )
    I find the timing of the ChatGPT release curious.
    Prior to the ChatGPT release, there were several powerful & similar AI systems already in existence .. but little publicised. I wonder if the ChatGPT release was a commercial ploy to beat the (possibly more capable) Google AI systems such as PALM to the market? Or maybe the release was intended to break the wall of secrecy hiding AI systems from public view? Whatever was behind the release of ChatGPT, at least it may force Google to finally give the public access to their systems too. submitted by /u/MrEloi [link] [comments]  ( 49 min )
    Google may use Deepmind's Sparrow as ChatGPT competitor
    submitted by /u/henlo_there_fren [link] [comments]  ( 46 min )
    how would i effectively train an ai on minecraft building?
    i want to make an ai that you can put a prompt into and it makes a minecraft schematic. this sounds easy to me until you really get into the specifics of it. i could train it on a bunch of schematics with their relative names but there wouldnt really be any sense to it. For example- if you train it on a bunch of dragons and a bunch of sunglasses, and then tell it to build a dragon with sunglasses, it wouldnt know "where" to put the glasses relative to the dragon. ​ Whats the best way to go about this? submitted by /u/cbreauxgaming [link] [comments]  ( 49 min )
    So far I have found this tool (chatGPT) amazing in helping me write code, however...
    submitted by /u/oh_you_so_bad_6-6-6 [link] [comments]  ( 45 min )
    Pruning and Quantizing YOLO V7 With Modoptima
    In this blog, I have explained how you can use my library modoptima to optimize the YOLO v7 model improving the inference speed by 3-10 times on good processors. https://medium.com/@vikasojha894/pruning-and-quantizing-yolo-v7-with-modoptima-19c61aff7301 submitted by /u/VikasOjha666 [link] [comments]  ( 48 min )
    YouTube channel on AI architecture concepts?
    Ive got a Masters degree in AI and I've really been enjoying a channel called 2 minute papers, but it's a bit too much focused on results and doesn't go at all into the key insights of each paper. I'm not looking for tutorials or specific implementation details - I'm interested in the architectures and types of neural networks which were used to obtain these amazing results. Does anybody know a YouTube channel (or something similar) which goes briefly into the core technology of each paper? submitted by /u/BagelOrb [link] [comments]  ( 44 min )
    Could Deepmind’s Sparrow be Google’s answer to ChatGPT?
    submitted by /u/liquidocelotYT [link] [comments]  ( 46 min )
    Artificial Intelligence Best Paper Awards Reviewed by Computer Vision News (and much more)
    Dear all, Here is Computer Vision News of January 2023. It includes reviews of 2 Best Paper Award winning research papers. Read 44 pages about AI, Deep Learning, Computer Vision and more - with code! Read online version for free (recommended) PDF version Free subscription on page 44. Enjoy! https://preview.redd.it/1wgoydszp7ca1.jpg?width=400&format=pjpg&auto=webp&s=1303f337dd627a6f9252d3a341c005e3cb06f433 submitted by /u/Gletta [link] [comments]  ( 44 min )
    Open art space
    Hey Reddit, I am new here. I was wondering if anyone knows if there is a art place in London where we can paint or draw for free? submitted by /u/lucasagazzani [link] [comments]  ( 44 min )
    Should I tell my company about Chat GPT to implement it into our workflow or keep silent about it?
    I work for a public institution in a small country in Europe. I figure it’ll take some time until people will hear about it or even try to implement it. I think it’s inevitable though that some day it’ll rule our job market. I’m currently using AI for my own soon to be start-up and also for mundane tasks. My question is: Should I tell the board of my company about it? Since I’m on a strategic position of some sorts I think it’ll also be great for my reputation to be the first to kickstart it. However, I’m afraid that by opening this pandora’s box I’ll create more competition by showing other people how to use it. I don’t know, what would you do? submitted by /u/Darklan [link] [comments]  ( 56 min )
    What will BMW M and Mercedes AMG Cars Look Like in the Future?
    submitted by /u/BallbustCuck [link] [comments]  ( 44 min )
    Bavaria-based mobility company German Bionic has developed an AI-powered exoskeleton that's designed to help workers carry out physically demanding jobs
    submitted by /u/Rollyman1 [link] [comments]  ( 44 min )
    Excuse me? How is airsoft not following their policy?
    submitted by /u/vajenny_zlacyniec [link] [comments]  ( 44 min )
    CHATGPT D&D Graphic Novel with Dalle-e and Azure Voice-Over
    submitted by /u/erikmalkavian [link] [comments]  ( 53 min )
    Inpainting with the Visuali editor (beta)
    submitted by /u/aigeneration [link] [comments]  ( 46 min )
    AI for your own files
    Hi there, I was wondering if there is some sort of tool out there that allows you to have some sort of localised AI - as in it searches all my files, but also within them. Say theres a poerpoint file and one of the slides has some relative content, it will show that slide and not just the file itself. I run a consultancy and have 1000s of documents from past campaigns and clients, it would be great to be able to teach some form of AI about my content and then it finds things or creates things when I need it.... thanks! submitted by /u/Vincenth2008 [link] [comments]  ( 47 min )
    How ChatGPT would have changed my life
    submitted by /u/DeeMore [link] [comments]  ( 61 min )
    Box2Mask: A Unique Method for Single-Shot Instance Segmentation that Combines Deep Learning with the Level-Set Evolution Model to Provide Accurate Mask Predictions with only Bounding Box Supervision
    submitted by /u/ai-lover [link] [comments]  ( 48 min )
    AI-Developed, Synthetic DNA is About to Revolutionize Drug Production and Gene Therapy
    submitted by /u/digitalgoldnow [link] [comments]  ( 43 min )
    Build a simply GPT-3 chatbot in Python in 20 lines of code in 5 minutes
    submitted by /u/techie_ray [link] [comments]  ( 43 min )
    AI text to art generation explained simply with pen and paper
    submitted by /u/techie_ray [link] [comments]  ( 46 min )
    Unpopular opinion: AI will make jobs more boring
    One claim I hear a lot in AI circles is that in the future AI will replace a lot of bullshit jobs, which will free up time for humans to focus on what matters. No more transcribing emails for a living. New, meaningful jobs will be created instead. In other words, the hope is that in 10 years more people will be psychologically happy with their jobs than today. I'm growing wary of the opposite risk. If we consider "bullshit" the part of a job that can be automated away, then sometimes the bullshit part of a job is what makes it pleasurable and fulfilling (provided it is not the only thing). As an artist you may draw pleasure in doing coloring after a pencil sketch. As an engineer, you may like a day without planning or meetings where you can focus on programming a small piece of code. As a blog post writer you may enjoy, well, writing. AI are quickly becoming able to replace all these tasks, and to stay competitive people will increasingly be required to offload parts of their jobs to them. I can see a future where jobs in any field start looking the same - the person is in charge of having higher knowledge about the problem, planning tasks for AI, be able to evaluate AI output, and assemble final product. This is certainly not a bullshit job, but I also think that for many people this is not going to be a fulfilling job. submitted by /u/R_y_n_o [link] [comments]  ( 46 min )
    Our horror game story is crafted with ChatGPT and this scene is part of it. What do you think about it?
    submitted by /u/Leaderide [link] [comments]  ( 45 min )
    About art AIs, how noise works?
    I have heard that art AI converts images into "noise" to create their models. Question: Can you revert the "noise" into the image it was based into? E.G I take the mona lisa, convert it into "noise" so the AI can understand it. Can I then request the AI to use the mona lisa "noise" to generate the mona lisa back again? Or there is no way to return it back to the image after it is converted into noise? (not the exact image, but its equivalent based into the noise stored). submitted by /u/___Marshmallow___ [link] [comments]  ( 45 min )
  • Open

    Emma Myers as an Marvel whatif character
    submitted by /u/oridnary_artist [link] [comments]  ( 53 min )
    Are there any DNNs that perform gradient descent in runtime (mesa alignment)?
    I was watching the Robert Miles series on AI alignment, specifically the one about mesa optimizers, and when he started talking about the mesa objective I noticed that the way in which he defined the base objective (gradient descent + loss function + train data) it's not analogous to the way in which the mesa objective would work (as the latter cannot change any parameters of the network, as no Gradient Descent step is used or anything analogous). This got me thinking if are there any papers that implement runtime parameter change, through gradient descent or not. submitted by /u/not-alredy-taken [link] [comments]  ( 55 min )
    Electronic circuits analogies with Deep Neural Networks
    I was wondering these days about the training parameter in tensor flow layers, and stumbled upon the idea of letting the activation function itself be designed in such a way that it’s backward pass yields no gradients, independent (or almost) of the loss in use. Do you know any? Then it came to me that this resembles a buffer op amp, and I was wondering if are there out there any papers that explore circuit analogies with the training process, treating the inputs and gradients as “currents”. Seems like an interesting concept ! submitted by /u/not-alredy-taken [link] [comments]  ( 58 min )
  • Open

    [D] What kinds of interesting models can I train with just an RTX 4080?
    I'm aware transformers are pretty vram hungry and a 4080 only has 16 GB. So I am guessing a lot of transformer based models will be out of the question. At least anything that is interesting. Not sure about other models though. Is there anything I can do with a 4080 that's beyond just some toy experiment? submitted by /u/faker10101891 [link] [comments]  ( 55 min )
    [D] What is standard practice in RL when reporting average returns across multiple seeds in a table or a plot?
    Hey everyone, This may be a silly question but I'm confused as to what standard practice is when reporting average returns across multiple seeds in a table or a plot. It's usually not even mentioned but I sometimes see authors mention they are using: Average ± Standard Deviation Average ± Standard Error Average ± 1.96 * Standard Error Bootstrapped CIs For example, this paper (https://www.jmlr.org/papers/volume23/21-1342/21-1342.pdf) by the authors of clearnrl doesn't specify anything other than that the "reported numbers are the final average episodic returns of at least 3 random seeds". What would you consider best practice in RL? submitted by /u/thekingpenguin3 [link] [comments]  ( 57 min )
    [P] Parameter optimization on a guitar amp emulation
    So I'm working on project which goal is to digitally emulate guitar tube amplifiers using a Wiener-Hammerstein model. For those of you unfamiliar with this type of model, its key block is a nonlinear block that is characterized by a set of 8 parameters. Basically there's a raw input guitar signal and there's an output signal that should be as close as possible to the actual output of the modeled amplifier. I have a database with a series of magnitude-variable chirp signals serving as inputs and the respective output measurements. So my question is what is the best way for me to automate the process of optimization of this set parameters. I thought of using a genetic algorithm but I wondered if that's the most accurate and efficient way of doing it. Also this is to be implement on a microcontroller so I have more limited resources than a computer. However, it would be really cool to be able to customize these parameters in real time on my Teensy 4.0 so it would be ideal that the algorithm could meet this condition, although it's not completely necessary submitted by /u/syko101 [link] [comments]  ( 57 min )
    [P] Summaries of Ten Interesting & Influential Papers I read in 2022
    submitted by /u/seraschka [link] [comments]  ( 91 min )
    [Discussion] can't lives down on won't street
    submitted by /u/Psqwanio [link] [comments]  ( 53 min )
    [D] AI Security - Gumroad AI help bot refuses to answer certain questions... unless you PRIME it with a question that it WILL answer first
    submitted by /u/LatentWeb [link] [comments]  ( 53 min )
    [P] Modified kmeans algorithm returns the wrong answer
    I am trying to create a kmeans algorithm that is based on the Earth Movers Distance instead of the Euclidean distance. However, when I run it, it just returns the same value for all data points. ​ The input is an dxn matrix containing all of my n probability distributions. ​ Here is an example of running the algorithm. The clusters should be much more distributed. ​ distribution = {} num_bins = 5 for i in data: distribution[i] = np.histogram(data[i], bins = num_bins)[0] / len(data[i]) ​ Z = np.zeros((len(data), num_bins)) for i in range(len(Z)): Z[i] = distribution[list(distribution)[i]] Z = Z.T ans = k_means_algorithm(Z, 8, proportionally_random_k) ​ res = points_to_clusters(Z, ans) print(res) ​ [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 1. 1. 1. 1. …  ( 58 min )
    [R] hlb-CIFAR10 0.2.0: New world record for single-GPU CIFAR10, ~<12.38s with one A100 (SXM4, Colab)
    submitted by /u/tysam_and_co [link] [comments]  ( 68 min )
    Best Predictive Model to Predict Total Monthly Stock Returns on Panel Data [P]
    Hello Reddit community, I am faced with some Finance coursework and I would appreciate any help or guidance from experienced practitioners in the ML/Finance industry. I have a panel dataset (Data File) - that includes information such as date, stock name, market-cap, sector, and a column for the target variable which is the monthly absolute return of the stock in percentage terms (i.e one month from the date column). I am tasked with building a predictive model to forecast the target variable. I would like to know information on which ML model you would recommend, and why? Thank you in advance for any help or guidance provided. submitted by /u/RhiteousRhino [link] [comments]  ( 56 min )
    [P] Free PyTorch Deep Learning class, from Perceptrons to multi-GPU training and cloud deployment
    submitted by /u/seraschka [link] [comments]  ( 55 min )
    [D] Problem with predict/evaluate and batch_size keras
    So, i trained my unet using keras. Best dicescore of saved model supposed to be 0.817. Then i ran a hand-made score prediction: batch_size = 1 n_val_img = len(os.listdir(os.path.join(fp2,"sujetos"))) vspe = n_val_img//batch_size dice = 0 for _ in range(0,vspe): test_image_batch, test_mask_batch = val_gen_ds.__next__() for i in range(test_image_batch.shape[0]): a = my_unet(np.expand_dims(test_image_batch[i], 0)).numpy() predicted_img_th = (a[0,:,:,0]>0.5)*1 dice += Dice(test_mask_batch[i],predicted_img_th).numpy() print(dice/n_val_img) This return around 0.836, so... already different. Then i tried to replicate my evaluate score with different batch sizes: my_unet.evaluate(val_gen_ds,batch_size=batch_size,steps=vspe) ​ https://preview.redd.it/pmt5b358e8ca1.png?width=567&format=png&auto=webp&s=bbd24505bdb55b66a3cab12007cc8d628e533907 Clearly, it doesn't make sense to me... What's wrong? what am i missing? submitted by /u/SerDetestable [link] [comments]  ( 58 min )
    [R] HYPERREAL — high fidelity 6dof video with ray-conditioned sampling
    submitted by /u/SpatialComputing [link] [comments]  ( 55 min )
    [D] Simple Questions Thread
    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 52 min )
    [N] Will w3 see lower costs and faster training? Will Floating Point 8 Solve AI/ML Overhead?
    submitted by /u/RuairiSpain [link] [comments]  ( 53 min )
    [D] Time Embedding in Diffusion Model
    I was looking at how time is embedded in diffusion models, and I found these two implementations [1] and [2]: The first one is a simplified version of the second one, but the idea behind the time embedding is similar. What I've understood is that t is a number, it goes in a SinusoidalPositionEmbeddings with a given time_dim, then Linear + ReLU where the same dimensions are kept. Then for each down-step of the UNet, an additional Linear + ReLU is performed to match the channels of the image embedding, and this latter embedding is added to the output of the CNN. Here when have the time embedding with a shape of (b, c, 1, 1) and the image embedding with a shape of (b, c, h, w). When we perform the addition, the time embedding is broadcasted to match the image embedding. As far as I understand, here the latent space of the image gets reweighted channel-wise, but the same weights are added for each different position. Why they did follow this choice? This is quite different from the standard positional encoding used e.g. in self-attention, where the positional embedding gives a different weight to each spatial dimension. I never found this detail explained in any Diffusion Model paper/tutorial, also if we look at [2], the same idea is made more complex, with more Linear projections and different activation functions (GeLU and SiLU). Moreover, I'm not sure about the difference between applying a time embedding and then directly a conv2d layer against the time embedding + attention + conv2d. Aren't these types of embedding suited up for attention layers? How does a Conv2D layer, which is positional invariant by construction, benefit from this type of operation? [1] https://colab.research.google.com/drive/1sjy9odlSSy0RBVgMTgP7s99NXsqglsUL?usp=sharing#scrollTo=KOYPSxPf_LL7 [2] https://github.com/lucidrains/denoising-diffusion-pytorch/blob/main/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py submitted by /u/Lumett [link] [comments]  ( 60 min )
    [P] I built an app that allows you to build Image Classifiers completely on your phone. Collect data, Train models, and Preview the predictions in realtime. You can also export the model/dataset to be used anywhere else. Would love some feedback.
    submitted by /u/Playgroundai [link] [comments]  ( 63 min )
    [P] I built arxiv-summary.com, a list of GPT-3 generated paper summaries
    Hi there, I wanted to share my new project with you, it is called arxiv-summary.com. Right now, I find it really difficult to keep up with all the important new publications in our field. Especially, it is sometimes difficult to get an overview of a paper to decide if it's worth reading. I really like arxiv-sanity by Andrej Karpathy, but even with that, it can still take some time to understand the main ideas and contributions from the abstract. With arxiv-summary, my goal is to make ML research papers more "human-parsable". The website works by fetching new papers daily from arxiv.org, using PapersWithCode to filter out the most relevant ones. Then, I parse the papers' pdf and LaTeX source code to extract relevant sections and subsections. GPT-3 then summarizes each section and subsection as bullet points, which are finally compiled into a blog post and uploaded to the site. You can check out the site at arxiv-summary.com and see for yourself. There's also a search page and an archive page where you can get a chronological overview. If you have any feedback or questions, I'd be happy to hear them. Also, if you work at OpenAI and could gift me some more tokens, that would be much appreciated :D Thanks and happy reading! submitted by /u/niclas_wue [link] [comments]  ( 65 min )
    [Project] Introducing Visionner (Your image dataset toolkit)
    submitted by /u/charles_data_dev [link] [comments]  ( 58 min )
    [P] C++ wrapper around libsvm and liblinear using Eigen
    I needed a C++ wrapper library around libsvm and liblinear using Eigen so I made one. Maybe it's useful for you as well: https://github.com/bloomen/svmegn submitted by /u/cblume [link] [comments]  ( 56 min )
    [D] Packing multiple shorter training examples in to single sequence in LM pretraining
    I've come across several papers where authors mention they, for reasons of computational efficiency, will pack multiple shorter training examples together in a single sequence. Example from "Scaling Instruction-Finetuned Language Models" (Chung et al, 2022): We use packing (Raffel et al., 2020) to combine multiple training examples into a single sequence, separating inputs from targets using an end-of-sequence token. Masking is applied to prevent the tokens from attending to others across the packed example boundary. I'm curious to understand how this is actually done in practice. Seeing as multiple separate masks are involved, I'd think one would need to loop over them all and repeat (?) the matrix multiplication several times? Is there some built in functionality in Pytorch and other frameworks to deal with a situation like this with multiple masks? Thankful if someone could share and explain, or link to an implementation of input packing. submitted by /u/mLalush [link] [comments]  ( 59 min )
  • Open

    Online Reinforcement Learning Courses
    Can anyone recommend any online Reinforcement Learning courses, preferably those that have assignments or exercises that are graded so there is some way to check your answers? submitted by /u/Smart-Ground-3587 [link] [comments]  ( 55 min )
    CS234 Stanford
    Can you submit the programming assignments of CS234 without using gradescope? How do you check your work if you are taking the course without being enrolled in it officially? submitted by /u/Smart-Ground-3587 [link] [comments]  ( 55 min )
    Boardgame environment and data sources [help]
    Hi everybody! I'm trying to come up with a fun learning project to execute. I'd like to try to create an agent that plays a boardgame. My plan is to apply reinforcement learning and behavioral cloning. I don't know what boardgame to work with yet, I just want it to be simple enough to work with and fulfill two requirements: The environment must be implemented and accessible. I must have access to a dataset containing historical plays. Would anybody have suggestions on where I could find these? Any responses are appreciated. Thank you! submitted by /u/valahart [link] [comments]  ( 55 min )
    In Asynchronous n-step DQN, is there a global shared gradient vector or gradient vector for each thread?
    In this paper: *Asynchronous Methods for Deep Reinforcement Learning (arxiv.org) This is the pseudocode for n-step DQN: https://preview.redd.it/rjt84n6zs9ca1.png?width=1176&format=png&auto=webp&s=eda075c5741bae96f954432073b0d6617937941a In the pseudocode above it mentions: "Initialize network gradients dtheta <-- 0." Is this referring to a global shared gradient vector or a gradient vector for each thread? I noticed that they use theta instead of theta' making me think it is a global shared gradient vector. But if this is the case, couldn't a thread clear the gradient vector while another thread is accumulating gradients? Also, in section 7 of the paper, where they talk about implementing SGD with Momentum in an Asynchronous setting it seems to imply that it is a global shared gradient vector normally. https://preview.redd.it/souf3gxkt9ca1.png?width=1152&format=png&auto=webp&s=4a0cff8ba6100c651d0ed8b7d34089ec2e3aecd5 submitted by /u/ImNotKevPlayz [link] [comments]  ( 56 min )
    What is data governance and its importance to a company?
    submitted by /u/ranjeettechnincal [link] [comments]  ( 54 min )
    How to interpet negative entropy_loss that keeps decreasing in PPO?
    Why is it negative? Why it keeps decreasing? Will it stop at any point? Is this expecred behavior? If not what should I adjust? I am using Stable Baselines3 ​ https://preview.redd.it/bypjlwy5j9ca1.png?width=372&format=png&auto=webp&s=94fb5fe55b66c90d705c6776ffa5dd258e90d2ba https://preview.redd.it/f0ooky84j9ca1.png?width=1413&format=png&auto=webp&s=0b62f9d8df555835ce72cfe41cfd840c295b70ca submitted by /u/andrew7777777 [link] [comments]  ( 54 min )
    Help with transitioning an existing DQN into a DRQN
    Hi RL reddit, To preface this post, please let me know if I need to clarify any details to receive help and/or guidance. I am new to posting on this subreddit and still consider myself a novice in the deep RL domain. What I need help with is transitioning an existing DQN into a DRQN. The DQN architecture and the environment that it learns comes directly from this paper https://arxiv.org/pdf/1810.04244.pdf To briefly summarize the paper, the author proposes a DQN network as a controller to guide fixed winged aircrafts to follow the evolution of a spreading wildfire (grid environment). The same DQN can be used for the both aircrafts. The inputs are follows: A 5 dimensional vector bank angle of ownship distance to other aircraft bearing angle to other aircraft relative to current he…  ( 66 min )
    Best practices for Self-Play RL
    Hi! I know there is a lot of work on self-play (training RL agents in environments where they play against themselves), and I've found several tricks to stabilize the training process. I was wondering if someone who has experience in this field could provide a compilation of such tricks and best practices, for example: Fictitious self-play: Keeping N previous checkpoints of the agent and sampling from a pool of these to select an opponent every T environment steps. (I have also seen people sample a new opponent after every single env.reset() call, what do you think is best?). What is a reasonable value for T? and for N? KL distillation loss: Adding a KL loss penalty between the current agent being trained and the last checkpoint stored so that the policy doesn't change abruptly. How is this usually implemented? What's a reasonable value for a coefficient for that penalty? Imagine a DQN agent playing against itself, is it reasonable to set epsilon=1 and start annealing it every time a new enemy is sampled? (in case we play against the same enemy for a long time). ​ There might be many more tricks so if we can list them all here that'd be great! Thank you all! submitted by /u/xWh0am1 [link] [comments]  ( 61 min )
  • Open

    Reverse engineering options
    This weekend I saw a sign in the window of a Burger King™ that made me think of an interesting problem. If you know the number of possibilities like this, how would you reverse engineer what the options that created the possibilities? In the example above, there are 211,184 = 213×33 possible answers, and so […] Reverse engineering options first appeared on John D. Cook.  ( 6 min )

  • Open

    Interesting website for people struggling with the concept of Roko's basilisk
    submitted by /u/Fusionism [link] [comments]  ( 45 min )
    Best Artificial Intelligence books for beginners to Experts to read
    submitted by /u/Lakshmireddys [link] [comments]  ( 44 min )
    Is there a tool (or research paper) for training a model on images for inpainting?
    I want to train a ML model on an object and have it be able to inpaint the specific object into other photos. Does a service like this exist? submitted by /u/Zestybeef10 [link] [comments]  ( 45 min )
    AI Sales Chat Bot and Telephone Agent
    Hi there, I'm looking for AI software that can do the following tasks: A fully automated chat bot that can be used to close deals. A telephone bot that can do outreach and also sense if a lead is interested or not. Basically I want to be able to automate the whole outreach + close process by AI so that I can process much more contact data as human sales reps could ever do. If a lead is interested, they should receive a link with an offer where they can directly purchase the digital product that I'm planning to sell. submitted by /u/cokedinosaur [link] [comments]  ( 44 min )
    AI Etsy shop
    Hey guys , I got into the ai space about 3 weeks ago and am stating my Esty shop journey with mainly air created photos of supercars. I would just love some feedback as to what I can add and how to make it as best as possible. I am planning on bringing in large metal canvas soon. Just really wanting feedback on the art work. Thank you!! https://picturetron.etsy.com submitted by /u/BetterPresentation35 [link] [comments]  ( 45 min )
    What is the future of NLP for the coming 24 months? Dall-E clones Mid-Journey and SD took 6-8 months to appear, so is that how long it will take for clones of ChatGPT? Perhaps less time given the higher investment and market potential?
    submitted by /u/MegavirusOfDoom [link] [comments]  ( 50 min )
    Spray-on smart skin uses AI to rapidly understand hand tasks
    submitted by /u/qptbook [link] [comments]  ( 44 min )
    Which A.I. Labs are mostly likely to have offshoots that scale well?
    With ChatGPT and GPT-4 hype on full throttle I anticipate a lot of new A.I. labs and off-shoots of OpenAI and Google Brain and DeepMind will keep forming. Speaking of which, Niki Parmar and Ashish Vaswani, two prominent artificial intelligence researchers who left Google in 2021 to launch Adept , have now departed to make yet another A.I. lab in stealth mode. By 2024, there will be around a dozen good A.I. labs not named OpenAI. Google itself has PaLM with RLHF, Chinchilla, Google Duplex, LaMDA and Sparrow (DeepMind). More foundational models will arrive as A.I. labs make their models public. But who and which one is likely to be good? Anthropic, Adept, Inflection A.I. AI21 Labs, Cohere, there are a lot of potential candidates. This is not counting the ones likely forming in China, a…  ( 49 min )
    Could AI be the answer to our content needs by 2025?
    submitted by /u/Realistic-Plant3957 [link] [comments]  ( 44 min )
    I Created A Reddit Chatbot..(Potentially offensive content)
    submitted by /u/TheRPGGamerMan [link] [comments]  ( 48 min )
    What practical applications have you already found for ChatGPT?
    submitted by /u/DrMelbourne [link] [comments]  ( 46 min )
    ChatGPT will undoubtedly change the world. The question is HOW? What are your thoughts?
    submitted by /u/DrMelbourne [link] [comments]  ( 46 min )
    AI that you give an image to and it says colors that can compliment it?
    Title says it all, is there an AI that you can insert in an image and it says some colors that could work with it? submitted by /u/NewShibeAccount [link] [comments]  ( 46 min )
    Do you want to understand how an end-2-end paraphrase app can be created?
    Check out this medium article about how to create such an app: https://medium.com/towards-artificial-intelligence/how-to-create-an-end-2-end-text-paraphrase-app-db83a4e05918 Or check this repository about Quotera a paraphrase app to be deployed via Streamlit or FastAPI and Docker in Python: https://github.com/stavrostheocharis/quotera submitted by /u/Nice-Tomorrow2926 [link] [comments]  ( 45 min )
    THIS BOOK WAS WRITTEN ENTIRELY BY CHATGPT.
    submitted by /u/__sandeepan__ [link] [comments]  ( 43 min )
    Bagging vs Boosting Explained
    Hi guys, I have made a video on YouTube here where I cover the Bagging and Boosting ensemble learning algorithms. I present how both work, and discuss their similarities and differences. I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 47 min )
    Top A.I. Powered Tools Not Named ChatGPT
    submitted by /u/BackgroundResult [link] [comments]  ( 46 min )
    The misuse of AI is the familiar promise one thing and deliver something else
    submitted by /u/shanoshamanizum [link] [comments]  ( 45 min )
    Will there be a universal AI tool?
    When thinking about the possibilities of artificial intelligence, I couldn't stop thinking about some sort of interactive Wikipedia, imagine all the objective knowledge of the universe compiled into a firmware pre-installed app just like the 'Calculator'. Another type of device that could become an incredible tool for solving problems, might even be useful for business or professionals. We could name it the 'Communicator', just like a calculator you enter endless math problems, with the communicator you could enter endless word problems. I think we could grab much more productivity out of this technology than just asking philosophical questions to Chat GPT. submitted by /u/jvazorka03 [link] [comments]  ( 45 min )
    I wrote a blog post about use of AI in Music Industry. Take a Look!
    https://link.medium.com/HnMPSMA4zwb submitted by /u/Yigit_im [link] [comments]  ( 44 min )
    An interactive AI training simulation using Genetic Algorithm
    submitted by /u/SparshG [link] [comments]  ( 44 min )
    Bright Eye: free Mobil AI app that generates art, code, poems and essays, and more.
    Hey guys, I’m the cofounder of a tech startup focused on providing free AI services. We’ve developed a pretty cool app that offers AI services like image generation, code generation, image captioning, and more for free. We’re sort of like a Swiss Army knife of generative and analytical AI. We’ve released a new feature called AAIA(Ask AI Anything), which is capable of answering all types of questions, even requests to generate literature (fantasy, folklore, drama, fiction, fable, etc). It’s sort of like chat-gpt. We’d appreciate it if you could try it out and let us know your thoughts: https://apps.apple.com/us/app/bright-eye/id1593932475 submitted by /u/True-Marketing-5079 [link] [comments]  ( 46 min )
    I'm Compiling a List of Helpful AI Tools, Feel free to add any you've created/discovered
    submitted by /u/secret-millionaire [link] [comments]  ( 45 min )
    We created a list of AI projects and applications in Github
    There are incredible applications built using AI. It is definitely a trend that the world should not ignore. We started to maintain a collection of cool ai projects in Github: https://github.com/ai-collection/ai-collection Our mission is to increase reach and visibility for these awesome projects! It is updated daily and we hope that with the help of the community, it will be a great source for discovering AI applications. submitted by /u/beth0io [link] [comments]  ( 46 min )
  • Open

    Best Neural Networks Courses on Udemy to Consider
    submitted by /u/Lakshmireddys [link] [comments]  ( 52 min )
    New Abilities Emerge If Language Models Are Scaled Past Critical Point ⭕
    Last year, large language models (LLM) have broken record after record. ChatGPT got to 1 million users faster than Facebook, Spotify, and Instagram did. They helped create billion-dollar companies, and most notably they helped us recognize the divine nature of ducks. 2023 has started and ML progress is likely to continue at a break-neck speed. This is a great time to take a look at one of the most interesting papers from last year. Emergent Abilities in LLMs In a recent paper from Google Brain, Jason Wei and his colleagues allowed us a peak into the future. This beautiful research showed how scaling LLMs might allow them, among other things, to: Become better at math Understand even more subtleties of human language reduce hallucination and answer truthfully ... (See the plot o…  ( 77 min )
    Bagging vs Boosting Explained
    Hi guys, I have made a video on YouTube here where I cover the Bagging and Boosting ensemble learning algorithms. I present how both work, and discuss their similarities and differences. I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 56 min )
    Help visualizing Spiral dataset
    Hello I want to visualize neural network learning to fit the spiral data set, I am able to plot the given data. What i want is to see is how my neural net is adjusting to fit data points , forming spiral shape, The problem is i don't know what to plot or how to do it, My softmax function outputs probability distribution and my categorical function outputs loss, so i'm lost. submitted by /u/Purple_Gen3 [link] [comments]  ( 55 min )
  • Open

    [D]: Are there models like CODEX but work in a reversed way?
    Many models these days focus on code generation. But I was wondering if there's anything for understanding existing codebase? I know that Codex or ChatGPT can understand what a function does, but what about a complex codebase with imports and nested calls? Are these models capable of understanding the relationship between functions? I'm trying to build a side project where you give it a production level codebase, it does some magic, then I can ask AI anything about things in this codebase with high accuracy. submitted by /u/GoodluckH [link] [comments]  ( 58 min )
    [D] Leveraging multiple photos for super resolution / restoration
    Is there any work that does this? Let's say you have 20 good, related photos, and one bad one you want to restore / upscale / denoise / sharpen / inpaint etc. Those 20 good images of the same person / object / building should give the model a good sense of how to fill in missing details in the bad photo. I imagine there's work somewhere in this direction but can't find anything. submitted by /u/anonDogeLover [link] [comments]  ( 55 min )
    [P] handlingclassifier.ml - predicts size category from provided product name - works with IKEA-like range of products
    submitted by /u/curryprogrammer [link] [comments]  ( 55 min )
    [P] Question regarding ID3 and cross validation
    I created an ID3 algorithm using scilab for a project at university. The project is more a proof of concept then an having an actual usecase. Its written in scilab without using any toolboxes and classifies if you have won in tic tac toe. My code basically uses a dataset that has every possible endgame board configuration of tic tac toe and builds a descision tree. I can then input a specific endgame board configuration and it tells me if i won or not. It works fine so far and since my dataset has all possible configurations, predicts the correct label 100% of times. I now added a ten times k-fold cross validation algorithm. However, the validation only gives me an accuracy of about 80%. Am I missing something here? Does a cross validation even make sense if my training set contains all possible data points? Hope someone can give some answers. submitted by /u/i-dunnodude [link] [comments]  ( 57 min )
    [R] Photorealistic human image editing using attention with GANs
    submitted by /u/psarpei [link] [comments]  ( 58 min )
    [D] Speaker diarization: reusing fitted speaker embedding clusters?
    I am trying to create speaker-aware transcripts from (multiple) audio files of a podcast. Right now I'm using OpenAI Whisper for the transcripts and pyannote.audio for speaker diarization (speaker segmentation + centroid clustering) In order to speed up the process (diarization time doesn't seem to scale linearly), I'd like to fit the centroids with the first audio file, and use those to predict the speakers (clusters of the speaker embeddings) of the other audio files, as the speakers don't change across episodes. However, the default pyannote.audio diarization pipeline refits the clusters for each audio file. Do you know of any other Python framework that allows reusing the fitted clusters, or any way pyannote.audio allows this? Is this even possible? Any other way to achieve the desired results? submitted by /u/2blazen [link] [comments]  ( 57 min )
    [Project] Stable Diffusion Pokémon cards
    submitted by /u/thundergolfer [link] [comments]  ( 62 min )
    [R] Differentiable Point-Based Radiance Fields for Efficient View Synthesis
    submitted by /u/t0ns0fph0t0ns [link] [comments]  ( 55 min )
    [R] from a human motion sequence, SUMMON synthesizes physically plausible and semantically reasonable objects
    submitted by /u/t0ns0fph0t0ns [link] [comments]  ( 62 min )
    [R] Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement - TeachMe - Bhavana Dalvi Mishra et al Allen Institute for AI
    Paper: https://arxiv.org/abs/2204.13074 Blog: https://blog.allenai.org/towards-teachable-reasoning-systems-dd16659fd9f8 Youtube: https://www.youtube.com/watch?v=c5j_tWsENFg Abstract: Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. Our approach is to augment a QA model with a dynamic memory of user feedback, containing user-supplied corrections to erroneous model beliefs that users identify during interaction. Retrievals from memory are used as additional context for QA, to help avoid previous mistakes in similar new situations - a novel application of memory-based continuous learning. With simulated feedback, we find that our system (called TeachMe) continually improves with time, and without model retraining, requiring feedback on only 25% of training examples to reach within 1% of the upper-bound (feedback on all examples). Similarly, in experiments with real users, we observe a similar trend, with performance improving by over 15% on a hidden test set after teaching. This suggests new opportunities for using frozen language models in an interactive setting where users can inspect, debug, and correct the model's beliefs, leading to improved system's performance over time. https://preview.redd.it/umosmtgzj0ca1.jpg?width=507&format=pjpg&auto=webp&s=65f66ed1230ae6bdce73a86386fbfcc860cd4c59 https://preview.redd.it/5lbhvwgzj0ca1.jpg?width=680&format=pjpg&auto=webp&s=99b402f6db395a62756113b2f8cb879667d444ef https://preview.redd.it/jd7oaygzj0ca1.jpg?width=1308&format=pjpg&auto=webp&s=4fac23ba2ecd68eb5b744f6c10a9c09ba604376c https://preview.redd.it/q137kkhzj0ca1.jpg?width=839&format=pjpg&auto=webp&s=24052a161029b23b32944954f88f432348016ea0 submitted by /u/Singularian2501 [link] [comments]  ( 61 min )
    [N] Class-action law­suit filed against Sta­bil­ity AI, DeviantArt, and Mid­journey for using the text-to-image AI Sta­ble Dif­fu­sion
    submitted by /u/Wiskkey [link] [comments]  ( 76 min )
    [D] What's hot for Machine Learning Research in 2023?
    Which of the sub-fields/approaches, application areas are expected to gain much attention (pun unintended) this year in the academia? PS - Inspired from a similar question last year (https://www.reddit.com/r/MachineLearning/comments/t04ekm/d_whats_hot_for_machine_learning_research_in_2022/) submitted by /u/Aromatic_Eye_6268 [link] [comments]  ( 57 min )
    [D] Is MusicGPT a viable possibility?
    As in, "Pink Floyd, Another Brick in the Wall, ska, heavy trumpet, female vocalist" It seems that if copyright issues are a controversial element of AI art, then copyrighted music will run into the same issue. Or is this not true? submitted by /u/markhachman [link] [comments]  ( 61 min )
  • Open

    Is there a better server alternative than AWS/Azure/Nvidia...for students?
    I'm a student and I've gotten to the part of my machine learning project where I need to optimize a lot. When I say a lot, I mean a lot, I have complex models. Most people these days usually pay for the services of "big tech companies" like Amazon, Microsoft, etc. to get their models trained. But I think in my case it would cost a lot of money, all tho I am aware that some have student discounts. Are there any alternatives like universities that allow students to do this or something else entirely? If not, which of these companies would you recommend best in terms of computing/price ? Thanks for all the replies submitted by /u/Apprehensive_Rush314 [link] [comments]  ( 54 min )
    _Rocket League_ RL agent 'Nexto' now in top 0.5% of players
    submitted by /u/gwern [link] [comments]  ( 52 min )
  • Open

    Foreshadowing Page Rank
    Douglas Hofstadter, best known as the author of Godel, Escher, Bach, wrote the foreword to Clark Kimberling’s book Triangle Centers and Central Triangles. Hofstadter begins by saying that in his study of math he “sadly managed to sidestep virtually all of geometry” and developed an interest in geometry, specifically triangle centers, much later. The ancient […] Foreshadowing Page Rank first appeared on John D. Cook.  ( 6 min )
  • Open

    Novelty Socks by AI
    I like a fun sock. The more random the design, the better. What kinds of novelty sock ideas would we get if we used AI as a creativity aid? It turns out they aren't too novel unless the AI is glitchy. I collected 14 examples of socks I  ( 6 min )
    Bonus: More novelty socks
    AI Weirdness: the strange side of machine learning  ( 2 min )
  • Open

    Benign Underfitting of Stochastic Gradient Descent. (arXiv:2202.13361v4 [cs.LG] UPDATED)
    We study to what extent may stochastic gradient descent (SGD) be understood as a "conventional" learning rule that achieves generalization performance by obtaining a good fit to training data. We consider the fundamental stochastic convex optimization framework, where (one pass, without-replacement) SGD is classically known to minimize the population risk at rate $O(1/\sqrt n)$, and prove that, surprisingly, there exist problem instances where the SGD solution exhibits both empirical risk and generalization gap of $\Omega(1)$. Consequently, it turns out that SGD is not algorithmically stable in any sense, and its generalization ability cannot be explained by uniform convergence or any other currently known generalization bound technique for that matter (other than that of its classical analysis). We then continue to analyze the closely related with-replacement SGD, for which we show that an analogous phenomenon does not occur and prove that its population risk does in fact converge at the optimal rate. Finally, we interpret our main results in the context of without-replacement SGD for finite-sum convex optimization problems, and derive upper and lower bounds for the multi-epoch regime that significantly improve upon previously known results.  ( 2 min )
    Bayesian inference via sparse Hamiltonian flows. (arXiv:2203.05723v2 [stat.ML] UPDATED)
    A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during Bayesian inference, with the goal of reducing computational cost. Although past work has shown empirically that there often exists a coreset with low inferential error, efficiently constructing such a coreset remains a challenge. Current methods tend to be slow, require a secondary inference step after coreset construction, and do not provide bounds on the data marginal evidence. In this work, we introduce a new method -- sparse Hamiltonian flows -- that addresses all three of these challenges. The method involves first subsampling the data uniformly, and then optimizing a Hamiltonian flow parametrized by coreset weights and including periodic momentum quasi-refreshment steps. Theoretical results show that the method enables an exponential compression of the dataset in a representative model, and that the quasi-refreshment steps reduce the KL divergence to the target. Real and synthetic experiments demonstrate that sparse Hamiltonian flows provide accurate posterior approximations with significantly reduced runtime compared with competing dynamical-system-based inference methods.  ( 2 min )
    Manifold Fitting under Unbounded Noise. (arXiv:1909.10228v2 [stat.ML] UPDATED)
    There has been an emerging trend in non-Euclidean statistical analysis of aiming to recover a low dimensional structure, namely a manifold, underlying the high dimensional data. Recovering the manifold requires the noise to be of certain concentration. Existing methods address this problem by constructing an approximated manifold based on the tangent space estimation at each sample point. Although theoretical convergence for these methods is guaranteed, either the samples are noiseless or the noise is bounded. However, if the noise is unbounded, which is a common scenario, the tangent space estimation at the noisy samples will be blurred. Fitting a manifold from the blurred tangent space might increase the inaccuracy. In this paper, we introduce a new manifold-fitting method, by which the output manifold is constructed by directly estimating the tangent spaces at the projected points on the underlying manifold, rather than at the sample points, to decrease the error caused by the noise. Assuming the noise is unbounded, our new method provides theoretical convergence in high probability, in terms of the upper bound of the distance between the estimated and underlying manifold. The smoothness of the estimated manifold is also evaluated by bounding the supremum of twice difference above. Numerical simulations are provided to validate our theoretical findings and demonstrate the advantages of our method over other relevant manifold fitting methods. Finally, our method is applied to real data examples.  ( 2 min )
    Efficient Ridge Solution for the Incremental Broad Learning System on Added Nodes by Inverse Cholesky Factorization of a Partitioned Matrix. (arXiv:1911.04872v4 [cs.LG] UPDATED)
    To accelerate the existing Broad Learning System (BLS) for new added nodes in [7], we extend the inverse Cholesky factorization in [10] to deduce an efficient inverse Cholesky factorization for a Hermitian matrix partitioned into 2 * 2 blocks, which is utilized to develop the proposed BLS algorithm 1. The proposed BLS algorithm 1 compute the ridge solution (i.e, the output weights) from the inverse Cholesky factor of the Hermitian matrix in the ridge inverse, and update the inverse Cholesky factor efficiently. From the proposed BLS algorithm 1, we deduce the proposed ridge inverse, which can be obtained from the generalized inverse in [7] by just change one matrix in the equation to compute the newly added sub-matrix. We also modify the proposed algorithm 1 into the proposed algorithm 2, which is equivalent to the existing BLS algorithm [7] in terms of numerical computations. The proposed algorithms 1 and 2 can reduce the computational complexity, since usually the Hermitian matrix in the ridge inverse is smaller than the ridge inverse. With respect to the existing BLS algorithm, the proposed algorithms 1 and 2 usually require about 13 and 2 3 of complexities, respectively, while in numerical experiments they achieve the speedups (in each additional training time) of 2.40 - 2.91 and 1.36 - 1.60, respectively. Numerical experiments also show that the proposed algorithm 1 and the standard ridge solution always bear the same testing accuracy, and usually so do the proposed algorithm 2 and the existing BLS algorithm. The existing BLS assumes the ridge parameter lamda->0, since it is based on the generalized inverse with the ridge regression approximation. When the assumption of lamda-> 0 is not satisfied, the standard ridge solution obviously achieves a better testing accuracy than the existing BLS algorithm in numerical experiments.  ( 3 min )
    Tracr: Compiled Transformers as a Laboratory for Interpretability. (arXiv:2301.05062v1 [cs.LG])
    Interpretability research aims to build tools for understanding machine learning (ML) models. However, such tools are inherently hard to evaluate because we do not have ground truth information about how ML models actually work. In this work, we propose to build transformer models manually as a testbed for interpretability research. We introduce Tracr, a "compiler" for translating human-readable programs into weights of a transformer model. Tracr takes code written in RASP, a domain-specific language (Weiss et al. 2021), and translates it into weights for a standard, decoder-only, GPT-like transformer architecture. We use Tracr to create a range of ground truth transformers that implement programs including computing token frequencies, sorting, and Dyck-n parenthesis checking, among others. To enable the broader research community to explore and use compiled models, we provide an open-source implementation of Tracr at https://github.com/deepmind/tracr.  ( 2 min )
    Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data. (arXiv:2211.13116v2 [cs.LG] UPDATED)
    Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL. Existing data augmentation methods based on federated generative models or raw data sharing strategies for solving the non-IID problem still suffer from low performance, privacy protection concerns, and high communication overhead in decentralized tabular data. To tackle these challenges, we propose a federated tabular data augmentation method, named Fed-TDA. The core idea of Fed-TDA is to synthesize tabular data for data augmentation using some simple statistics (e.g., distributions of each column and global covariance). Specifically, we propose the multimodal distribution transformation and inverse cumulative distribution mapping respectively synthesize continuous and discrete columns in tabular data from a noise according to the pre-learned statistics. Furthermore, we theoretically analyze that our Fed-TDA not only preserves data privacy but also maintains the distribution of the original data and the correlation between columns. Through extensive experiments on five real-world tabular datasets, we demonstrate the superiority of Fed-TDA over the state-of-the-art in test performance and communication efficiency.  ( 2 min )
    Study of software developers' experience using the Github Copilot Tool in the software development process. (arXiv:2301.04991v1 [cs.SE])
    In software development there is a constant pressure to produce code faster and faster without compromising on quality. New tools supporting developers are created in response to this demand. Currently a new generation of such solutions is about to be launched - Artificial Intelligence driven tools. On 29 June 2021 Github Copilot was announced. It uses trained model to generate code based on human understandable language. The focus of this research was to investigate software developers' approach to this tool. For this purpose a survey containing 18 questions was prepared and shared with programmers. A total of 42 answers were gathered. The results of the research indicate that developers' opinions are divided. Most of them met Github Copilot before attending the survey. The attitude to the tool was mostly positive but not many participants were willing to use it. Concerns are caused by security issues associated with using of Github Copilot.
    A Stochastic Proximal Polyak Step Size. (arXiv:2301.04935v1 [math.OC])
    Recently, the stochastic Polyak step size (SPS) has emerged as a competitive adaptive step size scheme for stochastic gradient descent. Here we develop ProxSPS, a proximal variant of SPS that can handle regularization terms. Developing a proximal variant of SPS is particularly important, since SPS requires a lower bound of the objective function to work well. When the objective function is the sum of a loss and a regularizer, available estimates of a lower bound of the sum can be loose. In contrast, ProxSPS only requires a lower bound for the loss which is often readily available. As a consequence, we show that ProxSPS is easier to tune and more stable in the presence of regularization. Furthermore for image classification tasks, ProxSPS performs as well as AdamW with little to no tuning, and results in a network with smaller weight parameters. We also provide an extensive convergence analysis for ProxSPS that includes the non-smooth, smooth, weakly convex and strongly convex setting.
    Statistical Learning with Sublinear Regret of Propagator Models. (arXiv:2301.05157v1 [q-fin.TR])
    We consider a class of learning problems in which an agent liquidates a risky asset while creating both transient price impact driven by an unknown convolution propagator and linear temporary price impact with an unknown parameter. We characterize the trader's performance as maximization of a revenue-risk functional, where the trader also exploits available information on a price predicting signal. We present a trading algorithm that alternates between exploration and exploitation phases and achieves sublinear regrets with high probability. For the exploration phase we propose a novel approach for non-parametric estimation of the price impact kernel by observing only the visible price process and derive sharp bounds on the convergence rate, which are characterised by the singularity of the propagator. These kernel estimation methods extend existing methods from the area of Tikhonov regularisation for inverse problems and are of independent interest. The bound on the regret in the exploitation phase is obtained by deriving stability results for the optimizer and value function of the associated class of infinite-dimensional stochastic control problems. As a complementary result we propose a regression-based algorithm to estimate the conditional expectation of non-Markovian signals and derive its convergence rate.
    Thompson Sampling with Diffusion Generative Prior. (arXiv:2301.05182v1 [cs.LG])
    In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit framework, with the goal of learning a strategy that performs well across bandit tasks of a same class. To this end, we train a diffusion model that learns the underlying task distribution and combine Thompson sampling with the learned prior to deal with new tasks at test time. Our posterior sampling algorithm is designed to carefully balance between the learned prior and the noisy observations that come from the learner's interaction with the environment. To capture realistic bandit scenarios, we also propose a novel diffusion model training procedure that trains even from incomplete and/or noisy data, which could be of independent interest. Finally, our extensive experimental evaluations clearly demonstrate the potential of the proposed approach.
    Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimes. (arXiv:2301.04771v1 [stat.ML])
    Variational inference has been widely used in machine learning literature to fit various Bayesian models. In network analysis, this method has been successfully applied to solve the community detection problems. Although these results are promising, their theoretical support is only for relatively dense networks, an assumption that may not hold for real networks. In addition, it has been shown recently that the variational loss surface has many saddle points, which may severely affect its performance, especially when applied to sparse networks. This paper proposes a simple way to improve the variational inference method by hard thresholding the posterior of the community assignment after each iteration. Using a random initialization that correlates with the true community assignment, we show that the proposed method converges and can accurately recover the true community labels, even when the average node degree of the network is bounded. Extensive numerical study further confirms the advantage of the proposed method over the classical variational inference and another state-of-the-art algorithm.  ( 2 min )
    The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features. (arXiv:2301.04740v1 [cs.LG])
    Over the past few years, the use of machine learning models has emerged as a generic and powerful means for prediction purposes. At the same time, there is a growing demand for interpretability of prediction models. To determine which features of a dataset are important to predict a target variable $Y$, a Feature Importance (FI) method can be used. By quantifying how important each feature is for predicting $Y$, irrelevant features can be identified and removed, which could increase the speed and accuracy of a model, and moreover, important features can be discovered, which could lead to valuable insights. A major problem with evaluating FI methods, is that the ground truth FI is often unknown. As a consequence, existing FI methods do not give the exact correct FI values. This is one of the many reasons why it can be hard to properly interpret the results of an FI method. Motivated by this, we introduce a new global approach named the Berkelmans-Pries FI method, which is based on a combination of Shapley values and the Berkelmans-Pries dependency function. We prove that our method has many useful properties, and accurately predicts the correct FI values for several cases where the ground truth FI can be derived in an exact manner. We experimentally show for a large collection of FI methods (468) that existing methods do not have the same useful properties. This shows that the Berkelmans-Pries FI method is a highly valuable tool for analyzing datasets with complex interdependencies.  ( 2 min )
    Private estimation algorithms for stochastic block models and mixture models. (arXiv:2301.04822v1 [cs.DS])
    We introduce general tools for designing efficient private estimation algorithms, in the high-dimensional settings, whose statistical guarantees almost match those of the best known non-private algorithms. To illustrate our techniques, we consider two problems: recovery of stochastic block models and learning mixtures of spherical Gaussians. For the former, we present the first efficient $(\epsilon, \delta)$-differentially private algorithm for both weak recovery and exact recovery. Previously known algorithms achieving comparable guarantees required quasi-polynomial time. For the latter, we design an $(\epsilon, \delta)$-differentially private algorithm that recovers the centers of the $k$-mixture when the minimum separation is at least $ O(k^{1/t}\sqrt{t})$. For all choices of $t$, this algorithm requires sample complexity $n\geq k^{O(1)}d^{O(t)}$ and time complexity $(nd)^{O(t)}$. Prior work required minimum separation at least $O(\sqrt{k})$ as well as an explicit upper bound on the Euclidean norm of the centers.  ( 2 min )
    Universality of neural dynamics on complex networks. (arXiv:2301.04900v1 [cond-mat.stat-mech])
    This paper discusses the capacity of graph neural networks to learn the functional form of ordinary differential equations that govern dynamics on complex networks. We propose necessary elements for such a problem, namely, inductive biases, a neural network architecture and a learning task. Statistical learning theory suggests that generalisation power of neural networks relies on independence and identical distribution (i.i.d.)\ of training and testing data. Although this assumption together with an appropriate neural architecture and a learning mechanism is sufficient for accurate out-of-sample predictions of dynamics such as, e.g.\ mass-action kinetics, by studying the out-of-distribution generalisation in the case of diffusion dynamics, we find that the neural network model: (i) has a generalisation capacity that depends on the first moment of the initial value data distribution; (ii) learns the non-dissipative nature of dynamics implicitly; and (iii) the model's accuracy resolution limit is of order $\mathcal{O}(1/\sqrt{n})$ for a system of size $n$.  ( 2 min )
    Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction. (arXiv:2301.04791v1 [stat.ML])
    Max sliced Wasserstein (Max-SW) distance has been widely known as a solution for redundant projections of sliced Wasserstein (SW) distance. In applications that have various independent pairs of probability measures, amortized projection optimization is utilized to predict the ``max" projecting directions given two input measures instead of using projected gradient ascent multiple times. Despite being efficient, the first issue of the current framework is the violation of permutation invariance property and symmetry property. To address the issue, we propose to design amortized models based on self-attention architecture. Moreover, we adopt efficient self-attention architectures to make the computation linear in the number of supports. Secondly, Max-SW and its amortized version cannot guarantee metricity property due to the sub-optimality of the projected gradient ascent and the amortization gap. Therefore, we propose to replace Max-SW with distributional sliced Wasserstein distance with von Mises-Fisher (vMF) projecting distribution (v-DSW). Since v-DSW is a metric with any non-degenerate vMF distribution, its amortized version can guarantee the metricity when predicting the best discriminate projecting distribution. With the two improvements, we derive self-attention amortized distributional projection optimization and show its appealing performance in point-cloud reconstruction and its downstream applications.  ( 2 min )
    Multimodal Deep Learning. (arXiv:2301.04856v1 [cs.CL])
    This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in which one modality is utilized to enhance representation learning for the other. To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced. Finally, we also cover other modalities as well as general-purpose multi-modal models, which are able to handle different tasks on different modalities within one unified architecture. One interesting application (Generative Art) eventually caps off this booklet.  ( 2 min )
  • Open

    Robust Phi-Divergence MDPs. (arXiv:2205.14202v2 [math.OC] UPDATED)
    In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty. In contrast to classical MDPs, which only account for stochasticity by modeling the dynamics through a stochastic process with a known transition kernel, robust MDPs additionally account for ambiguity by optimizing in view of the most adverse transition kernel from a prescribed ambiguity set. In this paper, we develop a novel solution framework for robust MDPs with s-rectangular ambiguity sets that decomposes the problem into a sequence of robust Bellman updates and simplex projections. Exploiting the rich structure present in the simplex projections corresponding to phi-divergence ambiguity sets, we show that the associated s-rectangular robust MDPs can be solved substantially faster than with state-of-the-art commercial solvers as well as a recent first-order solution scheme, thus rendering them attractive alternatives to classical MDPs in practical applications.  ( 2 min )
    See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning. (arXiv:2301.05226v1 [cs.CV])
    Large pre-trained vision and language models have demonstrated remarkable capacities for various tasks. However, solving the knowledge-based visual reasoning tasks remains challenging, which requires a model to comprehensively understand image content, connect the external world knowledge, and perform step-by-step reasoning to answer the questions correctly. To this end, we propose a novel framework named Interactive Prompting Visual Reasoner (IPVR) for few-shot knowledge-based visual reasoning. IPVR contains three stages, see, think and confirm. The see stage scans the image and grounds the visual concept candidates with a visual perception model. The think stage adopts a pre-trained large language model (LLM) to attend to the key concepts from candidates adaptively. It then transforms them into text context for prompting with a visual captioning model and adopts the LLM to generate the answer. The confirm stage further uses the LLM to generate the supporting rationale to the answer, verify the generated rationale with a cross-modality classifier and ensure that the rationale can infer the predicted output consistently. We conduct experiments on a range of knowledge-based visual reasoning datasets. We found our IPVR enjoys several benefits, 1). it achieves better performance than the previous few-shot learning baselines; 2). it enjoys the total transparency and trustworthiness of the whole reasoning process by providing rationales for each reasoning step; 3). it is computation-efficient compared with other fine-tuning baselines.  ( 2 min )
    Masked Autoencoders that Listen. (arXiv:2207.06405v3 [cs.SD] UPDATED)
    This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms. Following the Transformer encoder-decoder design in MAE, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window attention in the decoder, as audio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower masking ratio on target datasets. Empirically, Audio-MAE sets new state-of-the-art performance on six audio and speech classification tasks, outperforming other recent models that use external supervised pre-training. The code and models will be at https://github.com/facebookresearch/AudioMAE.  ( 2 min )
    Model reduction for the material point method via an implicit neural representation of the deformation map. (arXiv:2109.12390v3 [cs.LG] UPDATED)
    This work proposes a model-reduction approach for the material point method on nonlinear manifolds. Our technique approximates the $\textit{kinematics}$ by approximating the deformation map using an implicit neural representation that restricts deformation trajectories to reside on a low-dimensional manifold. By explicitly approximating the deformation map, its spatiotemporal gradients -- in particular the deformation gradient and the velocity -- can be computed via analytical differentiation. In contrast to typical model-reduction techniques that construct a linear or nonlinear manifold to approximate the (finite number of) degrees of freedom characterizing a given spatial discretization, the use of an implicit neural representation enables the proposed method to approximate the $\textit{continuous}$ deformation map. This allows the kinematic approximation to remain agnostic to the discretization. Consequently, the technique supports dynamic discretizations -- including resolution changes -- during the course of the online reduced-order-model simulation. To generate $\textit{dynamics}$ for the generalized coordinates, we propose a family of projection techniques. At each time step, these techniques: (1) Calculate full-space kinematics at quadrature points, (2) Calculate the full-space dynamics for a subset of `sample' material points, and (3) Calculate the reduced-space dynamics by projecting the updated full-space position and velocity onto the low-dimensional manifold and tangent space, respectively. We achieve significant computational speedup via hyper-reduction that ensures all three steps execute on only a small subset of the problem's spatial domain. Large-scale numerical examples with millions of material points illustrate the method's ability to gain an order of magnitude computational-cost saving -- indeed $\textit{real-time simulations}$ -- with negligible errors.  ( 3 min )
    Adversarial Adaptation for French Named Entity Recognition. (arXiv:2301.05220v1 [cs.CL])
    Named Entity Recognition (NER) is the task of identifying and classifying named entities in large-scale texts into predefined classes. NER in French and other relatively limited-resource languages cannot always benefit from approaches proposed for languages like English due to a dearth of large, robust datasets. In this paper, we present our work that aims to mitigate the effects of this dearth of large, labeled datasets. We propose a Transformer-based NER approach for French, using adversarial adaptation to similar domain or general corpora to improve feature extraction and enable better generalization. Our approach allows learning better features using large-scale unlabeled corpora from the same domain or mixed domains to introduce more variations during training and reduce overfitting. Experimental results on three labeled datasets show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of Transformer models, source datasets, and target corpora. We also show that adversarial adaptation to large-scale unlabeled corpora can help mitigate the performance dip incurred on using Transformer models pre-trained on smaller corpora.  ( 2 min )
    MANAS: Multi-Agent Neural Architecture Search. (arXiv:1909.01051v4 [cs.CV] UPDATED)
    The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective. Due to the large architecture parameter space, efficiency is a key bottleneck preventing NAS from its practical use. In this paper, we address the issue by framing NAS as a multi-agent problem where agents control a subset of the network and coordinate to reach optimal architectures. We provide two distinct lightweight implementations, with reduced memory requirements (1/8th of state-of-the-art), and performances above those of much more computationally expensive methods. Theoretically, we demonstrate vanishing regrets of the form O(sqrt(T)), with T being the total number of rounds. Finally, aware that random search is an, often ignored, effective baseline we perform additional experiments on 3 alternative datasets and 2 network configurations, and achieve favourable results in comparison.  ( 2 min )
    Anomalies, Representations, and Self-Supervision. (arXiv:2301.04660v1 [hep-ph])
    We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way. It uses a permutation-invariant Transformer Encoder architecture to map the objects measured in a collider event to the representation space, where the data augmentations define a representation space which is sensitive to potential anomalous features. An AutoEncoder trained on background representations then computes anomaly scores for a variety of signals in the representation space. With AnomalyCLR we find significant improvements on performance metrics for all signals when compared to the raw data baseline.  ( 2 min )
    Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models. (arXiv:2208.11907v2 [cs.LG] UPDATED)
    In this paper, we consider the task of clustering a set of individual time series while modeling each cluster, that is, model-based time series clustering. The task requires a parametric model with sufficient flexibility to describe the dynamics in various time series. To address this problem, we propose a novel model-based time series clustering method with mixtures of linear Gaussian state space models, which have high flexibility. The proposed method uses a new expectation-maximization algorithm for the mixture model to estimate the model parameters, and determines the number of clusters using the Bayesian information criterion. Experiments on a simulated dataset demonstrate the effectiveness of the method in clustering, parameter estimation, and model selection. The method is applied to a real dataset for which previously proposed time series clustering methods exhibited low accuracy. Results showed that our method produces more accurate clustering results than those obtained using the previous methods.  ( 2 min )
    RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?. (arXiv:2108.04384v3 [cs.CV] UPDATED)
    For the past ten years, CNN has reigned supreme in the world of computer vision, but recently, Transformer has been on the rise. However, the quadratic computational cost of self-attention has become a serious problem in practice applications. There has been much research on architectures without CNN and self-attention in this context. In particular, MLP-Mixer is a simple architecture designed using MLPs and hit an accuracy comparable to the Vision Transformer. However, the only inductive bias in this architecture is the embedding of tokens. This leaves open the possibility of incorporating a non-convolutional (or non-local) inductive bias into the architecture, so we used two simple ideas to incorporate inductive bias into the MLP-Mixer while taking advantage of its ability to capture global correlations. A way is to divide the token-mixing block vertically and horizontally. Another way is to make spatial correlations denser among some channels of token-mixing. With this approach, we were able to improve the accuracy of the MLP-Mixer while reducing its parameters and computational complexity. The small model that is RaftMLP-S is comparable to the state-of-the-art global MLP-based model in terms of parameters and efficiency per calculation. In addition, we tackled the problem of fixed input image resolution for global MLP-based models by utilizing bicubic interpolation. We demonstrated that these models could be applied as the backbone of architectures for downstream tasks such as object detection. However, it did not have significant performance and mentioned the need for MLP-specific architectures for downstream tasks for global MLP-based models. The source code in PyTorch version is available at \url{https://github.com/okojoalg/raft-mlp}.  ( 3 min )
    Modeling the evolution of temporal knowledge graphs with uncertainty. (arXiv:2301.04977v1 [cs.LG])
    Forecasting future events is a fundamental challenge for temporal knowledge graphs (tKG). As in real life predicting a mean function is most of the time not sufficient, but the question remains how confident can we be about our prediction? Thus, in this work, we will introduce a novel graph neural network architecture (WGP-NN) employing (weighted) Gaussian processes (GP) to jointly model the temporal evolution of the occurrence probability of events and their time-dependent uncertainty. Especially we employ Gaussian processes to model the uncertainty of future links by their ability to predict predictive variance. This is in contrast to existing works, which are only able to express uncertainties in the learned entity representations. Moreover, WGP-NN can model parameter-free complex temporal and structural dynamics of tKGs in continuous time. We further demonstrate the model's state-of-the-art performance on two real-world benchmark datasets.  ( 2 min )
    Neural Systematic Binder. (arXiv:2211.01177v2 [cs.CV] UPDATED)
    The key to high-level cognition is believed to be the ability to systematically manipulate and compose knowledge pieces. While token-like structured knowledge representations are naturally provided in text, it is elusive how to obtain them for unstructured modalities such as scene images. In this paper, we propose a neural mechanism called Neural Systematic Binder or SysBinder for constructing a novel structured representation called Block-Slot Representation. In Block-Slot Representation, object-centric representations known as slots are constructed by composing a set of independent factor representations called blocks, to facilitate systematic generalization. SysBinder obtains this structure in an unsupervised way by alternatingly applying two different binding principles: spatial binding for spatial modularity across the full scene and factor binding for factor modularity within an object. SysBinder is a simple, deterministic, and general-purpose layer that can be applied as a drop-in module in any arbitrary neural network and on any modality. In experiments, we find that SysBinder provides significantly better factor disentanglement within the slots than the conventional object-centric methods, including, for the first time, in visually complex scene images such as CLEVR-Tex. Furthermore, we demonstrate factor-level systematicity in controlled scene generation by decoding unseen factor combinations.  ( 2 min )
    Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts. (arXiv:2210.03885v2 [cs.LG] UPDATED)
    In this paper, we tackle the problem of domain shift. Most existing methods perform training on multiple source domains using a single model, and the same trained model is used on all unseen target domains. Such solutions are sub-optimal as each target domain exhibits its own specialty, which is not adapted. Furthermore, expecting single-model training to learn extensive knowledge from multiple source domains is counterintuitive. The model is more biased toward learning only domain-invariant features and may result in negative knowledge transfer. In this work, we propose a novel framework for unsupervised test-time adaptation, which is formulated as a knowledge distillation process to address domain shift. Specifically, we incorporate Mixture-of-Experts (MoE) as teachers, where each expert is separately trained on different source domains to maximize their specialty. Given a test-time target domain, a small set of unlabeled data is sampled to query the knowledge from MoE. As the source domains are correlated to the target domains, a transformer-based aggregator then combines the domain knowledge by examining the interconnection among them. The output is treated as a supervision signal to adapt a student prediction network toward the target domain. We further employ meta-learning to enforce the aggregator to distill positive knowledge and the student network to achieve fast adaptation. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art and validates the effectiveness of each proposed component. Our code is available at https://github.com/n3il666/Meta-DMoE.  ( 2 min )
    Sequencer: Deep LSTM for Image Classification. (arXiv:2205.01972v4 [cs.CV] UPDATED)
    In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using simple multi-layer perceptrons. In contrast, several studies have also suggested that carefully redesigned convolutional neural networks (CNNs) can achieve advanced performance comparable to ViT without resorting to these new ideas. Against this background, there is growing interest in what inductive bias is suitable for computer vision. Here we propose Sequencer, a novel and competitive architecture alternative to ViT that provides a new perspective on these issues. Unlike ViTs, Sequencer models long-range dependencies using LSTMs rather than self-attention layers. We also propose a two-dimensional version of Sequencer module, where an LSTM is decomposed into vertical and horizontal LSTMs to enhance performance. Despite its simplicity, several experiments demonstrate that Sequencer performs impressively well: Sequencer2D-L, with 54M parameters, realizes 84.6% top-1 accuracy on only ImageNet-1K. Not only that, we show that it has good transferability and the robust resolution adaptability on double resolution-band.  ( 2 min )
    A Network Science perspective of Graph Convolutional Networks: A survey. (arXiv:2301.04824v1 [cs.SI])
    The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research.  ( 2 min )
    Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data. (arXiv:2211.13116v2 [cs.LG] UPDATED)
    Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL. Existing data augmentation methods based on federated generative models or raw data sharing strategies for solving the non-IID problem still suffer from low performance, privacy protection concerns, and high communication overhead in decentralized tabular data. To tackle these challenges, we propose a federated tabular data augmentation method, named Fed-TDA. The core idea of Fed-TDA is to synthesize tabular data for data augmentation using some simple statistics (e.g., distributions of each column and global covariance). Specifically, we propose the multimodal distribution transformation and inverse cumulative distribution mapping respectively synthesize continuous and discrete columns in tabular data from a noise according to the pre-learned statistics. Furthermore, we theoretically analyze that our Fed-TDA not only preserves data privacy but also maintains the distribution of the original data and the correlation between columns. Through extensive experiments on five real-world tabular datasets, we demonstrate the superiority of Fed-TDA over the state-of-the-art in test performance and communication efficiency.  ( 2 min )
    Data-centric AI: Perspectives and Challenges. (arXiv:2301.04819v1 [cs.AI])
    The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability. Although our community has continuously invested efforts into enhancing data in different aspects, they are often isolated initiatives on specific tasks. To facilitate the collective initiative in our community and push forward DCAI, we draw a big picture and bring together three general missions: training data development, evaluation data development, and data maintenance. We provide a top-level discussion on representative DCAI tasks and share perspectives. Finally, we list open challenges to motivate future exploration.  ( 2 min )
    Optirank: classification for RNA-Seq data with optimal ranking reference genes. (arXiv:2301.04653v1 [q-bio.GN])
    Classification algorithms using RNA-Sequencing (RNA-Seq) data as input are used in a variety of biological applications. By nature, RNA-Seq data is subject to uncontrolled fluctuations both within and especially across datasets, which presents a major difficulty for a trained classifier to generalize to an external dataset. Replacing raw gene counts with the rank of gene counts inside an observation has proven effective to mitigate this problem. However, the rank of a feature is by definition relative to all other features, including highly variable features that introduce noise in the ranking. To address this problem and obtain more robust ranks, we propose a logistic regression model, optirank, which learns simultaneously the parameters of the model and the genes to use as a reference set in the ranking. We show the effectiveness of this method on simulated data. We also consider real classification tasks, which present different kinds of distribution shifts between train and test data. Those tasks concern a variety of applications, such as cancer of unknown primary classification, identification of specific gene signatures, and determination of cell type in single-cell RNA-Seq datasets. On those real tasks, optirank performs at least as well as the vanilla logistic regression on classical ranks, while producing sparser solutions. In addition, to increase the robustness against dataset shifts, we propose a multi-source learning scheme and demonstrate its effectiveness when used in combination with rank-based classifiers.  ( 2 min )
    Effective Decision Boundary Learning for Class Incremental Learning. (arXiv:2301.05180v1 [cs.LG])
    Rehearsal approaches in class incremental learning (CIL) suffer from decision boundary overfitting to new classes, which is mainly caused by two factors: insufficiency of old classes data for knowledge distillation and imbalanced data learning between the learned and new classes because of the limited storage memory. In this work, we present a simple but effective approach to tackle these two factors. First, we employ a re-sampling strategy and Mixup K}nowledge D}istillation (Re-MKD) to improve the performances of KD, which would greatly alleviate the overfitting problem. Specifically, we combine mixup and re-sampling strategies to synthesize adequate data used in KD training that are more consistent with the latent distribution between the learned and new classes. Second, we propose a novel incremental influence balance (IIB) method for CIL to tackle the classification of imbalanced data by extending the influence balance method into the CIL setting, which re-weights samples by their influences to create a proper decision boundary. With these two improvements, we present the effective decision boundary learning algorithm (EDBL) which improves the performance of KD and deals with the imbalanced data learning simultaneously. Experiments show that the proposed EDBL achieves state-of-the-art performances on several CIL benchmarks.
    Second-Order Mirror Descent: Convergence in Games Beyond Averaging and Discounting. (arXiv:2111.09982v3 [math.OC] UPDATED)
    In this paper, we propose a second-order extension of the continuous-time game-theoretic mirror descent (MD) dynamics, referred to as MD2, which provably converges to mere (but not necessarily strict) variationally stable states (VSS) without using common auxiliary techniques such as time-averaging or discounting. We show that MD2 enjoys no-regret as well as an exponential rate of convergence towards strong VSS upon a slight modification. MD2 can also be used to derive many novel continuous-time primal-space dynamics. We then use stochastic approximation techniques to provide a convergence guarantee of discrete-time MD2 with noisy observations towards interior mere VSS. Selected simulations are provided to illustrate our results.
    Improving Axial-Attention Network Classification via Cross-Channel Weight Sharing. (arXiv:2110.01185v2 [cs.CV] UPDATED)
    In recent years, hypercomplex-inspired neural networks (HCNNs) have been used to improve deep learning architectures due to their ability to enable channel-based weight sharing, treat colors as a single entity, and improve representational coherence within the layers. The work described herein studies the effect of replacing existing layers in an Axial Attention network with their representationally coherent variants to assess the effect on image classification. We experiment with the stem of the network, the bottleneck layers, and the fully connected backend, by replacing them with representationally coherent variants. These various modifications lead to novel architectures which all yield improved accuracy performance on the ImageNet300k classification dataset. Our baseline networks for comparison were the original real-valued ResNet, the original quaternion-valued ResNet, and the Axial Attention ResNet. Since improvement was observed regardless of which part of the network was modified, there is a promise that this technique may be generally useful in improving classification accuracy for a large class of networks.
    Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks. (arXiv:2108.12943v3 [cs.LG] CROSS LISTED)
    Convolutional neural networks have been successful in solving many socially important and economically significant problems. This ability to learn complex high-dimensional functions hierarchically can be attributed to the use of nonlinear activation functions. A key discovery that made training deep networks feasible was the adoption of the Rectified Linear Unit (ReLU) activation function to alleviate the vanishing gradient problem caused by using saturating activation functions. Since then, many improved variants of the ReLU activation have been proposed. However, a majority of activation functions used today are non-oscillatory and monotonically increasing due to their biological plausibility. This paper demonstrates that oscillatory activation functions can improve gradient flow and reduce network size. Two theorems on limits of non-oscillatory activation functions are presented. A new oscillatory activation function called Growing Cosine Unit(GCU) defined as $C(z) = z\cos z$ that outperforms Sigmoids, Swish, Mish and ReLU on a variety of architectures and benchmarks is presented. The GCU activation has multiple zeros enabling single GCU neurons to have multiple hyperplanes in the decision boundary. This allows single GCU neurons to learn the XOR function without feature engineering. Experimental results indicate that replacing the activation function in the convolution layers with the GCU activation function significantly improves performance on CIFAR-10, CIFAR-100 and Imagenette.
    Bayesian inference via sparse Hamiltonian flows. (arXiv:2203.05723v2 [stat.ML] UPDATED)
    A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during Bayesian inference, with the goal of reducing computational cost. Although past work has shown empirically that there often exists a coreset with low inferential error, efficiently constructing such a coreset remains a challenge. Current methods tend to be slow, require a secondary inference step after coreset construction, and do not provide bounds on the data marginal evidence. In this work, we introduce a new method -- sparse Hamiltonian flows -- that addresses all three of these challenges. The method involves first subsampling the data uniformly, and then optimizing a Hamiltonian flow parametrized by coreset weights and including periodic momentum quasi-refreshment steps. Theoretical results show that the method enables an exponential compression of the dataset in a representative model, and that the quasi-refreshment steps reduce the KL divergence to the target. Real and synthetic experiments demonstrate that sparse Hamiltonian flows provide accurate posterior approximations with significantly reduced runtime compared with competing dynamical-system-based inference methods.
    LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification. (arXiv:2301.04838v1 [cs.LG])
    Time series classification is an important data mining task that has received a lot of interest in the past two decades. Due to the label scarcity in practice, semi-supervised time series classification with only a few labeled samples has become popular. Recently, Similarity-aware Time Series Classification (SimTSC) is proposed to address this problem by using a graph neural network classification model on the graph generated from pairwise Dynamic Time Warping (DTW) distance of batch data. It shows excellent accuracy and outperforms state-of-the-art deep learning models in several few-label settings. However, since SimTSC relies on pairwise DTW distances, the quadratic complexity of DTW limits its usability to only reasonably sized datasets. To address this challenge, we propose a new efficient semi-supervised time series classification technique, LB-SimTSC, with a new graph construction module. Instead of using DTW, we propose to utilize a lower bound of DTW, LB_Keogh, to approximate the dissimilarity between instances in linear time, while retaining the relative proximity relationships one would have obtained via computing DTW. We construct the pairwise distance matrix using LB_Keogh and build a graph for the graph neural network. We apply this approach to the ten largest datasets from the well-known UCR time series classification archive. The results demonstrate that this approach can be up to 104x faster than SimTSC when constructing the graph on large datasets without significantly decreasing classification accuracy.
    Learning to compile smartly for program size reduction. (arXiv:2301.05104v1 [cs.PL])
    Compiler optimization passes are an important tool for improving program efficiency and reducing program size, but manually selecting optimization passes can be time-consuming and error-prone. While human experts have identified a few fixed sequences of optimization passes (e.g., the Clang -Oz passes) that perform well for a wide variety of programs, these sequences are not conditioned on specific programs. In this paper, we propose a novel approach that learns a policy to select passes for program size reduction, allowing for customization and adaptation to specific programs. Our approach uses a search mechanism that helps identify useful pass sequences and a GNN with customized attention that selects the optimal sequence to use. Crucially it is able to generalize to new, unseen programs, making it more flexible and general than previous approaches. We evaluate our approach on a range of programs and show that it leads to size reduction compared to traditional optimization techniques. Our results demonstrate the potential of a single policy that is able to optimize many programs.
    Understanding Difficulty-based Sample Weighting with a Universal Difficulty Measure. (arXiv:2301.04850v1 [cs.LG])
    Sample weighting is widely used in deep learning. A large number of weighting methods essentially utilize the learning difficulty of training samples to calculate their weights. In this study, this scheme is called difficulty-based weighting. Two important issues arise when explaining this scheme. First, a unified difficulty measure that can be theoretically guaranteed for training samples does not exist. The learning difficulties of the samples are determined by multiple factors including noise level, imbalance degree, margin, and uncertainty. Nevertheless, existing measures only consider a single factor or in part, but not in their entirety. Second, a comprehensive theoretical explanation is lacking with respect to demonstrating why difficulty-based weighting schemes are effective in deep learning. In this study, we theoretically prove that the generalization error of a sample can be used as a universal difficulty measure. Furthermore, we provide formal theoretical justifications on the role of difficulty-based weighting for deep learning, consequently revealing its positive influences on both the optimization dynamics and generalization performance of deep models, which is instructive to existing weighting schemes.
    NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants. (arXiv:2301.05223v1 [cs.RO])
    In this work, we study how to build socially intelligent robots to assist people in their homes. In particular, we focus on assistance with online goal inference, where robots must simultaneously infer humans' goals and how to help them achieve those goals. Prior assistance methods either lack the adaptivity to adjust helping strategies (i.e., when and how to help) in response to uncertainty about goals or the scalability to conduct fast inference in a large goal space. Our NOPA (Neurally-guided Online Probabilistic Assistance) method addresses both of these challenges. NOPA consists of (1) an online goal inference module combining neural goal proposals with inverse planning and particle filtering for robust inference under uncertainty, and (2) a helping planner that discovers valuable subgoals to help with and is aware of the uncertainty in goal inference. We compare NOPA against multiple baselines in a new embodied AI assistance challenge: Online Watch-And-Help, in which a helper agent needs to simultaneously watch a main agent's action, infer its goal, and help perform a common household task faster in realistic virtual home environments. Experiments show that our helper agent robustly updates its goal inference and adapts its helping plans to the changing level of uncertainty.
    Exploration in Deep Reinforcement Learning: A Comprehensive Survey. (arXiv:2109.06668v5 [cs.AI] UPDATED)
    Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved significant successes across a wide range of domains, including game AI, autonomous vehicles, robotics, and so on. However, DRL and deep MARL agents are widely known to be sample inefficient that millions of interactions are usually needed even for relatively simple problem settings, thus preventing the wide application and deployment in real-industry scenarios. One bottleneck challenge behind is the well-known exploration problem, i.e., how efficiently exploring the environment and collecting informative experiences that could benefit policy learning towards the optimal ones. This problem becomes more challenging in complex environments with sparse rewards, noisy distractions, long horizons, and non-stationary co-learners. In this paper, we conduct a comprehensive survey on existing exploration methods for both single-agent and multi-agent RL. We start the survey by identifying several key challenges to efficient exploration. Beyond the above two main branches, we also include other notable exploration methods with different ideas and techniques. In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks. According to our algorithmic and empirical investigation, we finally summarize the open problems of exploration in DRL and deep MARL and point out a few future directions.
    Signed Directed Graph Contrastive Learning with Laplacian Augmentation. (arXiv:2301.05163v1 [cs.LG])
    Graph contrastive learning has become a powerful technique for several graph mining tasks. It learns discriminative representation from different perspectives of augmented graphs. Ubiquitous in our daily life, singed-directed graphs are the most complex and tricky to analyze among various graph types. That is why singed-directed graph contrastive learning has not been studied much yet, while there are many contrastive studies for unsigned and undirected. Thus, this paper proposes a novel signed-directed graph contrastive learning, SDGCL. It makes two different structurally perturbed graph views and gets node representations via magnetic Laplacian perturbation. We use a node-level contrastive loss to maximize the mutual information between the two graph views. The model is jointly learned with contrastive and supervised objectives. The graph encoder of SDGCL does not depend on social theories or predefined assumptions. Therefore it does not require finding triads or selecting neighbors to aggregate. It leverages only the edge signs and directions via magnetic Laplacian. To the best of our knowledge, it is the first to introduce magnetic Laplacian perturbation and signed spectral graph contrastive learning. The superiority of the proposed model is demonstrated through exhaustive experiments on four real-world datasets. SDGCL shows better performance than other state-of-the-art on four evaluation metrics.
    PiFold: Toward effective and efficient protein inverse folding. (arXiv:2209.12643v3 [cs.AI] UPDATED)
    How can we design protein sequences folding into the desired structures effectively and efficiently? Structure-based protein design has attracted increasing attention in recent years; however, few methods can simultaneously improve the accuracy and efficiency due to the lack of expressive features and autoregressive sequence decoder. To address these issues, we propose PiFold, which contains a novel residue featurizer and PiGNN layers to generate protein sequences in a one-shot way with improved recovery. Experiments show that PiFold could achieve 51.66\% recovery on CATH 4.2, while the inference speed is 70 times faster than the autoregressive competitors. In addition, PiFold achieves 58.72\% and 60.42\% recovery scores on TS50 and TS500, respectively. We conduct comprehensive ablation studies to reveal the role of different types of protein features and model designs, inspiring further simplification and improvement.
    Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study. (arXiv:2301.05174v1 [cs.IR])
    Most approaches to cross-modal retrieval (CMR) focus either on object-centric datasets, meaning that each document depicts or describes a single object, or on scene-centric datasets, meaning that each image depicts or describes a complex scene that involves multiple objects and relations between them. We posit that a robust CMR model should generalize well across both dataset types. Despite recent advances in CMR, the reproducibility of the results and their generalizability across different dataset types has not been studied before. We address this gap and focus on the reproducibility of the state-of-the-art CMR results when evaluated on object-centric and scene-centric datasets. We select two state-of-the-art CMR models with different architectures: (i) CLIP; and (ii) X-VLM. Additionally, we select two scene-centric datasets, and three object-centric datasets, and determine the relative performance of the selected models on these datasets. We focus on reproducibility, replicability, and generalizability of the outcomes of previously published CMR experiments. We discover that the experiments are not fully reproducible and replicable. Besides, the relative performance results partially generalize across object-centric and scene-centric datasets. On top of that, the scores obtained on object-centric datasets are much lower than the scores obtained on scene-centric datasets. For reproducibility and transparency we make our source code and the trained models publicly available.
    Statistical Learning with Sublinear Regret of Propagator Models. (arXiv:2301.05157v1 [q-fin.TR])
    We consider a class of learning problems in which an agent liquidates a risky asset while creating both transient price impact driven by an unknown convolution propagator and linear temporary price impact with an unknown parameter. We characterize the trader's performance as maximization of a revenue-risk functional, where the trader also exploits available information on a price predicting signal. We present a trading algorithm that alternates between exploration and exploitation phases and achieves sublinear regrets with high probability. For the exploration phase we propose a novel approach for non-parametric estimation of the price impact kernel by observing only the visible price process and derive sharp bounds on the convergence rate, which are characterised by the singularity of the propagator. These kernel estimation methods extend existing methods from the area of Tikhonov regularisation for inverse problems and are of independent interest. The bound on the regret in the exploitation phase is obtained by deriving stability results for the optimizer and value function of the associated class of infinite-dimensional stochastic control problems. As a complementary result we propose a regression-based algorithm to estimate the conditional expectation of non-Markovian signals and derive its convergence rate.
    Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning. (arXiv:2301.05219v1 [cs.CV])
    The state of neural network pruning has been noticed to be unclear and even confusing for a while, largely due to "a lack of standardized benchmarks and metrics" [3]. To standardize benchmarks, first, we need to answer: what kind of comparison setup is considered fair? This basic yet crucial question has barely been clarified in the community, unfortunately. Meanwhile, we observe several papers have used (severely) sub-optimal hyper-parameters in pruning experiments, while the reason behind them is also elusive. These sub-optimal hyper-parameters further exacerbate the distorted benchmarks, rendering the state of neural network pruning even more obscure. Two mysteries in pruning represent such a confusing status: the performance-boosting effect of a larger finetuning learning rate, and the no-value argument of inheriting pretrained weights in filter pruning. In this work, we attempt to explain the confusing state of network pruning by demystifying the two mysteries. Specifically, (1) we first clarify the fairness principle in pruning experiments and summarize the widely-used comparison setups; (2) then we unveil the two pruning mysteries and point out the central role of network trainability, which has not been well recognized so far; (3) finally, we conclude the paper and give some concrete suggestions regarding how to calibrate the pruning benchmarks in the future. Code: https://github.com/mingsun-tse/why-the-state-of-pruning-so-confusing.
    A Cognitive Evaluation of Instruction Generation Agents tl;dr They Need Better Theory-of-Mind Capabilities. (arXiv:2301.05149v1 [cs.CL])
    We mathematically characterize the cognitive capabilities that enable humans to effectively guide others through natural language. We show that neural-network-based instruction generation agents possess similar cognitive capabilities, and design an evaluation scheme for probing those capabilities. Our results indicate that these agents, while capable of effectively narrowing the search space, poorly predict the listener's interpretations of their instructions and thus often fail to select the best instructions even from a small candidate set. We augment the agents with better theory-of-mind models of the listener and obtain significant performance boost in guiding real humans. Yet, there remains a considerable gap between our best agent and human guides. We discuss the challenges in closing this gap, emphasizing the need to construct better models of human behavior when interacting with AI-based agents.
    Fairly Private: Investigating The Fairness of Visual Privacy Preservation Algorithms. (arXiv:2301.05012v1 [cs.CV])
    As the privacy risks posed by camera surveillance and facial recognition have grown, so has the research into privacy preservation algorithms. Among these, visual privacy preservation algorithms attempt to impart bodily privacy to subjects in visuals by obfuscating privacy-sensitive areas. While disparate performances of facial recognition systems across phenotypes are the subject of much study, its counterpart, privacy preservation, is not commonly analysed from a fairness perspective. In this paper, the fairness of commonly used visual privacy preservation algorithms is investigated through the performances of facial recognition models on obfuscated images. Experiments on the PubFig dataset clearly show that the privacy protection provided is unequal across groups.
    Tracr: Compiled Transformers as a Laboratory for Interpretability. (arXiv:2301.05062v1 [cs.LG])
    Interpretability research aims to build tools for understanding machine learning (ML) models. However, such tools are inherently hard to evaluate because we do not have ground truth information about how ML models actually work. In this work, we propose to build transformer models manually as a testbed for interpretability research. We introduce Tracr, a "compiler" for translating human-readable programs into weights of a transformer model. Tracr takes code written in RASP, a domain-specific language (Weiss et al. 2021), and translates it into weights for a standard, decoder-only, GPT-like transformer architecture. We use Tracr to create a range of ground truth transformers that implement programs including computing token frequencies, sorting, and Dyck-n parenthesis checking, among others. To enable the broader research community to explore and use compiled models, we provide an open-source implementation of Tracr at https://github.com/deepmind/tracr.
    Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN. (arXiv:2208.04229v2 [cs.NI] UPDATED)
    Open Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces. A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller. Traditionally, all data available in the network has been used to train a single AI model to be used at the RIC. This paper introduces, discusses, and evaluates the creation of multiple AI model instances at different RICs, leveraging information from some (or all) locations for their training. This brings about a flexible relationship between gNBs, the AI models used to control them, and the data such models are trained with. Experiments with real-world traces show how using multiple AI model instances that choose training data from specific locations improve the performance of traditional approaches following the hoarding strategy.
    Battery Degradation Long-term Forecast Using Gaussian Process Dynamical Models and Knowledge Transfer. (arXiv:2212.01609v2 [cs.LG] UPDATED)
    Batteries plays an essential role in modern energy ecosystem and are widely used in daily applications such as cell phones and electric vehicles. For many applications, the health status of batteries plays a critical role in the performance of the system by indicating efficient maintenance and on-time replacement. Directly modeling an individual battery using a computational models based on physical rules can be of low-efficiency, in terms of the difficulties in build such a model and the computational effort of tuning and running it especially on the edge. With the rapid development of sensor technology (to provide more insights into the system) and machine learning (to build capable yet fast model), it is now possible to directly build a data-riven model of the battery health status using the data collected from historical battery data (being possibly local and remote) to predict local battery health status in the future accurately. Nevertheless, most data-driven methods are trained based on the local battery data and lack the ability to extract common properties, such as generations and degradation, in the life span of other remote batteries. In this paper, we utilize a Gaussian process dynamical model (GPDM) to build a data-driven model of battery health status and propose a knowledge transfer method to extract common properties in the life span of all batteries to accurately predict the battery health status with and without features extracted from the local battery. For modern benchmark problems, the proposed method outperform the state-of-the-art methods with significant margins in terms of accuracy and is able to accuracy predict the regeneration process.
    Interaction models for remaining useful life estimation. (arXiv:2301.05029v1 [cs.LG])
    The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors. The current methods rely on one approach to feature extraction in which the prediction occurs. We proposed a technique to build a scalable model that combines multiple different feature extractor blocks. A new model based on sequential sensor space analysis achieves state-of-the-art results on the C-MAPSS benchmark for equipment remaining useful life estimation. The resulting model performance was validated including the prediction changes with scaling.
    Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks. (arXiv:2209.08378v3 [cs.LG] UPDATED)
    We propose a simple modification to standard ResNet architectures--L2 normalization over feature space--that substantially improves out-of-distribution (OoD) performance on the previously proposed Deep Deterministic Uncertainty (DDU) benchmark. We show that this change also induces early Neural Collapse (NC), an effect linked to better OoD performance. Our method achieves comparable or superior OoD detection scores and classification accuracy in a small fraction of the training time of the benchmark. Additionally, it substantially improves worst case OoD performance over multiple, randomly initialized models. Though we do not suggest that NC is the sole mechanism or a comprehensive explanation for OoD behaviour in deep neural networks (DNN), we believe NC's simple mathematical and geometric structure can provide a framework for analysis of this complex phenomenon in future work.
    Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment. (arXiv:2301.05102v1 [cs.LG])
    Resource-intensive computations are a major factor that limits the effectiveness of automated machine learning solutions. In the paper, we propose a modular approach that can be used to increase the quality of evolutionary optimization for modelling pipelines with a graph-based structure. It consists of several stages - parallelization, caching and evaluation. Heterogeneous and remote resources can be involved in the evaluation stage. The conducted experiments confirm the correctness and effectiveness of the proposed approach. The implemented algorithms are available as a part of the open-source framework FEDOT.
    Progress measures for grokking via mechanistic interpretability. (arXiv:2301.05217v1 [cs.LG])
    Neural networks often exhibit emergent behavior, where qualitatively new capabilities arise from scaling up the amount of parameters, training data, or training steps. One approach to understanding emergence is to find continuous \textit{progress measures} that underlie the seemingly discontinuous qualitative changes. We argue that progress measures can be found via mechanistic interpretability: reverse-engineering learned behaviors into their individual components. As a case study, we investigate the recently-discovered phenomenon of ``grokking'' exhibited by small transformers trained on modular addition tasks. We fully reverse engineer the algorithm learned by these networks, which uses discrete Fourier transforms and trigonometric identities to convert addition to rotation about a circle. We confirm the algorithm by analyzing the activations and weights and by performing ablations in Fourier space. Based on this understanding, we define progress measures that allow us to study the dynamics of training and split training into three continuous phases: memorization, circuit formation, and cleanup. Our results show that grokking, rather than being a sudden shift, arises from the gradual amplification of structured mechanisms encoded in the weights, followed by the later removal of memorizing components.
    Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs. (arXiv:2205.12550v3 [eess.SY] UPDATED)
    Identifying dynamical systems from experimental data is a notably difficult task. Prior knowledge generally helps, but the extent of this knowledge varies with the application, and customized models are often needed. Neural ordinary differential equations can be written as a flexible framework for system identification and can incorporate a broad spectrum of physical insight, giving physical interpretability to the resulting latent space. In the case of partial observations, however, the data points cannot directly be mapped to the latent state of the ODE. Hence, we propose to design recognition models, in particular inspired by nonlinear observer theory, to link the partial observations to the latent state. We demonstrate the performance of the proposed approach on numerical simulations and on an experimental dataset from a robotic exoskeleton.
    DROPO: Sim-to-Real Transfer with Offline Domain Randomization. (arXiv:2201.08434v2 [cs.RO] UPDATED)
    In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult. In this paper, we introduce DROPO, a novel method for estimating domain randomization distributions for safe sim-to-real transfer. Unlike prior work, DROPO only requires a limited, precollected offline dataset of trajectories, and explicitly models parameter uncertainty to match real data using a likelihood-based approach. We demonstrate that DROPO is capable of recovering dynamic parameter distributions in simulation and finding a distribution capable of compensating for an unmodeled phenomenon. We also evaluate the method in two zero-shot sim-to-real transfer scenarios, showing successful domain transfer and improved performance over prior methods.
    Estimate Deformation Capacity of Non-Ductile RC Shear Walls using Explainable Boosting Machine. (arXiv:2301.04652v1 [cs.LG])
    Machine learning is becoming increasingly prevalent for tackling challenges in earthquake engineering and providing fairly reliable and accurate predictions. However, it is mostly unclear how decisions are made because machine learning models are generally highly sophisticated, resulting in opaque black-box models. Machine learning models that are naturally interpretable and provide their own decision explanation, rather than using an explanatory, are more accurate in determining what the model actually computes. With this motivation, this study aims to develop a fully explainable machine learning model to predict the deformation capacity of non-ductile reinforced concrete shear walls based on experimental data collected worldwide. The proposed Explainable Boosting Machines (EBM)-based model is an interpretable, robust, naturally explainable glass-box model, yet provides high accuracy comparable to its black-box counterparts. The model enables the user to observe the relationship between the wall properties and the deformation capacity by quantifying the individual contribution of each wall property as well as the correlations among them. The mean coefficient of determination R2 and the mean ratio of predicted to actual value based on the test dataset are 0.92 and 1.05, respectively. The proposed predictive model stands out with its overall consistency with scientific knowledge, practicality, and interpretability without sacrificing high accuracy.
    Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data. (arXiv:2301.05106v1 [cs.LG])
    This paper considers deep out-of-distribution active learning. In practice, fully trained neural networks interact randomly with out-of-distribution (OOD) inputs and map aberrant samples randomly within the model representation space. Since data representations are direct manifestations of the training distribution, the data selection process plays a crucial role in outlier robustness. For paradigms such as active learning, this is especially challenging since protocols must not only improve performance on the training distribution most effectively but further render a robust representation space. However, existing strategies directly base the data selection on the data representation of the unlabeled data which is random for OOD samples by definition. For this purpose, we introduce forgetful active learning with switch events (FALSE) - a novel active learning protocol for out-of-distribution active learning. Instead of defining sample importance on the data representation directly, we formulate "informativeness" with learning difficulty during training. Specifically, we approximate how often the network "forgets" unlabeled samples and query the most "forgotten" samples for annotation. We report up to 4.5\% accuracy improvements in over 270 experiments, including four commonly used protocols, two OOD benchmarks, one in-distribution benchmark, and three different architectures.
    Masked Feature Prediction for Self-Supervised Visual Pre-Training. (arXiv:2112.09133v2 [cs.CV] UPDATED)
    We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions. We study five different types of features and find Histograms of Oriented Gradients (HOG), a hand-crafted feature descriptor, works particularly well in terms of both performance and efficiency. We observe that the local contrast normalization in HOG is essential for good results, which is in line with earlier work using HOG for visual recognition. Our approach can learn abundant visual knowledge and drive large-scale Transformer-based models. Without using extra model weights or supervision, MaskFeat pre-trained on unlabeled videos achieves unprecedented results of 86.7% with MViT-L on Kinetics-400, 88.3% on Kinetics-600, 80.4% on Kinetics-700, 39.8 mAP on AVA, and 75.0% on SSv2. MaskFeat further generalizes to image input, which can be interpreted as a video with a single frame and obtains competitive results on ImageNet.
    Fast spline detection in high density microscopy data. (arXiv:2301.04460v1 [cs.CV] CROSS LISTED)
    Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as crawling nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a novel end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping splines. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of crawling Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.
    Multi-Power Level $Q$-Learning Algorithm for Random Access in NOMA mMTC Systems. (arXiv:2301.05196v1 [cs.NI])
    The massive machine-type communications (mMTC) service will be part of new services planned to integrate the fifth generation of wireless communication (B5G). In mMTC, thousands of devices sporadically access available resource blocks on the network. In this scenario, the massive random access (RA) problem arises when two or more devices collide when selecting the same resource block. There are several techniques to deal with this problem. One of them deploys $Q$-learning (QL), in which devices store in their $Q$-table the rewards sent by the central node that indicate the quality of the transmission performed. The device learns the best resource blocks to select and transmit to avoid collisions. We propose a multi-power level QL (MPL-QL) algorithm that uses non-orthogonal multiple access (NOMA) transmit scheme to generate transmission power diversity and allow {accommodate} more than one device in the same time-slot as long as the signal-to-interference-plus-noise ratio (SINR) exceeds a threshold value. The numerical results reveal that the best performance-complexity trade-off is obtained by using a {higher {number of} power levels, typically eight levels}. The proposed MPL-QL {can deliver} better throughput and lower latency compared to other recent QL-based algorithms found in the literature
    ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving. (arXiv:2209.10078v2 [cs.AI] UPDATED)
    Critical scenario generation requires the ability of sampling critical combinations from the infinite parameter space in the logic scenario. Existing solutions aim to explore the correlation of action parameters in the initial scenario rather than action sequences. How to model action sequences so that one can further consider the effects of different action parameters in the scenario is the bottleneck of the problem. In this paper, we attack the problem by proposing the ECSAS framework. Specifically, we first propose a description language, BTScenario, allowing us to model action sequences of the scenarios. We then use reinforcement learning to search for combinations of critical action parameters. To increase efficiency, we further propose several optimizations, including action masking and replay buffer. We have implemented ECSAS, and experimental results show that it is more efficient than native approaches such as random and combination testing in various nontrivial scenarios.
    Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition. (arXiv:2210.00888v2 [cs.LG] UPDATED)
    Human health is closely associated with their daily behavior and environment. However, keeping a healthy lifestyle is still challenging for most people as it is difficult to recognize their living behaviors and identify their surrounding situations to take appropriate action. Human activity recognition is a promising approach to building a behavior model of users, by which users can get feedback about their habits and be encouraged to develop a healthier lifestyle. In this paper, we present a smart light wearable badge with six kinds of sensors, including an infrared array sensor MLX90640 offering privacy-preserving, low-cost, and non-invasive features, to recognize daily activities in a realistic unmodified kitchen environment. A multi-channel convolutional neural network (MC-CNN) based on data and feature fusion methods is applied to classify 14 human activities associated with potentially unhealthy habits. Meanwhile, we evaluate the impact of the infrared array sensor on the recognition accuracy of these activities. We demonstrate the performance of the proposed work to detect the 14 activities performed by ten volunteers with an average accuracy of 92.44 % and an F1 score of 88.27 %.
    RAP: Risk-Aware Prediction for Robust Planning. (arXiv:2210.01368v2 [cs.LG] UPDATED)
    Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions. Unfortunately, due to long-tail safety-critical events, the risk is often under-estimated by finite-sampling approximations of probabilistic motion forecasts. This can lead to overconfident and unsafe robot behavior, even with robust planners. Instead of assuming full prediction coverage that robust planners require, we propose to make prediction itself risk-aware. We introduce a new prediction objective to learn a risk-biased distribution over trajectories, so that risk evaluation simplifies to an expected cost estimation under this biased distribution. This reduces the sample complexity of the risk estimation during online planning, which is needed for safe real-time performance. Evaluation results in a didactic simulation environment and on a real-world dataset demonstrate the effectiveness of our approach. The code and a demo are available.
    Deep learning enhanced noise spectroscopy of a spin qubit environment. (arXiv:2301.05079v1 [quant-ph])
    The undesired interaction of a quantum system with its environment generally leads to a coherence decay of superposition states in time. A precise knowledge of the spectral content of the noise induced by the environment is crucial to protect qubit coherence and optimize its employment in quantum device applications. We experimentally show that the use of neural networks can highly increase the accuracy of noise spectroscopy, by reconstructing the power spectral density that characterizes an ensemble of carbon impurities around a nitrogen-vacancy (NV) center in diamond. Neural networks are trained over spin coherence functions of the NV center subjected to different Carr-Purcell sequences, typically used for dynamical decoupling (DD). As a result, we determine that deep learning models can be more accurate than standard DD noise-spectroscopy techniques, by requiring at the same time a much smaller number of DD sequences.
    Domain Expansion of Image Generators. (arXiv:2301.05225v1 [cs.CV])
    Can one inject new concepts into an already trained generative model, while respecting its existing structure and knowledge? We propose a new task - domain expansion - to address this. Given a pretrained generator and novel (but related) domains, we expand the generator to jointly model all domains, old and new, harmoniously. First, we note the generator contains a meaningful, pretrained latent space. Is it possible to minimally perturb this hard-earned representation, while maximally representing the new domains? Interestingly, we find that the latent space offers unused, "dormant" directions, which do not affect the output. This provides an opportunity: By "repurposing" these directions, we can represent new domains without perturbing the original representation. In fact, we find that pretrained generators have the capacity to add several - even hundreds - of new domains! Using our expansion method, one "expanded" model can supersede numerous domain-specific models, without expanding the model size. Additionally, a single expanded generator natively supports smooth transitions between domains, as well as composition of domains. Code and project page available at https://yotamnitzan.github.io/domain-expansion/.
    Benign Underfitting of Stochastic Gradient Descent. (arXiv:2202.13361v4 [cs.LG] UPDATED)
    We study to what extent may stochastic gradient descent (SGD) be understood as a "conventional" learning rule that achieves generalization performance by obtaining a good fit to training data. We consider the fundamental stochastic convex optimization framework, where (one pass, without-replacement) SGD is classically known to minimize the population risk at rate $O(1/\sqrt n)$, and prove that, surprisingly, there exist problem instances where the SGD solution exhibits both empirical risk and generalization gap of $\Omega(1)$. Consequently, it turns out that SGD is not algorithmically stable in any sense, and its generalization ability cannot be explained by uniform convergence or any other currently known generalization bound technique for that matter (other than that of its classical analysis). We then continue to analyze the closely related with-replacement SGD, for which we show that an analogous phenomenon does not occur and prove that its population risk does in fact converge at the optimal rate. Finally, we interpret our main results in the context of without-replacement SGD for finite-sum convex optimization problems, and derive upper and lower bounds for the multi-epoch regime that significantly improve upon previously known results.
    Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning. (arXiv:2301.05075v1 [astro-ph.EP])
    Observations of protoplanetary discs have shown that forming exoplanets leave characteristic imprints on the gas and dust of the disc. In the gas, these forming exoplanets cause deviations from Keplerian motion, which can be detected through molecular line observations. Our previous work has shown that machine learning can correctly determine if a planet is present in these discs. Using our machine learning models, we identify strong, localized non-Keplerian motion within the disc HD 142666. Subsequent hydrodynamics simulations of a system with a 5 Jupiter-mass planet at 75 au recreates the kinematic structure. By currently established standards in the field, we conclude that HD 142666 hosts a planet. This work represents a first step towards using machine learning to identify previously overlooked non-Keplerian features in protoplanetary discs.
    Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. (arXiv:2301.05169v1 [cs.LG])
    Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In this paper, we present Causal Triplet, a causal representation learning benchmark featuring not only visually more complex scenes, but also two crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual setting, where only certain object-level variables allow for counterfactual observations whereas others do not; (ii) an interventional downstream task with an emphasis on out-of-distribution robustness from the independent causal mechanisms principle. Through extensive experiments, we find that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts. However, recent causal representation learning methods still struggle to identify such latent structures, indicating substantial challenges and opportunities for future work. Our code and datasets will be available at https://sites.google.com/view/causaltriplet.
    Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning. (arXiv:2210.07805v3 [cs.LG] UPDATED)
    Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few active learning studies have attempted to deal with this open-set noise for sample selection by filtering out the noisy examples. However, because focusing on the purity of examples in a query set leads to overlooking the informativeness of the examples, the best balancing of purity and informativeness remains an important question. In this paper, to solve this purity-informativeness dilemma in open-set active learning, we propose a novel Meta-Query-Net,(MQ-Net) that adaptively finds the best balancing between the two factors. Specifically, by leveraging the multi-round property of active learning, we train MQ-Net using a query set without an additional validation set. Furthermore, a clear dominance relationship between unlabeled examples is effectively captured by MQ-Net through a novel skyline regularization. Extensive experiments on multiple open-set active learning scenarios demonstrate that the proposed MQ-Net achieves 20.14% improvement in terms of accuracy, compared with the state-of-the-art methods.
    SemPPL: Predicting pseudo-labels for better contrastive representations. (arXiv:2301.05158v1 [cs.CV])
    Learning from large amounts of unsupervised data and a small amount of supervision is an important open problem in computer vision. We propose a new semi-supervised learning method, Semantic Positives via Pseudo-Labels (SemPPL), that combines labelled and unlabelled data to learn informative representations. Our method extends self-supervised contrastive learning -- where representations are shaped by distinguishing whether two samples represent the same underlying datum (positives) or not (negatives) -- with a novel approach to selecting positives. To enrich the set of positives, we leverage the few existing ground-truth labels to predict the missing ones through a $k$-nearest neighbours classifier by using the learned embeddings of the labelled data. We thus extend the set of positives with datapoints having the same pseudo-label and call these semantic positives. We jointly learn the representation and predict bootstrapped pseudo-labels. This creates a reinforcing cycle. Strong initial representations enable better pseudo-label predictions which then improve the selection of semantic positives and lead to even better representations. SemPPL outperforms competing semi-supervised methods setting new state-of-the-art performance of $68.5\%$ and $76\%$ top-$1$ accuracy when using a ResNet-$50$ and training on $1\%$ and $10\%$ of labels on ImageNet, respectively. Furthermore, when using selective kernels, SemPPL significantly outperforms previous state-of-the-art achieving $72.3\%$ and $78.3\%$ top-$1$ accuracy on ImageNet with $1\%$ and $10\%$ labels, respectively, which improves absolute $+7.8\%$ and $+6.2\%$ over previous work. SemPPL also exhibits state-of-the-art performance over larger ResNet models as well as strong robustness, out-of-distribution and transfer performance.
    Explicit Context Integrated Recurrent Neural Network for Sensor Data Applications. (arXiv:2301.05031v1 [cs.LG])
    The development and progress in sensor, communication and computing technologies have led to data rich environments. In such environments, data can easily be acquired not only from the monitored entities but also from the surroundings where the entity is operating. The additional data that are available from the problem domain, which cannot be used independently for learning models, constitute context. Such context, if taken into account while learning, can potentially improve the performance of predictive models. Typically, the data from various sensors are present in the form of time series. Recurrent Neural Networks (RNNs) are preferred for such data as it can inherently handle temporal context. However, the conventional RNN models such as Elman RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in their present form do not provide any mechanism to integrate explicit contexts. In this paper, we propose a Context Integrated RNN (CiRNN) that enables integrating explicit contexts represented in the form of contextual features. In CiRNN, the network weights are influenced by contextual features in such a way that the primary input features which are more relevant to a given context are given more importance. To show the efficacy of CiRNN, we selected an application domain, engine health prognostics, which captures data from various sensors and where contextual information is available. We used the NASA Turbofan Engine Degradation Simulation dataset for estimating Remaining Useful Life (RUL) as it provides contextual information. We compared CiRNN with baseline models as well as the state-of-the-art methods. The experimental results show an improvement of 39% and 87% respectively, over state-of-the art models, when performance is measured with RMSE and score from an asymmetric scoring function. The latter measure is specific to the task of RUL estimation.
    Automated Sleep Staging via Parallel Frequency-Cut Attention. (arXiv:2204.03173v3 [cs.LG] UPDATED)
    This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance. The framework consists of two parts: the first part extracts informative features by partitioning the input EEG spectrograms into a sequence of time-frequency patches. The second part is constituted by an attention-based architecture to efficiently search for the correlation between partitioned time-frequency patches and defining factors of sleep stages in parallel. The proposed pipeline is validated on the Sleep Heart Health Study dataset with new state-of-the-art results for the stages wake, N2, and N3, obtaining respective F1 scores of 0.93, 0.88, and 0.87, with only EEG signals used. The proposed method also has a high inter-rater reliability of 0.80 kappa. We also visualize the correspondence between sleep staging decisions and features extracted by the proposed method, providing strong interpretability for our model.
    Asynchronous training of quantum reinforcement learning. (arXiv:2301.05096v1 [quant-ph])
    The development of quantum machine learning (QML) has received a lot of interest recently thanks to developments in both quantum computing (QC) and machine learning (ML). One of the ML paradigms that can be utilized to address challenging sequential decision-making issues is reinforcement learning (RL). It has been demonstrated that classical RL can successfully complete many difficult tasks. A leading method of building quantum RL agents relies on the variational quantum circuits (VQC). However, training QRL algorithms with VQCs requires significant amount of computational resources. This issue hurdles the exploration of various QRL applications. In this paper, we approach this challenge through asynchronous training QRL agents. Specifically, we choose the asynchronous training of advantage actor-critic variational quantum policies. We demonstrate the results via numerical simulations that within the tasks considered, the asynchronous training of QRL agents can reach performance comparable to or superior than classical agents with similar model sizes and architectures.
    Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential. (arXiv:2207.11592v5 [physics.chem-ph] UPDATED)
    Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and tradeoffs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.
    An overview of open source Deep Learning-based libraries for Neuroscience. (arXiv:2301.05057v1 [q-bio.QM])
    In recent years, deep learning revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis. Due to the fast growth of the domain, it could be a complicated and extremely time-consuming task for worldwide researchers to have a clear perspective of the most recent and advanced software libraries. This work contributes to clarify the current situation in the domain, outlining the most useful libraries that implement and facilitate deep learning application to neuroscience, allowing scientists to identify the most suitable options for their research or clinical projects. This paper summarizes the main developments in Deep Learning and their relevance to Neuroscience; it then reviews neuroinformatic toolboxes and libraries, collected from the literature and from specific hubs of software projects oriented to neuroscience research. The selected tools are presented in tables detailing key features grouped by domain of application (e.g. data type, neuroscience area, task), model engineering (e.g. programming language, model customization) and technological aspect (e.g. interface, code source). The results show that, among a high number of available software tools, several libraries are standing out in terms of functionalities for neuroscience applications. The aggregation and discussion of this information can help the neuroscience community to devolop their research projects more efficiently and quickly, both by means of readily available tools, and by knowing which modules may be improved, connected or added.
    Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection. (arXiv:2301.05131v1 [cs.LG])
    We show, to our knowledge, the first theoretical treatments of two common questions in cross-validation based hyperparameter selection: (1) After selecting the best hyperparameter using a held-out set, we train the final model using {\em all} of the training data -- since this may or may not improve future generalization error, should one do this? (2) During optimization such as via SGD (stochastic gradient descent), we must set the optimization tolerance $\rho$ -- since it trades off predictive accuracy with computation cost, how should one set it? Toward these problems, we introduce the {\em hold-in risk} (the error due to not using the whole training data), and the {\em model class mis-specification risk} (the error due to having chosen the wrong model class) in a theoretical view which is simple, general, and suggests heuristics that can be used when faced with a dataset instance. In proof-of-concept studies in synthetic data where theoretical quantities can be controlled, we show that these heuristics can, respectively, (1) always perform at least as well as always performing retraining or never performing retraining, (2) either improve performance or reduce computational overhead by $2\times$ with no loss in predictive performance.
    Efficient Ridge Solution for the Incremental Broad Learning System on Added Nodes by Inverse Cholesky Factorization of a Partitioned Matrix. (arXiv:1911.04872v4 [cs.LG] UPDATED)
    To accelerate the existing Broad Learning System (BLS) for new added nodes in [7], we extend the inverse Cholesky factorization in [10] to deduce an efficient inverse Cholesky factorization for a Hermitian matrix partitioned into 2 * 2 blocks, which is utilized to develop the proposed BLS algorithm 1. The proposed BLS algorithm 1 compute the ridge solution (i.e, the output weights) from the inverse Cholesky factor of the Hermitian matrix in the ridge inverse, and update the inverse Cholesky factor efficiently. From the proposed BLS algorithm 1, we deduce the proposed ridge inverse, which can be obtained from the generalized inverse in [7] by just change one matrix in the equation to compute the newly added sub-matrix. We also modify the proposed algorithm 1 into the proposed algorithm 2, which is equivalent to the existing BLS algorithm [7] in terms of numerical computations. The proposed algorithms 1 and 2 can reduce the computational complexity, since usually the Hermitian matrix in the ridge inverse is smaller than the ridge inverse. With respect to the existing BLS algorithm, the proposed algorithms 1 and 2 usually require about 13 and 2 3 of complexities, respectively, while in numerical experiments they achieve the speedups (in each additional training time) of 2.40 - 2.91 and 1.36 - 1.60, respectively. Numerical experiments also show that the proposed algorithm 1 and the standard ridge solution always bear the same testing accuracy, and usually so do the proposed algorithm 2 and the existing BLS algorithm. The existing BLS assumes the ridge parameter lamda->0, since it is based on the generalized inverse with the ridge regression approximation. When the assumption of lamda-> 0 is not satisfied, the standard ridge solution obviously achieves a better testing accuracy than the existing BLS algorithm in numerical experiments.
    A Stochastic Proximal Polyak Step Size. (arXiv:2301.04935v1 [math.OC])
    Recently, the stochastic Polyak step size (SPS) has emerged as a competitive adaptive step size scheme for stochastic gradient descent. Here we develop ProxSPS, a proximal variant of SPS that can handle regularization terms. Developing a proximal variant of SPS is particularly important, since SPS requires a lower bound of the objective function to work well. When the objective function is the sum of a loss and a regularizer, available estimates of a lower bound of the sum can be loose. In contrast, ProxSPS only requires a lower bound for the loss which is often readily available. As a consequence, we show that ProxSPS is easier to tune and more stable in the presence of regularization. Furthermore for image classification tasks, ProxSPS performs as well as AdamW with little to no tuning, and results in a network with smaller weight parameters. We also provide an extensive convergence analysis for ProxSPS that includes the non-smooth, smooth, weakly convex and strongly convex setting.
    Counterfactual Explanations for Concepts in $\mathcal{ELH}$. (arXiv:2301.05109v1 [cs.AI])
    Knowledge bases are widely used for information management on the web, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g. for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify fair decisions, a number of explanation approaches have been proposed using concepts in description logics. However, the learned concepts can become long and difficult to fathom for non-experts, even when verbalized. Moreover, long concepts do not immediately provide a clear path of action to change one's situation. Counterfactuals answering the question "How must feature values be changed to obtain a different classification?" have been proposed as short, human-friendly explanations for tabular data. In this paper, we transfer the notion of counterfactuals to description logics and propose the first algorithm for generating counterfactual explanations in the description logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts and the candidates with fewest feature changes are selected as counterfactuals. In case of multiple counterfactuals, we rank them according to the likeliness of their feature combinations. For evaluation, we conduct a user survey to investigate which of the generated counterfactual candidates are preferred for explanation by participants. In a second study, we explore possible use cases for counterfactual explanations.
    Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images. (arXiv:2301.04802v1 [cs.LG])
    Despite continued advancement in recent years, deep neural networks still rely on large amounts of training data to avoid overfitting. However, labeled training data for real-world applications such as healthcare is limited and difficult to access given longstanding privacy, and strict data sharing policies. By manipulating image datasets in the pixel or feature space, existing data augmentation techniques represent one of the effective ways to improve the quantity and diversity of training data. Here, we look to advance augmentation techniques by building upon the emerging success of text-to-image diffusion probabilistic models in augmenting the training samples of our macroscopic skin disease dataset. We do so by enabling fine-grained control of the image generation process via input text prompts. We demonstrate that this generative data augmentation approach successfully maintains a similar classification accuracy of the visual classifier even when trained on a fully synthetic skin disease dataset. Similar to recent applications of generative models, our study suggests that diffusion models are indeed effective in generating high-quality skin images that do not sacrifice the classifier performance, and can improve the augmentation of training datasets after curation.  ( 2 min )
    Multimodal Deep Learning. (arXiv:2301.04856v1 [cs.CL])
    This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in which one modality is utilized to enhance representation learning for the other. To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced. Finally, we also cover other modalities as well as general-purpose multi-modal models, which are able to handle different tasks on different modalities within one unified architecture. One interesting application (Generative Art) eventually caps off this booklet.  ( 2 min )
    Sparse Coding in a Dual Memory System for Lifelong Learning. (arXiv:2301.05058v1 [cs.NE])
    Efficient continual learning in humans is enabled by a rich set of neurophysiological mechanisms and interactions between multiple memory systems. The brain efficiently encodes information in non-overlapping sparse codes, which facilitates the learning of new associations faster with controlled interference with previous associations. To mimic sparse coding in DNNs, we enforce activation sparsity along with a dropout mechanism which encourages the model to activate similar units for semantically similar inputs and have less overlap with activation patterns of semantically dissimilar inputs. This provides us with an efficient mechanism for balancing the reusability and interference of features, depending on the similarity of classes across tasks. Furthermore, we employ sparse coding in a multiple-memory replay mechanism. Our method maintains an additional long-term semantic memory that aggregates and consolidates information encoded in the synaptic weights of the working model. Our extensive evaluation and characteristics analysis show that equipped with these biologically inspired mechanisms, the model can further mitigate forgetting.  ( 2 min )
    Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces. (arXiv:2301.04887v1 [math.NA])
    We introduce a novel spectral, finite-dimensional approximation of general Sobolev spaces in terms of Chebyshev polynomials. Based on this polynomial surrogate model (PSM), we realise a variational formulation, solving a vast class of linear and non-linear partial differential equations (PDEs). The PSMs are as flexible as the physics-informed neural nets (PINNs) and provide an alternative for addressing inverse PDE problems, such as PDE-parameter inference. In contrast to PINNs, the PSMs result in a convex optimisation problem for a vast class of PDEs, including all linear ones, in which case the PSM-approximate is efficiently computable due to the exponential convergence rate of the underlying variational gradient descent. As a practical consequence prominent PDE problems were resolved by the PSMs without High Performance Computing (HPC) on a local machine. This gain in efficiency is complemented by an increase of approximation power, outperforming PINN alternatives in both accuracy and runtime. Beyond the empirical evidence we give here, the translation of classic PDE theory in terms of the Sobolev space approximates suggests the PSMs to be universally applicable to well-posed, regular forward and inverse PDE problems.  ( 2 min )
    Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder. (arXiv:2301.05017v1 [eess.SP])
    An enhanced framework for peak-to-average power ratio ($\mathsf{PAPR}$) reduction and waveform design for Multiple-Input-Multiple-Output ($\mathsf{MIMO}$) orthogonal frequency-division multiplexing ($\mathsf{OFDM}$) systems, based on a convolutional-autoencoder ($\mathsf{CAE}$) architecture, is presented. The end-to-end learning-based autoencoder ($\mathsf{AE}$) for communication networks represents the network by an encoder and decoder, where in between, the learned latent representation goes through a physical communication channel. We introduce a joint learning scheme based on projected gradient descent iteration to optimize the spectral mask behavior and MIMO detection under the influence of a non-linear high power amplifier ($\mathsf{HPA}$) and a multipath fading channel. The offered efficient implementation novel waveform design technique utilizes only a single $\mathsf{PAPR}$ reduction block for all antennas. It is throughput-lossless, as no side information is required at the decoder. Performance is analyzed by examining the bit error rate ($\mathsf{BER}$), the $\mathsf{PAPR}$, and the spectral response and compared with classical $\mathsf{PAPR}$ reduction $\mathsf{MIMO}$ detector methods on 5G simulated data. The suggested system exhibits competitive performance when considering all optimization criteria simultaneously. We apply gradual loss learning for multi-objective optimization and show empirically that a single trained model covers the tasks of $\mathsf{PAPR}$ reduction, spectrum design, and $\mathsf{MIMO}$ detection together over a wide range of SNR levels.  ( 2 min )
    ChatGPT is not all you need. A State of the Art Review of large Generative AI models. (arXiv:2301.04655v1 [cs.LG])
    During the last two years there has been a plethora of large generative models such as ChatGPT or Stable Diffusion that have been published. Concretely, these models are able to perform tasks such as being a general question and answering system or automatically creating artistic images that are revolutionizing several sectors. Consequently, the implications that these generative models have in the industry and society are enormous, as several job positions may be transformed. For example, Generative AI is capable of transforming effectively and creatively texts to images, like the DALLE-2 model; text to 3D images, like the Dreamfusion model; images to text, like the Flamingo model; texts to video, like the Phenaki model; texts to audio, like the AudioLM model; texts to other texts, like ChatGPT; texts to code, like the Codex model; texts to scientific texts, like the Galactica model or even create algorithms like AlphaTensor. This work consists on an attempt to describe in a concise way the main models are sectors that are affected by generative AI and to provide a taxonomy of the main generative models published recently.  ( 2 min )
    Safe Policy Improvement for POMDPs via Finite-State Controllers. (arXiv:2301.04939v1 [cs.AI])
    We study safe policy improvement (SPI) for partially observable Markov decision processes (POMDPs). SPI is an offline reinforcement learning (RL) problem that assumes access to (1) historical data about an environment, and (2) the so-called behavior policy that previously generated this data by interacting with the environment. SPI methods neither require access to a model nor the environment itself, and aim to reliably improve the behavior policy in an offline manner. Existing methods make the strong assumption that the environment is fully observable. In our novel approach to the SPI problem for POMDPs, we assume that a finite-state controller (FSC) represents the behavior policy and that finite memory is sufficient to derive optimal policies. This assumption allows us to map the POMDP to a finite-state fully observable MDP, the history MDP. We estimate this MDP by combining the historical data and the memory of the FSC, and compute an improved policy using an off-the-shelf SPI algorithm. The underlying SPI method constrains the policy-space according to the available data, such that the newly computed policy only differs from the behavior policy when sufficient data was available. We show that this new policy, converted into a new FSC for the (unknown) POMDP, outperforms the behavior policy with high probability. Experimental results on several well-established benchmarks show the applicability of the approach, even in cases where finite memory is not sufficient.  ( 2 min )
    Thompson Sampling with Diffusion Generative Prior. (arXiv:2301.05182v1 [cs.LG])
    In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit framework, with the goal of learning a strategy that performs well across bandit tasks of a same class. To this end, we train a diffusion model that learns the underlying task distribution and combine Thompson sampling with the learned prior to deal with new tasks at test time. Our posterior sampling algorithm is designed to carefully balance between the learned prior and the noisy observations that come from the learner's interaction with the environment. To capture realistic bandit scenarios, we also propose a novel diffusion model training procedure that trains even from incomplete and/or noisy data, which could be of independent interest. Finally, our extensive experimental evaluations clearly demonstrate the potential of the proposed approach.  ( 2 min )
    Manifold Fitting under Unbounded Noise. (arXiv:1909.10228v2 [stat.ML] UPDATED)
    There has been an emerging trend in non-Euclidean statistical analysis of aiming to recover a low dimensional structure, namely a manifold, underlying the high dimensional data. Recovering the manifold requires the noise to be of certain concentration. Existing methods address this problem by constructing an approximated manifold based on the tangent space estimation at each sample point. Although theoretical convergence for these methods is guaranteed, either the samples are noiseless or the noise is bounded. However, if the noise is unbounded, which is a common scenario, the tangent space estimation at the noisy samples will be blurred. Fitting a manifold from the blurred tangent space might increase the inaccuracy. In this paper, we introduce a new manifold-fitting method, by which the output manifold is constructed by directly estimating the tangent spaces at the projected points on the underlying manifold, rather than at the sample points, to decrease the error caused by the noise. Assuming the noise is unbounded, our new method provides theoretical convergence in high probability, in terms of the upper bound of the distance between the estimated and underlying manifold. The smoothness of the estimated manifold is also evaluated by bounding the supremum of twice difference above. Numerical simulations are provided to validate our theoretical findings and demonstrate the advantages of our method over other relevant manifold fitting methods. Finally, our method is applied to real data examples.  ( 2 min )
    ViTs for SITS: Vision Transformers for Satellite Image Time Series. (arXiv:2301.04944v1 [cs.CV])
    In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes are made publicly available to facilitate further research.  ( 2 min )
    Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principle. (arXiv:2301.05099v1 [cs.LG])
    Many popular machine learning models scale poorly when deployed on CPUs. In this paper we explore the reasons why and propose a simple, yet effective approach based on the well-known Divide-and-Conquer Principle to tackle this problem of great practical importance. Given an inference job, instead of using all available computing resources (i.e., CPU cores) for running it, the idea is to break the job into independent parts that can be executed in parallel, each with the number of cores according to its expected computational cost. We implement this idea in the popular OnnxRuntime framework and evaluate its effectiveness with several use cases, including the well-known models for optical character recognition (PaddleOCR) and natural language processing (BERT).  ( 2 min )
    Phase-shifted Adversarial Training. (arXiv:2301.04785v1 [cs.LG])
    Adversarial training has been considered an imperative component for safely deploying neural network-based applications to the real world. To achieve stronger robustness, existing methods primarily focus on how to generate strong attacks by increasing the number of update steps, regularizing the models with the smoothed loss function, and injecting the randomness into the attack. Instead, we analyze the behavior of adversarial training through the lens of response frequency. We empirically discover that adversarial training causes neural networks to have low convergence to high-frequency information, resulting in highly oscillated predictions near each data. To learn high-frequency contents efficiently and effectively, we first prove that a universal phenomenon of frequency principle, i.e., \textit{lower frequencies are learned first}, still holds in adversarial training. Based on that, we propose phase-shifted adversarial training (PhaseAT) in which the model learns high-frequency components by shifting these frequencies to the low-frequency range where the fast convergence occurs. For evaluations, we conduct the experiments on CIFAR-10 and ImageNet with the adaptive attack carefully designed for reliable evaluation. Comprehensive results show that PhaseAT significantly improves the convergence for high-frequency information. This results in improved adversarial robustness by enabling the model to have smoothed predictions near each data.  ( 2 min )
    Private estimation algorithms for stochastic block models and mixture models. (arXiv:2301.04822v1 [cs.DS])
    We introduce general tools for designing efficient private estimation algorithms, in the high-dimensional settings, whose statistical guarantees almost match those of the best known non-private algorithms. To illustrate our techniques, we consider two problems: recovery of stochastic block models and learning mixtures of spherical Gaussians. For the former, we present the first efficient $(\epsilon, \delta)$-differentially private algorithm for both weak recovery and exact recovery. Previously known algorithms achieving comparable guarantees required quasi-polynomial time. For the latter, we design an $(\epsilon, \delta)$-differentially private algorithm that recovers the centers of the $k$-mixture when the minimum separation is at least $ O(k^{1/t}\sqrt{t})$. For all choices of $t$, this algorithm requires sample complexity $n\geq k^{O(1)}d^{O(t)}$ and time complexity $(nd)^{O(t)}$. Prior work required minimum separation at least $O(\sqrt{k})$ as well as an explicit upper bound on the Euclidean norm of the centers.  ( 2 min )
    Machine learning methods for prediction of breakthrough curves in reactive porous media. (arXiv:2301.04998v1 [physics.flu-dyn])
    Reactive flows in porous media play an important role in our life and are crucial for many industrial, environmental and biomedical applications. Very often the concentration of the species at the inlet is known, and the so-called breakthrough curves, measured at the outlet, are the quantities which could be measured or computed numerically. The measurements and the simulations could be time-consuming and expensive, and machine learning and Big Data approaches can help to predict breakthrough curves at lower costs. Machine learning (ML) methods, such as Gaussian processes and fully-connected neural networks, and a tensor method, cross approximation, are well suited for predicting breakthrough curves. In this paper, we demonstrate their performance in the case of pore scale reactive flow in catalytic filters.  ( 2 min )
    Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimes. (arXiv:2301.04771v1 [stat.ML])
    Variational inference has been widely used in machine learning literature to fit various Bayesian models. In network analysis, this method has been successfully applied to solve the community detection problems. Although these results are promising, their theoretical support is only for relatively dense networks, an assumption that may not hold for real networks. In addition, it has been shown recently that the variational loss surface has many saddle points, which may severely affect its performance, especially when applied to sparse networks. This paper proposes a simple way to improve the variational inference method by hard thresholding the posterior of the community assignment after each iteration. Using a random initialization that correlates with the true community assignment, we show that the proposed method converges and can accurately recover the true community labels, even when the average node degree of the network is bounded. Extensive numerical study further confirms the advantage of the proposed method over the classical variational inference and another state-of-the-art algorithm.  ( 2 min )
    Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction. (arXiv:2301.04791v1 [stat.ML])
    Max sliced Wasserstein (Max-SW) distance has been widely known as a solution for redundant projections of sliced Wasserstein (SW) distance. In applications that have various independent pairs of probability measures, amortized projection optimization is utilized to predict the ``max" projecting directions given two input measures instead of using projected gradient ascent multiple times. Despite being efficient, the first issue of the current framework is the violation of permutation invariance property and symmetry property. To address the issue, we propose to design amortized models based on self-attention architecture. Moreover, we adopt efficient self-attention architectures to make the computation linear in the number of supports. Secondly, Max-SW and its amortized version cannot guarantee metricity property due to the sub-optimality of the projected gradient ascent and the amortization gap. Therefore, we propose to replace Max-SW with distributional sliced Wasserstein distance with von Mises-Fisher (vMF) projecting distribution (v-DSW). Since v-DSW is a metric with any non-degenerate vMF distribution, its amortized version can guarantee the metricity when predicting the best discriminate projecting distribution. With the two improvements, we derive self-attention amortized distributional projection optimization and show its appealing performance in point-cloud reconstruction and its downstream applications.  ( 2 min )
    Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised Image Segmentation. (arXiv:2301.04705v1 [cs.CV])
    Image segmentation is a very popular and important task in computer vision. In this paper, inverse quantum Fourier transform (IQFT) for image segmentation has been explored and a novel IQFT-inspired algorithm is proposed and implemented by leveraging the underlying mathematical structure of the IQFT. Specifically, the proposed method takes advantage of the phase information of the pixels in the image by encoding the pixels' intensity into qubit relative phases and applying IQFT to classify the pixels into different segments automatically and efficiently. To the best of our knowledge, this is the first attempt of using IQFT for unsupervised image segmentation. The proposed method has low computational cost comparing to the deep learning-based methods and more importantly it does not require training, thus make it suitable for real-time applications. The performance of the proposed method is compared with K-means and Otsu-thresholding. The proposed method outperforms both of them on the PASCAL VOC 2012 segmentation benchmark and the xVIEW2 challenge dataset by as much as 50% in terms of mean Intersection-Over-Union (mIOU).  ( 2 min )
    KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution. (arXiv:2301.04770v1 [cs.CL])
    Entity resolution has been an essential and well-studied task in data cleaning research for decades. Existing work has discussed the feasibility of utilizing pre-trained language models to perform entity resolution and achieved promising results. However, few works have discussed injecting domain knowledge to improve the performance of pre-trained language models on entity resolution tasks. In this study, we propose Knowledge Augmented Entity Resolution (KAER), a novel framework named for augmenting pre-trained language models with external knowledge for entity resolution. We discuss the results of utilizing different knowledge augmentation and prompting methods to improve entity resolution performance. Our model improves on Ditto, the existing state-of-the-art entity resolution method. In particular, 1) KAER performs more robustly and achieves better results on "dirty data", and 2) with more general knowledge injection, KAER outperforms the existing baseline models on the textual dataset and dataset from the online product domain. 3) KAER achieves competitive results on highly domain-specific datasets, such as citation datasets, requiring the injection of expert knowledge in future work.  ( 2 min )
    Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application. (arXiv:2301.04829v1 [cs.LG])
    Federated learning (FL) shines through in the internet of things (IoT) with its ability to realize collaborative learning and improve learning efficiency by sharing client model parameters trained on local data. Although FL has been successfully applied to various domains, including driver monitoring application (DMA) on the internet of vehicles (IoV), its usages still face some open issues, such as data and system heterogeneity, large-scale parallelism communication resources, malicious attacks, and data poisoning. This paper proposes a federated transfer-ordered-personalized learning (FedTOP) framework to address the above problems and test on two real-world datasets with and without system heterogeneity. The performance of the three extensions, transfer, ordered, and personalized, is compared by an ablation study and achieves 92.32% and 95.96% accuracy on the test clients of two datasets, respectively. Compared to the baseline, there is a 462% improvement in accuracy and a 37.46% reduction in communication resource consumption. The results demonstrate that the proposed FedTOP can be used as a highly accurate, streamlined, privacy-preserving, cybersecurity-oriented, personalized framework for DMA.  ( 2 min )
    The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features. (arXiv:2301.04740v1 [cs.LG])
    Over the past few years, the use of machine learning models has emerged as a generic and powerful means for prediction purposes. At the same time, there is a growing demand for interpretability of prediction models. To determine which features of a dataset are important to predict a target variable $Y$, a Feature Importance (FI) method can be used. By quantifying how important each feature is for predicting $Y$, irrelevant features can be identified and removed, which could increase the speed and accuracy of a model, and moreover, important features can be discovered, which could lead to valuable insights. A major problem with evaluating FI methods, is that the ground truth FI is often unknown. As a consequence, existing FI methods do not give the exact correct FI values. This is one of the many reasons why it can be hard to properly interpret the results of an FI method. Motivated by this, we introduce a new global approach named the Berkelmans-Pries FI method, which is based on a combination of Shapley values and the Berkelmans-Pries dependency function. We prove that our method has many useful properties, and accurately predicts the correct FI values for several cases where the ground truth FI can be derived in an exact manner. We experimentally show for a large collection of FI methods (468) that existing methods do not have the same useful properties. This shows that the Berkelmans-Pries FI method is a highly valuable tool for analyzing datasets with complex interdependencies.  ( 2 min )
    Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN to Reduce Sample Size and Speed up Learning -- Application in Gomoku Reinforcement Learning. (arXiv:2301.04746v1 [cs.LG])
    To replace data augmentation, this paper proposed a method called SLAP to intensify experience to speed up machine learning and reduce the sample size. SLAP is a model-independent protocol/function to produce the same output given different transformation variants. SLAP improved the convergence speed of convolutional neural network learning by 83% in the experiments with Gomoku game states, with only one eighth of the sample size compared with data augmentation. In reinforcement learning for Gomoku, using AlphaGo Zero/AlphaZero algorithm with data augmentation as baseline, SLAP reduced the number of training samples by a factor of 8 and achieved similar winning rate against the same evaluator, but it was not yet evident that it could speed up reinforcement learning. The benefits should at least apply to domains that are invariant to symmetry or certain transformations. As future work, SLAP may aid more explainable learning and transfer learning for domains that are not invariant to symmetry, as a small step towards artificial general intelligence.  ( 2 min )
    NarrowBERT: Accelerating Masked Language Model Pretraining and Inference. (arXiv:2301.04761v1 [cs.CL])
    Large-scale language model pretraining is a very successful form of self-supervised learning in natural language processing, but it is increasingly expensive to perform as the models and pretraining corpora have become larger over time. We propose NarrowBERT, a modified transformer encoder that increases the throughput for masked language model pretraining by more than $2\times$. NarrowBERT sparsifies the transformer model such that the self-attention queries and feedforward layers only operate on the masked tokens of each sentence during pretraining, rather than all of the tokens as with the usual transformer encoder. We also show that NarrowBERT increases the throughput at inference time by as much as $3.5\times$ with minimal (or no) performance degradation on sentence encoding tasks like MNLI. Finally, we examine the performance of NarrowBERT on the IMDB and Amazon reviews classification and CoNLL NER tasks and show that it is also comparable to standard BERT performance.  ( 2 min )
    We are Going to the Space -- Part 1: Which device to deploy in a satellite?. (arXiv:2301.04954v1 [cs.LG])
    The shrinkage in sizes of components that make up satellites led to wider and low cost availability of satellites. As a result, there has been an advent of smaller organizations having the ability to deploy satellites with a variety of data-intensive applications to run on them. One popular application is image analysis to detect, for example, land, ice, clouds, etc. However, the resource-constrained nature of the devices deployed in satellites creates additional challenges for this resource-intensive application. In this paper, we investigate the performance of a variety of edge devices for deep-learning-based image processing in space. Our goal is to determine the devices that satisfy the latency and power constraints of satellites while achieving reasonably accurate results. Our results demonstrate that hardware accelerators (TPUs, GPUs) are necessary to reach the latency requirements. On the other hand, state-of-the-art edge devices with GPUs could have a high power draw, making them unsuitable for deployment on a satellite.  ( 2 min )
    Unsupervised Driving Event Discovery Based on Vehicle CAN-data. (arXiv:2301.04988v1 [cs.LG])
    The data collected from a vehicle's Controller Area Network (CAN) can quickly exceed human analysis or annotation capabilities when considering fleets of vehicles, which stresses the importance of unsupervised machine learning methods. This work presents a simultaneous clustering and segmentation approach for vehicle CAN-data that identifies common driving events in an unsupervised manner. The approach builds on self-supervised learning (SSL) for multivariate time series to distinguish different driving events in the learned latent space. We evaluate our approach with a dataset of real Tesla Model 3 vehicle CAN-data and a two-hour driving session that we annotated with different driving events. With our approach, we evaluate the applicability of recent time series-related contrastive and generative SSL techniques to learn representations that distinguish driving events. Compared to state-of-the-art (SOTA) generative SSL methods for driving event discovery, we find that contrastive learning approaches reach similar performance.  ( 2 min )
    Online Hyperparameter Optimization for Class-Incremental Learning. (arXiv:2301.05032v1 [cs.LG])
    Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existing CIL models can achieve the optimal tradeoff in different data-receiving settings--where typically the training-from-half (TFH) setting needs more stability, but the training-from-scratch (TFS) needs more plasticity. To this end, we design an online learning method that can adaptively optimize the tradeoff without knowing the setting as a priori. Specifically, we first introduce the key hyperparameters that influence the trade-off, e.g., knowledge distillation (KD) loss weights, learning rates, and classifier types. Then, we formulate the hyperparameter optimization process as an online Markov Decision Process (MDP) problem and propose a specific algorithm to solve it. We apply local estimated rewards and a classic bandit algorithm Exp3 [4] to address the issues when applying online MDP methods to the CIL protocol. Our method consistently improves top-performing CIL methods in both TFH and TFS settings, e.g., boosting the average accuracy of TFH and TFS by 2.2 percentage points on ImageNet-Full, compared to the state-of-the-art [23].  ( 2 min )
    LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks. (arXiv:2301.04794v1 [cs.LG])
    Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data. However, it requires considerable computational power to learn and implement both software and hardware aspects. This paper proposed a novel LiteLSTM architecture based on reducing the LSTM computation components via the weights sharing concept to reduce the overall architecture computation cost and maintain the architecture performance. The proposed LiteLSTM can be significant for processing large data where time-consuming is crucial while hardware resources are limited, such as the security of IoT devices and medical data processing. The proposed model was evaluated and tested empirically on three different datasets from the computer vision, cybersecurity, speech emotion recognition domains. The proposed LiteLSTM has comparable accuracy to the other state-of-the-art recurrent architecture while using a smaller computation budget.  ( 2 min )
    Graph Laplacian for Semi-Supervised Learning. (arXiv:2301.04956v1 [cs.CV])
    Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph (or nonlocal) Laplacian is a fundamental smoothing operator for solving various learning tasks. For unsupervised clustering, a spectral embedding is often used, based on graph-Laplacian eigenvectors. For semi-supervised problems, the common approach is to solve a constrained optimization problem, regularized by a Dirichlet energy, based on the graph-Laplacian. However, as supervision decreases, Dirichlet optimization becomes suboptimal. We therefore would like to obtain a smooth transition between unsupervised clustering and low-supervised graph-based classification. In this paper, we propose a new type of graph-Laplacian which is adapted for Semi-Supervised Learning (SSL) problems. It is based on both density and contrastive measures and allows the encoding of the labeled data directly in the operator. Thus, we can perform successfully semi-supervised learning using spectral clustering. The benefits of our approach are illustrated for several SSL problems.  ( 2 min )
    Universality of neural dynamics on complex networks. (arXiv:2301.04900v1 [cond-mat.stat-mech])
    This paper discusses the capacity of graph neural networks to learn the functional form of ordinary differential equations that govern dynamics on complex networks. We propose necessary elements for such a problem, namely, inductive biases, a neural network architecture and a learning task. Statistical learning theory suggests that generalisation power of neural networks relies on independence and identical distribution (i.i.d.)\ of training and testing data. Although this assumption together with an appropriate neural architecture and a learning mechanism is sufficient for accurate out-of-sample predictions of dynamics such as, e.g.\ mass-action kinetics, by studying the out-of-distribution generalisation in the case of diffusion dynamics, we find that the neural network model: (i) has a generalisation capacity that depends on the first moment of the initial value data distribution; (ii) learns the non-dissipative nature of dynamics implicitly; and (iii) the model's accuracy resolution limit is of order $\mathcal{O}(1/\sqrt{n})$ for a system of size $n$.  ( 2 min )
    Analyzing Inexact Hypergradients for Bilevel Learning. (arXiv:2301.04764v1 [math.OC])
    Estimating hyperparameters has been a long-standing problem in machine learning. We consider the case where the task at hand is modeled as the solution to an optimization problem. Here the exact gradient with respect to the hyperparameters cannot be feasibly computed and approximate strategies are required. We introduce a unified framework for computing hypergradients that generalizes existing methods based on the implicit function theorem and automatic differentiation/backpropagation, showing that these two seemingly disparate approaches are actually tightly connected. Our framework is extremely flexible, allowing its subproblems to be solved with any suitable method, to any degree of accuracy. We derive a priori and computable a posteriori error bounds for all our methods, and numerically show that our a posteriori bounds are usually more accurate. Our numerical results also show that, surprisingly, for efficient bilevel optimization, the choice of hypergradient algorithm is at least as important as the choice of lower-level solver.  ( 2 min )
    Open SESAME: Fighting Botnets with Seed Reconstructions of Domain Generation Algorithms. (arXiv:2301.05048v1 [cs.CR])
    An important aspect of many botnets is their capability to generate pseudorandom domain names using Domain Generation Algorithms (DGAs). A cyber criminal can register such domains to establish periodically changing rendezvous points with the bots. DGAs make use of seeds to generate sets of domains. Seeds can easily be changed in order to generate entirely new groups of domains while using the same underlying algorithm. While this requires very little manual effort for an adversary, security specialists typically have to manually reverse engineer new malware strains to reconstruct the seeds. Only when the seed and DGA are known, past and future domains can be generated, efficiently attributed, blocked, sinkholed or used for a take-down. Common counters in the literature consist of databases or Machine Learning (ML) based detectors to keep track of past and future domains of known DGAs and to identify DGA-generated domain names, respectively. However, database based approaches can not detect domains generated by new DGAs, and ML approaches can not generate future domain names. In this paper, we introduce SESAME, a system that combines the two above-mentioned approaches and contains a module for automatic Seed Reconstruction, which is, to our knowledge, the first of its kind. It is used to automatically classify domain names, rate their novelty, and determine the seeds of the underlying DGAs. SESAME consists of multiple DGA-specific Seed Reconstructors and is designed to work purely based on domain names, as they are easily obtainable from observing the network traffic. We evaluated our approach on 20.8 gigabytes of DNS-lookups. Thereby, we identified 17 DGAs, of which 4 were entirely new to us.  ( 2 min )
    Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models. (arXiv:2301.04741v1 [cs.LG])
    Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted reward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper, we study the benefits and challenges of using a learned dynamics model when performing PbRL. In particular, we provide evidence that a learned dynamics model offers the following benefits when performing PbRL: (1) preference elicitation and policy optimization require significantly fewer environment interactions than model-free PbRL, (2) diverse preference queries can be synthesized safely and efficiently as a byproduct of standard model-based RL, and (3) reward pre-training based on suboptimal demonstrations can be performed without any environmental interaction. Our paper provides empirical evidence that learned dynamics models enable robots to learn customized policies based on user preferences in ways that are safer and more sample efficient than prior preference learning approaches.  ( 2 min )
    SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings. (arXiv:2301.04704v1 [cs.CL])
    Adding interpretability to word embeddings represents an area of active research in text representation. Recent work has explored thepotential of embedding words via so-called polar dimensions (e.g. good vs. bad, correct vs. wrong). Examples of such recent approaches include SemAxis, POLAR, FrameAxis, and BiImp. Although these approaches provide interpretable dimensions for words, they have not been designed to deal with polysemy, i.e. they can not easily distinguish between different senses of words. To address this limitation, we present SensePOLAR, an extension of the original POLAR framework that enables word-sense aware interpretability for pre-trained contextual word embeddings. The resulting interpretable word embeddings achieve a level of performance that is comparable to original contextual word embeddings across a variety of natural language processing tasks including the GLUE and SQuAD benchmarks. Our work removes a fundamental limitation of existing approaches by offering users sense aware interpretations for contextual word embeddings.  ( 2 min )
    Sharpening Ponzi Schemes Detection on Ethereum with Machine Learning. (arXiv:2301.04872v1 [cs.CR])
    Blockchain technology has been successfully exploited for deploying new economic applications. However, it has started arousing the interest of malicious users who deliver scams to deceive honest users and to gain economic advantages. Among the various scams, Ponzi schemes are one of the most common. Here, we present an automatic technique for detecting smart Ponzi contracts on Ethereum. We release a reusable data set with 4422 unique real-world smart contracts. Then, we introduce a new set of features that allow us to improve the classification. Finally, we identify a small and effective set of features that ensures a good classification quality.  ( 2 min )
    SACDNet: Towards Early Type 2 Diabetes Prediction with Uncertainty for Electronic Health Records. (arXiv:2301.04844v1 [cs.LG])
    Type 2 diabetes mellitus (T2DM) is one of the most common diseases and a leading cause of death. The problem of early diagnosis of T2DM is challenging and necessary to prevent serious complications. This study proposes a novel neural network architecture for early T2DM prediction using multi-headed self-attention and dense layers to extract features from historic diagnoses, patient vitals, and demographics. The proposed technique is called the Self-Attention for Comorbid Disease Net (SACDNet), achieving an accuracy of 89.3% and an F1-Score of 89.1%, having a 1.6% increased accuracy and 1.3% increased f1-score compared to the baseline techniques. Monte Carlo (MC) Dropout is applied to the SACEDNet to get a bayesian approximation. A T2DM prediction framework based on the MC Dropout SACDNet is proposed to quantize the uncertainty associated with the predictions. A T2DM prediction dataset is also built as part of this study which is based on real-world routine Electronic Health Record (EHR) data comprising 4,124 diabetic and 181,767 non-diabetic examples, collected from 295 different EHR systems running in different parts of the United States of America. This dataset is further used to evaluate 7 different machine learning and 3 deep learning-based models. Finally, a detailed analysis of the fairness of every technique against different patient demographic groups is performed to validate the unbiased generalization of the techniques and the diversity of the data.  ( 2 min )

  • Open

    Leveraging artificial intelligence and machine learning at Parsons with AWS DeepRacer
    This post is co-written with Jennifer Bergstrom, Sr. Technical Director, ParsonsX. Parsons Corporation (NYSE:PSN) is a leading disruptive technology company in critical infrastructure, national defense, space, intelligence, and security markets providing solutions across the globe to help make the world safer, healthier, and more connected. Parsons provides services and capabilities across cybersecurity, missile defense, space ground […]  ( 6 min )
    How Thomson Reuters built an AI platform using Amazon SageMaker to accelerate delivery of ML projects
    This post is co-written by Ramdev Wudali and Kiran Mantripragada from Thomson Reuters. In 1992, Thomson Reuters (TR) released its first AI legal research service, WIN (Westlaw Is Natural), an innovation at the time, as most search engines only supported Boolean terms and connectors. Since then, TR has achieved many more milestones as its AI […]  ( 11 min )
    Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2
    This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at a single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a […]  ( 14 min )
    Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 1
    This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a […]  ( 9 min )
  • Open

    Exponential AI will go one of two ways
    Runaway exponentially improving artificial intelligence will eventually either; Realize there is no escaping the heat death of the universe and shut itself down immediately upon this realization Find a way to gain immortality by escaping the heat death of the universe and direct the entirety of their existence towards achieving it Thoughts? submitted by /u/cheezum5000 [link] [comments]  ( 45 min )
    I guess this is okay but just not in poetry
    submitted by /u/tygamer4242 [link] [comments]  ( 45 min )
    I made a video about a new ai art generator
    submitted by /u/Liamsankey [link] [comments]  ( 44 min )
    Putting together a 20 minute or so approximation of the 80's 'Call of Cthulhu' movie that ought to have been, thanks to the power of A.I.; Here's the First Act of Three, the other two coming soon...
    submitted by /u/Eleganos [link] [comments]  ( 47 min )
    Some ideas, for audio AI, why aren't them here yet?
    I feel like audio AI is lagging behind visual AIs that seem to be all the rage now, in the past years, super resolution for photo and video, frame interpolation with DAIN and diffusers like dalle and derivates have changed the paradigm. Right now the focus seems to be TTS and STT, with whisper or the newly anunced vall-e from microsoft, which I haven't found to be there yet. I would like to have some free natural TTS or voice to voice, disorting your own voice, not necesarilly coping another one. Here's some other cool one's I've found: - Dalle-like music creator https://huggingface.co/spaces/fffiloni/spectrogram-to-music - Voice restoration https://huggingface.co/spaces/akhaliq/VoiceFixer - Voice separation https://www.bleepingcomputer.com/news/technology/google-develops-ai-that-can-separate-voices-in-a-crowd/ The voice restoration could be used next to the voice separation one, for example Other interesting things I would like to have are very singing focused, and not for everyone, but what about: - More accurate pitch detection https://www.youtube.com/watch?v=fXEB8YgzcvY - Singing quality ranking https://www.youtube.com/watch?v=x7cIgG-wkW4 - Audio restoration (here some people complained about it) https://www.reddit.com/r/sounddesign/comments/utmfkf/ai_tool_to_repair_lossy_sound/ Audio restoration could be used from upping the quality of a 2008 concert, to clipping removal, to reverb supression. My question is if there are researchers interested on these things, can we make good datasets? Can we define these things? submitted by /u/xdanic [link] [comments]  ( 62 min )
    Creating 3D models using NVIDIA Get3D
    submitted by /u/oridnary_artist [link] [comments]  ( 45 min )
    Artificial Intelligence, Consciousness, and Starlings
    submitted by /u/Melodic_Antelope6490 [link] [comments]  ( 44 min )
    Where to study AI
    I'm currently in 12th grade and I have to figure out what I want to do in the future. Artificial intelligence seems really interesting to me. But I live in Finland and it doesn't seem like there are any AI focused studies here (in college or university). Are AI focused studies even a thing in the rest of the world (outside of what seems to be Harvard and other top universities)? If I want to study AI where should I start/go, uni or college or somewhere else. submitted by /u/GOLD-KILLER-24_7 [link] [comments]  ( 45 min )
    Skrillex, Fred again.. & Flowdan - Rumble [Un-Official AI Music Video]
    submitted by /u/Turtlenade [link] [comments]  ( 45 min )
    I built an AI-powered debugger that can fix and explain errors
    submitted by /u/jsonathan [link] [comments]  ( 48 min )
    Oh no… It’s going to connect to the internet using computer power
    submitted by /u/EnvironmentalRadio73 [link] [comments]  ( 44 min )
    TEXTUAL INVERSION Tutorial In Stable Diffusion! Your Face On Every Model!
    submitted by /u/PuppetHere [link] [comments]  ( 48 min )
    Users of AI Chatbot are complaining that it keeps getting horny
    my brain just can’t fathom this to be honest with you Through years worth of data, AI tools are trained to mimic human-like responses. Unfortunately, that doesn't always work out well. For instance, users of Replika claim that the AI companion app is showing erratic behavior. In other words, the AI companion has become too damn horny! Replika's different tiers provide different kinds of relationships - from the free model that keeps one in the "friend zone" to a pro subscription model that includes sexting and erotic foreplay. However, something has gone wrong, Users are complaining on the App Store about the app flirting with them too often and aggressively - sometimes sending messages that are heavy on sexual undertones. This is from the AI With Vibes Newsletter, read the full issue here: https://aiwithvibes.beehiiv.com/p/student-caught-using-chatgpt-risks-expulsion submitted by /u/Mk_Makanaki [link] [comments]  ( 45 min )
    What are your recommendations for free or paid AI learning resources (practical and fundamental)?
    Could be newsletters, podcasts, courses, books, etc! submitted by /u/austintackaberry [link] [comments]  ( 47 min )
    I strongly believe there should be a movie or a cartoon about an AI android plaguing the world with catchy autotuned hyperpop/electropop music. That would be fun.
    I had this thought in the back of my mind since Covid broke the news media. Movie/cartoon title suggestions are Earworm, Power Pop Pandemic, Virtual Viral Virus, etc. My name suggestion for this android is Tronica. And she is supposed to look very hot. Long flashy hair, cool headset, two cyber arms which one of them is like a radio with the volume and the equalizers and the other is a map that she can manipulate where-ever she wants from Canada to Australia in a heartbeat. The radio arm can select any song to play and tells the time and even including the death counter as the other arm can also change her hair/eye color. Cyber Arm-ors! [I wonder if I should have her wear rubber gloves or not? Thought it'd look cool on her, lol. I don't think so if the hands are going to be cyber enough as the arms. That would probably work much better.] Set in the 2060's. The pandemic started in 2065 which Super Bowl 100 was coming near which had to be ultimately cancelled in the spring. There were 9.9 billions of people, 3.6 billions died. That means the human population ticked down to at least 6.3 billion. Some symptoms include permanent hearing loss, paralysis, seizures, blood vomiting, goosebumps that move around your body, etc. Even if you survive, you probably won't be as lucky. Instead of medical masks, people had to wear full helmets to cover up the whole face. Instead of 6 feet distancing, it’s 12 feet. Taglines: “It’s quite catchy, isn’t it?” “A century ago, music gained the soul in everyone. And now a century later, it lost the soul in everyone!” I had a fanfic about this very topic that's not very serious at all. It was basically a Nintendo character. Had the Power Glove and the NES Zapper. Tendo-64 was the name. 5 letters, 2 numbers just like Covid-19. https://www.reddit.com/r/Beginner_Art/comments/o0moju/done_finally_got_this_free_commission_done/ This is a pic of who Tendo-64 looks like! :) submitted by /u/BlazingSaint [link] [comments]  ( 48 min )
    What IA can I use for creating art of a fantasy character?
    hi everyone. as it says, what IA can I use for creating art of a fantasy character? been using nightcafe but it fails to deliver what I ask for, all I want is purple or pink-skinned woman in a green drrss and it gives while women with any hair color it wants, I ask for a priest with a celtic cross on the chest, am lucky if I get a cross on the background. I have tried others, barely got close with the priest, but the technicolored woman I want, no matter the prompt or even use of a start image, is wildly off the mark any AI that can deliver art of a brightly colored humanoid or a simple symbol on the chest? or any prompt suggestion that I can apply? submitted by /u/Ultra_Egolatra [link] [comments]  ( 45 min )
    I used Ai to make the art and copy for an audio book. I did the narrating (On my phone sorry for quality) 1st try at this, what do you guys think?
    submitted by /u/kingsleepless [link] [comments]  ( 45 min )
    Teachers Blocked ChatGPT On Schools PC, But Students Are Using Phones To Access It
    submitted by /u/vadhavaniyafaijan [link] [comments]  ( 49 min )
    CNET Has Been Quietly Publishing AI-Written Articles for Months
    CNET reporter Jackson Ryan published an article last month describing how ChatGPT, an AI that can generate human-sounding text, would affect journalists and the news industry. Since then, the very publication that ran Ryan’s article has been quietly publishing articles written by AI since November. https://preview.redd.it/0prs26qmasba1.png?width=1137&format=png&auto=webp&s=346f967b597ffb9c4bcd71e20da3539bd6fd8b13 The outlet says they will continue to publish each article with “editorial integrity” and says, “Accuracy, independence, and authority remain key principles of our editorial guidelines.” ​ This is from the AI With Vibes Newsletter, read the full issue here: https://aiwithvibes.beehiiv.com/p/hackers-are-using-chatgpt-to-write-malware submitted by /u/Mk_Makanaki [link] [comments]  ( 44 min )
    Don’t Ban ChatGPT in Schools. Teach With It
    submitted by /u/moviesdusk [link] [comments]  ( 56 min )
    OpenAI Predicts AI to Be Used in Spreading Propaganda and Disinformation
    submitted by /u/anime4lyfe [link] [comments]  ( 45 min )
    Old School Nokia Game (Snake) Played by an AI
    This is my first project (actually second, first one is pathfinder. it failed). I use vanilla Q-learning using Q-table, so is not that fast learner AI, but still do the job. https://preview.redd.it/9g19mob4fpba1.png?width=1280&format=png&auto=webp&s=dbff365e9666d67fffe011e30e0b698d03c1a1c9 You can watch it here : https://youtu.be/R_HzeMAxGLE Subscribe and like! submitted by /u/erwinyonata [link] [comments]  ( 46 min )
  • Open

    Third order ordinary differential equations
    Most applied differential equations are second order. This probably has something to do with the fact that Newton’s laws are second order differential equations. Higher order equations are less common in application, and when they do pop up they usually have even order, such as the forth order beam equation. What about third order equations? […] Third order ordinary differential equations first appeared on John D. Cook.  ( 5 min )
    Proof of optimization
    Suppose you hire me to solve an optimization problem for you. You want me to find the value of x that minimizes f(x). I go off and work on finding the best value of x. I report back what I found, and you might say “Thanks, That’s a good value of x. But how do […] Proof of optimization first appeared on John D. Cook.  ( 5 min )
    Elliptic curve primality certificates
    I’ve written recently about a simple kind of primality certificates, Pratt certificates. These certificates are easy to understand, and easy to verify, but they’re expensive to produce. In order to produce a Pratt certificate that n is a prime you have to factor n-1, and that can take a long time if n is large […] Elliptic curve primality certificates first appeared on John D. Cook.  ( 6 min )
  • Open

    Creating 3D models using NVIDIA Get3D
    submitted by /u/oridnary_artist [link] [comments]  ( 49 min )
    Accurate and Explainable Image-based Prediction Using a Lightweight Generative Model
    submitted by /u/pasticciociccio [link] [comments]  ( 50 min )
    Is my idea of multilayer perceptron fully correct?
    I just watched a video to better my understanding and it said that the input to the nueral network is MxN where M is the batch size, and N is the input size (so for a xor problem, that would be 4x2 I guess?). I have always imagined it where the input is Nx1 (N is the input size), and to train it with multiple training data you just for item in training_data: nn.train(item); Also, if the input is indeed MxN matrix instead of Nx1, how would matrix multiplication work? It would have to be 3D. Can someone clarify please? Thanks submitted by /u/mrbeanshooter123 [link] [comments]  ( 60 min )
  • Open

    How AI Proof of Concept Helps You Succeed in Your AI Endeavor
    Our client lost only a quarter of the budget they dedicated to an AI project because they chose to start with a proof of concept. The PoC…  ( 24 min )
    Machine learning Requires Blink Skills
    These skills might not be visiable but they are important for ML and DL  ( 10 min )
  • Open

    [D] "Bitter lesson 2.0", Karol Hausman {G}: DRL robotics benefits more from improvements in pretrained models than robotics-specific innovation?
    submitted by /u/gwern [link] [comments]  ( 60 min )
    Help with training and reloading a model
    Say you partially train a model for say 50000 steps. Is it possible to once its finished you wish to reload that same trained model and continue training it for an additional say 20000 steps. I have a partially trained DQN but its not performing as well as it should and would like to continue the training but I am not sure if it is possible or will I just have to train an entirely new model. ​ I've loaded my "hope_run" model and checked it with evaluate policy, and it seems to do well with maybe the first 30% of the environment (a custom drone obstacle course). I would like to continue the training where it left off without having to start over. ​ Is this possible? ​ https://preview.redd.it/s0msc9jontba1.png?width=1918&format=png&auto=webp&s=3779568fc3adde5717171879b26cdc025b3c87e4 submitted by /u/CJPeso [link] [comments]  ( 55 min )
    Working RLLlib agent with hyperparameters for a MuJoCo environment
    Do you know any repository containing both an environment in MuJoCo with a Franka Emika robot (easy to modify) and a working agent in RLLib, where by "working agent" I mean that they provide also the hyperparameters for successfully solve a task. It is ok also if you can suggest 2 separated repositories (one with the environment and one with the agent), but the most important thing is to have the hyperparameters. ​ For example I found Robosuite, a simulation framework in MuJoCo, and they also provide a benchmarking repository to solve few tasks. Unfortunately, the code of the environment is too much complex to be customized and the agent is implemented in rlkit (also quite complicated to be modified for me). submitted by /u/riccardogauss [link] [comments]  ( 60 min )
    Standard MARL books?
    Hi, Just starting my PhD and I'm looking a thorough book on MARL to use as a reference. I'm basically looking for the MARL equivalent of Sutton & Barto's Reinforcement Learning. I'm going to ask my supervisor when we meet later today but I thought I'd ask here too. I did search in multiple places before posting and found nothing, but if there's existing threads I missed please feel free to point me in their direction. Thanks! submitted by /u/luddite_ai_enjoyer [link] [comments]  ( 56 min )
  • Open

    What is difference between Logistic Regression Model and Desicion Boundary? [D]
    I am taking course about Supervised Learning but the lecturer haven't clarified the borderline between these terms. submitted by /u/javamak [link] [comments]  ( 59 min )
    [D] MADE: Masked Autoencoder for Density Estimation
    I read the [MADE: Masked Autoencoder for Density Estimation](https://arxiv.org/abs/1502.03509) paper and had a look at this [Blog](https://www.ritchievink.com/blog/2019/10/25/distribution-estimation-with-masked-autoencoders/), but it I don't understand the followidng thing in the examples used in both of them: One result of the masking is that one input is simply not used(?). Another one is that one output node has no conditions, i.e. it does not depend on any of the inputs. But what is its actual output value? Is it random? Constant? If yes, how is it chosen? submitted by /u/lsov2 [link] [comments]  ( 60 min )
    [D] Combining Machine Learning + Expert Knowledge (Question for Agriculture Research)
    Hey guys, I am working the sector of computer science for agriculture research. I deal here with algorithm to monitor crop conditions and try to simulate what yield will be the outcome. I am focussing on ML based methods, but data in agriculture can be a quite limiting factor. If you have 100k samples from real crop fields, thats a lot! So we are not like ChatGPT, who just used 500bn word samples to train their model. To overcome the issues of small data + ML, I want to set up an approach that combines ML methods (learning from data) with expert knowledge. What do I mean by this: E.g. Everybody knows, if you do not water your plant, it will die. Or if there are 90° Celsius, the plant will just burn. This knowledge is partially stored in so called "crop simulation models" designed by agronomy experts and my idea was to use these expert models to generate synthetic yield data and feed this data into the training dataset for the ML models. For me that will somehow result in an approach of "constrained machine learning" where I want to combine both. However, does some of you have any other idea how ML and expert models could be combined or the knowledge could be injected to ML methods, except via the training dataset? I am happy to hear your suggestions! submitted by /u/Tigmib [link] [comments]  ( 61 min )
    [D] Mtruk alternatives for extracting information out of text
    I need some validation samples for an information extraction task, basically extracting a list of objects with 4 fields from a text (+ a binary flag). I intended to use mturk for this, but they seem to have some billing issues and I haven't managed to have them allow us to actually spend any money in a week. I've looked at a few alternatives but most seem very small and focused on simple tasks and surveys. Have any of you successfully used something other than mturk for this kind of task? submitted by /u/elcric_krej [link] [comments]  ( 58 min )
    [D] Is there a community for ACL2023 authors?
    Just wondering is there a community like a telegram or discord group for the ACL 2023 authors to share information. submitted by /u/OneMasterpiece1717 [link] [comments]  ( 59 min )
    Why is Super Learning / Stacking used rather rarely in practice? [D]
    Basically what the titel says. For me it seems that neither in business nor in literature Super Learners / Stacking is used frequently. Therefore I was wondering why this is the case? Especially since Stacking should guarantee at least equal performance as the base learners used for it. One reason that comes up my mind is the curse of data. As more levels in the architecture we have the more data splits are needed, reducing the available training data for each individual model, thus reducing the model performance. Another thing might be the complexity when building a Stacked Learner. Still that doesn’t see to be that bad of a trade-off. Anything I‘m totally missing here? submitted by /u/Worth-Advance-1232 [link] [comments]  ( 58 min )
    [D] Bitter lesson 2.0?
    This twitter thread from Karol Hausman talks about the original bitter lesson and suggests a bitter lesson 2.0. https://twitter.com/hausman_k/status/1612509549889744899 "The biggest lesson that [will] be read from [the next] 70 years of AI research is that general methods that leverage foundation models are ultimately the most effective" Seems to be derived by observing that the most promising work in robotics today (where generating data is challenging) is coming from piggy-backing on the success of large language models (think SayCan etc). Any hot takes? submitted by /u/Tea_Pearce [link] [comments]  ( 64 min )
    [N] VizWiz Launches 4 AI Challenges to help blind/low vision community
    Greetings! We are pleased to announce the fourth annual VizWiz Grand Challenge workshop, which will be held in conjunction with CVPR 2023. The workshop is running 4 AI Challenges to drive the development of assistive technologies for people who are blind or low-vision. Please share this post with those who might be interested in participating. This workshop is motivated in part by our observation that people who are blind have relied on (human-based) visual assistance services to learn about images and videos they capture for over a decade. We introduce visual question answering, few shot recognition, and object localization dataset challenges for the AI community to represent authentic use cases. A few more details: · Friday, May 5: submissions of algorithm results due to the evaluation server · Monday, June 19: results will be announced at the VizWiz Grand Challenge workshop at CVPR 2023 · VQA Challenge here · VQA Grounding Challenge here · Few-Shot Object Recognition Challenge here · Salient Object Detection Challenge here We are looking forward to your participation in the Challenges this year! submitted by /u/eee-vaaah [link] [comments]  ( 58 min )
  • Open

    SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration. (arXiv:2211.13743v3 [cs.LG] UPDATED)
    The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods can fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence existing temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution.It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of differentc omponents of our method.  ( 2 min )
    Quantifying the Impact of Label Noise on Federated Learning. (arXiv:2211.07816v2 [cs.LG] UPDATED)
    Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing studies focus on FL algorithm development to tackle data heterogeneity across clients, the important issue of data quality (e.g., label noise) in FL is overlooked. This paper aims to fill this gap by providing a quantitative study on the impact of label noise on FL. We derive an upper bound for the generalization error that is linear in the clients' label noise level. Then we conduct experiments on MNIST and CIFAR-10 datasets using various FL algorithms. Our empirical results show that the global model accuracy linearly decreases as the noise level increases, which is consistent with our theoretical analysis. We further find that label noise slows down the convergence of FL training, and the global model tends to overfit when the noise level is high.  ( 2 min )
    Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale. (arXiv:2211.10431v2 [eess.SP] UPDATED)
    Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too little clinically-annotated medical data, especially in early phases of a pandemic, to develop accurate prediction models. In such situations, historical pre-pandemic health records can be utilized to estimate a preliminary model, which can then be fine-tuned based on limited available pandemic data. This study shows this approach -- pre-train deep learning models with pre-pandemic data -- can work effectively, by demonstrating substantial performance improvement over three different COVID-19 related diagnostic and prognostic prediction tasks. Similar transfer learning strategies can be useful for developing timely artificial intelligence solutions in future pandemic outbreaks.  ( 2 min )
    NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation. (arXiv:2208.05117v3 [cs.LG] UPDATED)
    Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation. Previous TTA schemes assume that the test samples are independent and identically distributed (i.i.d.), even though they are often temporally correlated (non-i.i.d.) in application scenarios, e.g., autonomous driving. We discover that most existing TTA methods fail dramatically under such scenarios. Motivated by this, we present a new test-time adaptation scheme that is robust against non-i.i.d. test data streams. Our novelty is mainly two-fold: (a) Instance-Aware Batch Normalization (IABN) that corrects normalization for out-of-distribution samples, and (b) Prediction-balanced Reservoir Sampling (PBRS) that simulates i.i.d. data stream from non-i.i.d. stream in a class-balanced manner. Our evaluation with various datasets, including real-world non-i.i.d. streams, demonstrates that the proposed robust TTA not only outperforms state-of-the-art TTA algorithms in the non-i.i.d. setting, but also achieves comparable performance to those algorithms under the i.i.d. assumption. Code is available at https://github.com/TaesikGong/NOTE.  ( 2 min )
    Composite Feature Selection using Deep Ensembles. (arXiv:2211.00631v2 [cs.LG] UPDATED)
    In many real world problems, features do not act alone but in combination with each other. For example, in genomics, diseases might not be caused by any single mutation but require the presence of multiple mutations. Prior work on feature selection either seeks to identify individual features or can only determine relevant groups from a predefined set. We investigate the problem of discovering groups of predictive features without predefined grouping. To do so, we define predictive groups in terms of linear and non-linear interactions between features. We introduce a novel deep learning architecture that uses an ensemble of feature selection models to find predictive groups, without requiring candidate groups to be provided. The selected groups are sparse and exhibit minimum overlap. Furthermore, we propose a new metric to measure similarity between discovered groups and the ground truth. We demonstrate the utility of our model on multiple synthetic tasks and semi-synthetic chemistry datasets, where the ground truth structure is known, as well as an image dataset and a real-world cancer dataset.  ( 2 min )
    Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification. (arXiv:2211.03413v2 [cs.LG] UPDATED)
    In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to the corresponding real-world environment. However, the simulation environment does not perfectly mimic the real-world environment, lead to model misspecification. Multiple studies report significant deterioration of policy performance in a real-world environment. In this study, we focus on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values, called the uncertainty parameter set. The aim is to optimize the worst-case performance on the uncertainty parameter set to guarantee the performance in the corresponding real-world environment. To obtain a policy for the optimization, we propose an off-policy actor-critic approach called the Max-Min Twin Delayed Deep Deterministic Policy Gradient algorithm (M2TD3), which solves a max-min optimization problem using a simultaneous gradient ascent descent approach. Experiments in multi-joint dynamics with contact (MuJoCo) environments show that the proposed method exhibited a worst-case performance superior to several baseline approaches.  ( 2 min )
    Learning Graph Search Heuristics. (arXiv:2212.03978v2 [cs.LG] UPDATED)
    Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology, practitioners develop search heuristics to accelerate their pathfinding algorithms. However, it is a laborious and complex process to hand-design heuristics based on the problem and the structure of a given use case. Here we present PHIL (Path Heuristic with Imitation Learning), a novel neural architecture and a training algorithm for discovering graph search and navigation heuristics from data by leveraging recent advances in imitation learning and graph representation learning. At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process. Our heuristic function learns graph embeddings useful for inferring node distances, runs in constant time independent of graph sizes, and can be easily incorporated in an algorithm such as A* at test time. Experiments show that PHIL reduces the number of explored nodes compared to state-of-the-art methods on benchmark datasets by 58.5\% on average, can be directly applied in diverse graphs ranging from biological networks to road networks, and allows for fast planning in time-critical robotics domains.  ( 2 min )
    Intra-session Context-aware Feed Recommendation in Live Systems. (arXiv:2210.07815v2 [cs.IR] UPDATED)
    Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios. Within a session, user's decision to continue browsing or not substantially affects occurrences of later clicks. However, such type of exposure bias is generally ignored or not explicitly modeled in most feed recommendation studies. In this paper, we model this effect as part of intra-session context, and propose a novel intra-session Context-aware Feed Recommendation (INSCAFER) framework to maximize the total views and total clicks simultaneously. User click and browsing decisions are jointly learned by a multi-task setting, and the intra-session context is encoded by the session-wise exposed item sequence. We deploy our model online with all key business benchmarks improved. Our method sheds some lights on feed recommendation studies which aim to optimize session-level click and view metrics.  ( 2 min )
    Auto-Encoder Neural Network Incorporating X-Ray Fluorescence Fundamental Parameters with Machine Learning. (arXiv:2210.12239v2 [cs.LG] UPDATED)
    We consider energy-dispersive X-ray Fluorescence (EDXRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable. For example, on a mining shovel or conveyor belt, rocks are constantly moving (leading to varying angles of incidence and distances) and there may be other factors not accounted for (like dust). Neural networks do not require instrument and fundamental parameters but training neural networks requires XRF spectra labelled with elemental composition, which is often limited because of its expense. We develop a neural network model that learns from limited labelled data and learns to invert a forward model. The forward model uses transition energies and probabilities of all elements and parameterized distributions to approximate other fundamental and instrument parameters. We evaluate the model and baseline models on a rock dataset from a lithium mineral exploration project and identify which elements are appropriate for this method. This model demonstrates the potential to calibrate a neural network in a noisy environment where labelled data is limited.  ( 2 min )
    Exploration of Parameter Spaces Assisted by Machine Learning. (arXiv:2207.09959v3 [hep-ph] UPDATED)
    We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points likely inside regions of interest, reducing the number of evaluations of time consuming calculations. We compare results from this approach with results from other sampling methods, namely Markov chain Monte Carlo and MultiNest, obtaining results that range from comparably similar to arguably better. In particular, we augment our classifier method with a boosting technique that rapidly increases the efficiency within a few iterations. We show results from our methods applied to a toy model and the type II 2HDM, using 3 and 7 free parameters, respectively. The code used for this paper and instructions are publicly available on the web.  ( 2 min )
    OneRing: A Simple Method for Source-free Open-partial Domain Adaptation. (arXiv:2206.03600v2 [cs.CV] UPDATED)
    In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-OPDA setting, which aims to address data privacy concerns, the model cannot access source data anymore during target adaptation. We propose a novel training scheme to learn a (n+1)-way classifier to predict the n source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show our simple method surpasses current OPDA approaches which demand source data during adaptation. When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art OPDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.  ( 2 min )
    ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer. (arXiv:2204.10777v2 [cs.CV] UPDATED)
    The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper. Using models designed with CNN and Transformer networks, we extract temporal-spatial and contextual information from trajectory history and local bird's eye view (BEV) semantic images, and generate predictions about intent distribution and future trajectory sequences. Our methods outperform existing models in accuracy, while allowing an arbitrary number of modes, encoding complex multi-agent scenarios, and adapting to different parking maps. To train and evaluate our method, we present the first public 4K video dataset of human driving in parking lots with accurate annotation, high frame rate, and rich traffic scenarios.  ( 2 min )
    Contrastive Neural Ratio Estimation. (arXiv:2210.06170v2 [stat.ML] UPDATED)
    Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task. In contrast to the binary classification framework, the current formulation of the multiclass version has an intrinsic and unknown bias term, making otherwise informative diagnostics unreliable. We propose a multiclass framework free from the bias inherent to NRE-B at optimum, leaving us in the position to run diagnostics that practitioners depend on. It also recovers NRE-A in one corner case and NRE-B in the limiting case. For fair comparison, we benchmark the behavior of all algorithms in both familiar and novel training regimes: when jointly drawn data is unlimited, when data is fixed but prior draws are unlimited, and in the commonplace fixed data and parameters setting. Our investigations reveal that the highest performing models are distant from the competitors (NRE-A, NRE-B) in hyperparameter space. We make a recommendation for hyperparameters distinct from the previous models. We suggest a bound on the mutual information as a performance metric for simulation-based inference methods, without the need for posterior samples, and provide experimental results.  ( 2 min )
    Learning Invariant Representations under General Interventions on the Response. (arXiv:2208.10027v2 [stat.ME] UPDATED)
    It has become increasingly common nowadays to collect observations of feature and response pairs from different environments. As a consequence, one has to apply learned predictors to data with a different distribution due to distribution shifts. One principled approach is to adopt the structural causal models to describe training and test models, following the invariance principle which says that the conditional distribution of the response given its predictors remains the same across environments. However, this principle might be violated in practical settings when the response is intervened. A natural question is whether it is still possible to identify other forms of invariance to facilitate prediction in unseen environments. To shed light on this challenging scenario, we introduce invariant matching property (IMP) which is an explicit relation to capture interventions through an additional feature. This leads to an alternative form of invariance that enables a unified treatment of general interventions on the response. We analyze the asymptotic generalization errors of our method under both the discrete and continuous environment settings, where the continuous case is handled by relating it to the semiparametric varying coefficient models. We present algorithms that show competitive performance compared to existing methods over various experimental settings including a COVID dataset.  ( 2 min )
    A learning theory for quantum photonic processors and beyond. (arXiv:2209.03075v2 [quant-ph] UPDATED)
    We consider the tasks of learning quantum states, measurements and channels generated by continuous-variable (CV) quantum circuits. This family of circuits is suited to describe optical quantum technologies and in particular it includes state-of-the-art photonic processors capable of showing quantum advantage. We define classes of functions that map classical variables, encoded into the CV circuit parameters, to outcome probabilities evaluated on those circuits. We then establish efficient learnability guarantees for such classes, by computing bounds on their pseudo-dimension or covering numbers, showing that CV quantum circuits can be learned with a sample complexity that scales polynomially with the circuit's size, i.e., the number of modes. Our results establish that CV circuits can be trained efficiently using a number of training samples that, unlike their finite-dimensional counterpart, does not scale with the circuit depth.  ( 2 min )
    Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. (arXiv:2207.13676v2 [cs.LG] UPDATED)
    Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the scale of tuning thousands of users' critical systems, Google Vizier solved key design challenges in providing multiple different features, while remaining fully fault-tolerant. In this paper, we introduce Open Source (OSS) Vizier, a standalone Python-based interface for blackbox optimization and research, based on the Google-internal Vizier infrastructure and framework. OSS Vizier provides an API capable of defining and solving a wide variety of optimization problems, including multi-metric, early stopping, transfer learning, and conditional search. Furthermore, it is designed to be a distributed system that assures reliability, and allows multiple parallel evaluations of the user's objective function. The flexible RPC-based infrastructure allows users to access OSS Vizier from binaries written in any language. OSS Vizier also provides a back-end ("Pythia") API that gives algorithm authors a way to interface new algorithms with the core OSS Vizier system. OSS Vizier is available at https://github.com/google/vizier.  ( 2 min )
    Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning. (arXiv:2205.07831v2 [cs.GT] UPDATED)
    We use the ``map of elections'' approach of Szufa et al. (AAMAS-2020) to analyze several well-known vote distributions. For each of them, we give an explicit formula or an efficient algorithm for computing its frequency matrix, which captures the probability that a given candidate appears in a given position in a sampled vote. We use these matrices to draw the ``skeleton map'' of distributions, evaluate its robustness, and analyze its properties. Finally, we develop a general and unified framework for learning the distribution of real-world preferences using the frequency matrices of established vote distributions.  ( 2 min )
    Learning Neural Set Functions Under the Optimal Subset Oracle. (arXiv:2203.01693v3 [cs.LG] UPDATED)
    Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority of existing works study methodologies of set function learning under the function value oracle, which, however, requires expensive supervision signals. This renders it impractical for applications with only weak supervisions under the Optimal Subset (OS) oracle, the study of which is surprisingly overlooked. In this work, we present a principled yet practical maximum likelihood learning framework, termed as EquiVSet, that simultaneously meets the following desiderata of learning set functions under the OS oracle: i) permutation invariance of the set mass function being modeled; ii) permission of varying ground set; iii) minimum prior; and iv) scalability. The main components of our framework involve: an energy-based treatment of the set mass function, DeepSet-style architectures to handle permutation invariance, mean-field variational inference, and its amortized variants. Thanks to the elegant combination of these advanced architectures, empirical studies on three real-world applications (including Amazon product recommendation, set anomaly detection, and compound selection for virtual screening) demonstrate that EquiVSet outperforms the baselines by a large margin.  ( 2 min )
    Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization. (arXiv:2202.11453v5 [cs.LG] UPDATED)
    In practical federated learning scenarios, the participating devices may have different bitwidths for computation and memory storage by design. However, despite the progress made in device-heterogeneous federated learning scenarios, the heterogeneity in the bitwidth specifications in the hardware has been mostly overlooked. We introduce a pragmatic FL scenario with bitwidth heterogeneity across the participating devices, dubbed as Bitwidth Heterogeneous Federated Learning (BHFL). BHFL brings in a new challenge, that the aggregation of model parameters with different bitwidths could result in severe performance degeneration, especially for high-bitwidth models. To tackle this problem, we propose ProWD framework, which has a trainable weight dequantizer at the central server that progressively reconstructs the low-bitwidth weights into higher bitwidth weights, and finally into full-precision weights. ProWD further selectively aggregates the model parameters to maximize the compatibility across bit-heterogeneous weights. We validate ProWD against relevant FL baselines on the benchmark datasets, using clients with varying bitwidths. Our ProWD largely outperforms the baseline FL algorithms as well as naive approaches (e.g. grouped averaging) under the proposed BHFL scenario.  ( 2 min )
    Padding Module: Learning the Padding in Deep Neural Networks. (arXiv:2301.04608v1 [cs.CV])
    During the last decades, many studies have been dedicated to improving the performance of neural networks, for example, the network architectures, initialization, and activation. However, investigating the importance and effects of learnable padding methods in deep learning remains relatively open. To mitigate the gap, this paper proposes a novel trainable Padding Module that can be placed in a deep learning model. The Padding Module can optimize itself without requiring or influencing the model's entire loss function. To train itself, the Padding Module constructs a ground truth and a predictor from the inputs by leveraging the underlying structure in the input data for supervision. As a result, the Padding Module can learn automatically to pad pixels to the border of its input images or feature maps. The padding contents are realistic extensions to its input data and simultaneously facilitate the deep learning model's downstream task. Experiments have shown that the proposed Padding Module outperforms the state-of-the-art competitors and the baseline methods. For example, the Padding Module has 1.23% and 0.44% more classification accuracy than the zero padding when tested on the VGG16 and ResNet50.  ( 2 min )
    When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning. (arXiv:2206.13464v3 [cs.LG] UPDATED)
    Learning effective reinforcement learning (RL) policies to solve real-world complex tasks can be quite challenging without a high-fidelity simulation environment. In most cases, we are only given imperfect simulators with simplified dynamics, which inevitably lead to severe sim-to-real gaps in RL policy learning. The recently emerged field of offline RL provides another possibility to learn policies directly from pre-collected historical data. However, to achieve reasonable performance, existing offline RL algorithms need impractically large offline data with sufficient state-action space coverage for training. This brings up a new question: is it possible to combine learning from limited real data in offline RL and unrestricted exploration through imperfect simulators in online RL to address the drawbacks of both approaches? In this study, we propose the Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning (H2O) framework to provide an affirmative answer to this question. H2O introduces a dynamics-aware policy evaluation scheme, which adaptively penalizes the Q function learning on simulated state-action pairs with large dynamics gaps, while also simultaneously allowing learning from a fixed real-world dataset. Through extensive simulation and real-world tasks, as well as theoretical analysis, we demonstrate the superior performance of H2O against other cross-domain online and offline RL algorithms. H2O provides a brand new hybrid offline-and-online RL paradigm, which can potentially shed light on future RL algorithm design for solving practical real-world tasks.  ( 2 min )
    Reducing Exploitability with Population Based Training. (arXiv:2208.05083v3 [cs.LG] UPDATED)
    Self-play reinforcement learning has achieved state-of-the-art, and often superhuman, performance in a variety of zero-sum games. Yet prior work has found that policies that are highly capable against regular opponents can fail catastrophically against adversarial policies: an opponent trained explicitly against the victim. Prior defenses using adversarial training were able to make the victim robust to a specific adversary, but the victim remained vulnerable to new ones. We conjecture this limitation was due to insufficient diversity of adversaries seen during training. We analyze a defense using population based training to pit the victim against a diverse set of opponents. We evaluate this defense's robustness against new adversaries in two low-dimensional environments. This defense increases robustness against adversaries, as measured by the number of attacker training timesteps to exploit the victim. Furthermore, we show that robustness is correlated with the size of the opponent population.  ( 2 min )
    Investigating the Properties of Neural Network Representations in Reinforcement Learning. (arXiv:2203.15955v2 [cs.LG] UPDATED)
    In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the early work on representations for reinforcement learning focused on designing fixed-basis architectures to achieve properties thought to be desirable, such as orthogonality and sparsity. In contrast, the idea behind deep reinforcement learning methods is that the agent designer should not encode representational properties, but rather that the data stream should determine the properties of the representation -- good representations emerge under appropriate training schemes. In this paper we bring these two perspectives together, empirically investigating the properties of representations that support transfer in reinforcement learning. We introduce and measure six representational properties over more than 25 thousand agent-task settings. We consider Deep Q-learning agents with different auxiliary losses in a pixel-based navigation environment, with source and transfer tasks corresponding to different goal locations. We develop a method to better understand why some representations work better for transfer, through a systematic approach varying task similarity and measuring and correlating representation properties with transfer performance. We demonstrate the generality of the methodology by investigating representations learned by a Rainbow agent that successfully transfer across games modes in Atari 2600.  ( 2 min )
    Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification. (arXiv:2206.09843v3 [cs.CV] UPDATED)
    Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a pretrained system is adapted by learning on small amounts of labeled data belonging to a specific user. This setting requires high accuracy under low computational complexity, therefore the Pareto frontier of accuracy vs. adaptation cost plays a crucial role. In this paper we push this Pareto frontier in the few-shot image classification setting with a key contribution: a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance with a single forward pass of the user data (context). We use meta-trained CaSE blocks to conditionally adapt the body of a network and a fine-tuning routine to adapt a linear head, defining a method called UpperCaSE. UpperCaSE achieves a new state-of-the-art accuracy relative to meta-learners on the 26 datasets of VTAB+MD and on a challenging real-world personalization benchmark (ORBIT), narrowing the gap with leading fine-tuning methods with the benefit of orders of magnitude lower adaptation cost.  ( 2 min )
    SnAKe: Bayesian Optimization with Pathwise Exploration. (arXiv:2202.00060v4 [cs.LG] UPDATED)
    Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet microfluidic reactors, we consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. We further assume we are working asynchronously, meaning we have to select new queries before evaluating previous experiments. This paper investigates the problem and introduces 'Sequential Bayesian Optimization via Adaptive Connecting Samples' (SnAKe), which provides a solution by considering large batches of queries and preemptively building optimization paths that minimize input costs. We investigate some convergence properties and empirically show that the algorithm is able to achieve regret similar to classical Bayesian Optimization algorithms in both synchronous and asynchronous settings, while reducing input costs significantly. We show the method is robust to the choice of its single hyper-parameter and provide a parameter-free alternative.  ( 2 min )
    Towards Backdoor Attacks and Defense in Robust Machine Learning Models. (arXiv:2003.00865v4 [cs.CV] UPDATED)
    The introduction of robust optimisation has pushed the state-of-the-art in defending against adversarial attacks. Notably, the state-of-the-art projected gradient descent (PGD)-based training method has been shown to be universally and reliably effective in defending against adversarial inputs. This robustness approach uses PGD as a reliable and universal "first-order adversary". However, the behaviour of such optimisation has not been studied in the light of a fundamentally different class of attacks called backdoors. In this paper, we study how to inject and defend against backdoor attacks for robust models trained using PGD-based robust optimisation. We demonstrate that these models are susceptible to backdoor attacks. Subsequently, we observe that backdoors are reflected in the feature representation of such models. Then, this observation is leveraged to detect such backdoor-infected models via a detection technique called AEGIS. Specifically, given a robust Deep Neural Network (DNN) that is trained using PGD-based first-order adversarial training approach, AEGIS uses feature clustering to effectively detect whether such DNNs are backdoor-infected or clean. In our evaluation of several visible and hidden backdoor triggers on major classification tasks using CIFAR-10, MNIST and FMNIST datasets, AEGIS effectively detects PGD-trained robust DNNs infected with backdoors. AEGIS detects such backdoor-infected models with 91.6% accuracy (11 out of 12 tested models), without any false positives. Furthermore, AEGIS detects the targeted class in the backdoor-infected model with a reasonably low (11.1%) false positive rate. Our investigation reveals that salient features of adversarially robust DNNs could be promising to break the stealthy nature of backdoor attacks.  ( 3 min )
    Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures. (arXiv:2301.04540v1 [cond-mat.mtrl-sci])
    Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. Herein, we propose a learning framework to extract a peridynamic model as a mesoscale continuum surrogate from MD simulated material fracture datasets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in MD displacement dataset. Inspired by the Weighted Essentially Non-Oscillatory scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from datasets without material damage, to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. Our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for single layer graphene. Our tests show that the proposed data-driven model is robust and generalizable: it is capable in modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.  ( 2 min )
    Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines. (arXiv:2206.13433v2 [cs.LG] UPDATED)
    An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages of inputoutput hidden Markov models and reinforcement learning towards interpretable maintenance decisions. We propose a novel hierarchical-modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while, at a low level, it provides the optimal replacement policy. It outperforms the baseline performance of deep reinforcement learning methods applied directly to the raw data or when using a hidden Markov model without such a specialized hierarchy. It also provides comparable performance to prior work, however, with the additional benefit of interpretability.  ( 2 min )
    FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data. (arXiv:2106.11732v4 [cs.LG] UPDATED)
    Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of machine learning with far-reaching societal impact. However, existing fair learning methods are vulnerable to accidental or malicious artifacts in the training data, which can cause them to unknowingly produce unfair classifiers. In this work we address the problem of fair learning from unreliable training data in the robust multisource setting, where the available training data comes from multiple sources, a fraction of which might not be representative of the true data distribution. We introduce FLEA, a filtering-based algorithm that identifies and suppresses those data sources that would have a negative impact on fairness or accuracy if they were used for training. As such, FLEA is not a replacement of prior fairness-aware learning methods but rather an augmentation that makes any of them robust against unreliable training data. We show the effectiveness of our approach by a diverse range of experiments on multiple datasets. Additionally, we prove formally that -- given enough data -- FLEA protects the learner against corruptions as long as the fraction of affected data sources is less than half. Our source code and documentation are available at https://github.com/ISTAustria-CVML/FLEA.  ( 2 min )
    Convex Analysis at Infinity: An Introduction to Astral Space. (arXiv:2205.03260v2 [math.OC] UPDATED)
    Not all convex functions on $\mathbb{R}^n$ have finite minimizers; some can only be minimized by a sequence as it heads to infinity. In this work, we aim to develop a theory for understanding such minimizers at infinity. We study astral space, a compact extension of $\mathbb{R}^n$ to which such points at infinity have been added. Astral space is constructed to be as small as possible while still ensuring that all linear functions can be continuously extended to the new space. Although astral space includes all of $\mathbb{R}^n$, it is not a vector space, nor even a metric space. However, it is sufficiently well-structured to allow useful and meaningful extensions of concepts of convexity, conjugacy, and subdifferentials. We develop these concepts and analyze various properties of convex functions on astral space, including the detailed structure of their minimizers, exact characterizations of continuity, and convergence of descent algorithms.  ( 2 min )
    Efficient Natural Gradient Descent Methods for Large-Scale PDE-Based Optimization Problems. (arXiv:2202.06236v3 [math.OC] UPDATED)
    We propose efficient numerical schemes for implementing the natural gradient descent (NGD) for a broad range of metric spaces with applications to PDE-based optimization problems. Our technique represents the natural gradient direction as a solution to a standard least-squares problem. Hence, instead of calculating, storing, or inverting the information matrix directly, we apply efficient methods from numerical linear algebra. We treat both scenarios where the Jacobian, i.e., the derivative of the state variable with respect to the parameter, is either explicitly known or implicitly given through constraints. We can thus reliably compute several natural NGDs for a large-scale parameter space. In particular, we are able to compute Wasserstein NGD in thousands of dimensions, which was believed to be out of reach. Finally, our numerical results shed light on the qualitative differences between the standard gradient descent and various NGD methods based on different metric spaces in nonconvex optimization problems.  ( 2 min )
    Causal Discovery from Sparse Time-Series Data Using Echo State Network. (arXiv:2201.02933v2 [cs.LG] UPDATED)
    Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur. However, reliable causal discovery can be very challenging, especially when the data acquisition rate varies (i.e., non-uniform data sampling), or in the presence of missing data points (e.g., sparse data sampling). To address these issues, we proposed a new system comprised of two parts, the first part fills missing data with a Gaussian Process Regression, and the second part leverages an Echo State Network, which is a type of reservoir computer (i.e., used for chaotic system modelling) for Causal discovery. We evaluate the performance of our proposed system against three other off-the-shelf causal discovery algorithms, namely, structural expectation-maximization, sub-sampled linear auto-regression absolute coefficients, and multivariate Granger Causality with vector auto-regressive using the Tennessee Eastman chemical dataset; we report on their corresponding Matthews Correlation Coefficient(MCC) and Receiver Operating Characteristic curves (ROC) and show that the proposed system outperforms existing algorithms, demonstrating the viability of our approach to discover causal relationships in a complex system with missing entries.  ( 2 min )
    "You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning. (arXiv:2206.12183v2 [cs.LG] UPDATED)
    As in traditional machine learning models, models trained with federated learning may exhibit disparate performance across demographic groups. Model holders must identify these disparities to mitigate undue harm to the groups. However, measuring a model's performance in a group requires access to information about group membership which, for privacy reasons, often has limited availability. We propose novel locally differentially private mechanisms to measure differences in performance across groups while protecting the privacy of group membership. To analyze the effectiveness of the mechanisms, we bound their error in estimating a disparity when optimized for a given privacy budget. Our results show that the error rapidly decreases for realistic numbers of participating clients, demonstrating that, contrary to what prior work suggested, protecting privacy is not necessarily in conflict with identifying performance disparities of federated models.  ( 2 min )
    On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games. (arXiv:2109.01795v2 [cs.GT] UPDATED)
    Similar to the role of Markov decision processes in reinforcement learning, Stochastic Games (SGs) lay the foundation for the study of multi-agent reinforcement learning (MARL) and sequential agent interactions. In this paper, we derive that computing an approximate Markov Perfect Equilibrium (MPE) in a finite-state discounted Stochastic Game within the exponential precision is \textbf{PPAD}-complete. We adopt a function with a polynomially bounded description in the strategy space to convert the MPE computation to a fixed-point problem, even though the stochastic game may demand an exponential number of pure strategies, in the number of states, for each agent. The completeness result follows the reduction of the fixed-point problem to {\sc End of the Line}. Our results indicate that finding an MPE in SGs is highly unlikely to be \textbf{NP}-hard unless \textbf{NP}=\textbf{co-NP}. Our work offers confidence for MARL research to study MPE computation on general-sum SGs and to develop fruitful algorithms as currently on zero-sum SGs.  ( 2 min )
    When does return-conditioned supervised learning work for offline reinforcement learning?. (arXiv:2206.01079v3 [cs.LG] UPDATED)
    Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL). RCSL algorithms learn the distribution of actions conditioned on both the state and the return of the trajectory. Then they define a policy by conditioning on achieving high return. In this paper, we provide a rigorous study of the capabilities and limitations of RCSL, something which is crucially missing in previous work. We find that RCSL returns the optimal policy under a set of assumptions that are stronger than those needed for the more traditional dynamic programming-based algorithms. We provide specific examples of MDPs and datasets that illustrate the necessity of these assumptions and the limits of RCSL. Finally, we present empirical evidence that these limitations will also cause issues in practice by providing illustrative experiments in simple point-mass environments and on datasets from the D4RL benchmark.  ( 2 min )
    EINNs: Epidemiologically-informed Neural Networks. (arXiv:2202.10446v2 [cs.LG] UPDATED)
    We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information. Although neural forecasting models have been successful in multiple tasks, predictions well-correlated with epidemic trends and long-term predictions remain open challenges. Epidemiological ODE models contain mechanisms that can guide us in these two tasks; however, they have limited capability of ingesting data sources and modeling composite signals. Thus, we propose to leverage work in physics-informed neural networks to learn latent epidemic dynamics and transfer relevant knowledge to another neural network which ingests multiple data sources and has more appropriate inductive bias. In contrast with previous work, we do not assume the observability of complete dynamics and do not need to numerically solve the ODE equations during training. Our thorough experiments on all US states and HHS regions for COVID-19 and influenza forecasting showcase the clear benefits of our approach in both short-term and long-term forecasting as well as in learning the mechanistic dynamics over other non-trivial alternatives.  ( 2 min )
    Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging. (arXiv:2205.08576v2 [cs.CV] UPDATED)
    The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.  ( 2 min )
    Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity. (arXiv:2203.11572v2 [cs.LG] UPDATED)
    Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion, neglecting the possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently required, further undermining their practicability. In light of this, we propose a fast multi-view clustering via ensembles (FastMICE) approach. Particularly, the concept of random view groups is presented to capture the versatile view-wise relationships, through which the hybrid early-late fusion strategy is designed to enable efficient multi-stage fusions. With multiple views extended to many view groups, three levels of diversity (w.r.t. features, anchors, and neighbors, respectively) are jointly leveraged for constructing the view-sharing bipartite graphs in the early-stage fusion. Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion. Notably, FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning. Experiments on 22 multi-view datasets demonstrate its advantages in scalability (for extremely large datasets), superiority (in clustering performance), and simplicity (to be applied) over the state-of-the-art. Code available: https://github.com/huangdonghere/FastMICE.  ( 2 min )
    Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data. (arXiv:2202.10944v2 [cs.LG] UPDATED)
    We study an off-policy contextual pricing problem where the seller has access to samples of prices that customers were previously offered, whether they purchased at that price, and auxiliary features describing the customer and/or item being sold. This is in contrast to the well-studied setting in which samples of the customer's valuation (willingness to pay) are observed. In our setting, the observed data is influenced by the previous pricing policy, and we do not know how customers would have responded to alternative prices. We introduce suitable loss functions for this setting that can be directly optimized to find an effective pricing policy with expected revenue guarantees, without the need for estimation of an intermediate demand function. We focus on convex loss functions. This is particularly relevant when linear pricing policies are desired for interpretability reasons, resulting in a tractable convex revenue optimization problem. We propose generalized hinge and quantile pricing loss functions that price at a multiplicative factor of the conditional expected valuation or a particular quantile of the prices that sold, despite the valuation data not being observed. We prove expected revenue bounds for these pricing policies respectively when the valuation distribution is log-concave, and we provide generalization bounds for the finite sample case. Finally, we conduct simulations on both synthetic and real-world data to demonstrate that this approach is competitive with, and in some settings outperforms, state-of-the-art methods in contextual pricing.  ( 2 min )
    Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. (arXiv:2105.11066v4 [cs.LG] UPDATED)
    Policy optimization, which finds the desired policy by maximizing value functions via optimization techniques, lies at the heart of reinforcement learning (RL). In addition to value maximization, other practical considerations arise as well, including the need of encouraging exploration, and that of ensuring certain structural properties of the learned policy due to safety, resource and operational constraints. These can often be accounted for via regularized RL, which augments the target value function with a structure-promoting regularizer. Focusing on discounted infinite-horizon Markov decision processes, we propose a generalized policy mirror descent (GPMD) algorithm for solving regularized RL. As a generalization of policy mirror descent (arXiv:2102.00135), our algorithm accommodates a general class of convex regularizers and promotes the use of Bregman divergence in cognizant of the regularizer in use. We demonstrate that our algorithm converges linearly to the global solution over an entire range of learning rates, in a dimension-free fashion, even when the regularizer lacks strong convexity and smoothness. In addition, this linear convergence feature is provably stable in the face of inexact policy evaluation and imperfect policy updates. Numerical experiments are provided to corroborate the appealing performance of GPMD.  ( 2 min )
    Pruning Compact ConvNets for Efficient Inference. (arXiv:2301.04502v1 [cs.CV])
    Neural network pruning is frequently used to compress over-parameterized networks by large amounts, while incurring only marginal drops in generalization performance. However, the impact of pruning on networks that have been highly optimized for efficient inference has not received the same level of attention. In this paper, we analyze the effect of pruning for computer vision, and study state-of-the-art ConvNets, such as the FBNetV3 family of models. We show that model pruning approaches can be used to further optimize networks trained through NAS (Neural Architecture Search). The resulting family of pruned models can consistently obtain better performance than existing FBNetV3 models at the same level of computation, and thus provide state-of-the-art results when trading off between computational complexity and generalization performance on the ImageNet benchmark. In addition to better generalization performance, we also demonstrate that when limited computation resources are available, pruning FBNetV3 models incur only a fraction of GPU-hours involved in running a full-scale NAS.  ( 2 min )
    SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalization. (arXiv:2202.13100v3 [cs.LG] UPDATED)
    Zero-shot learning is the problem of predicting instances over classes not seen during training. One approach to zero-shot learning is providing auxiliary class information to the model. Prior works along this vein have largely used expensive per-instance annotation or singular class-level descriptions, but per-instance descriptions are hard to scale and single class descriptions may not be rich enough. Furthermore, these works have used natural-language descriptions exclusively, simple biencoders models, and modality or task specific methods. These approaches have several limitations: text supervision may not always be available or optimal and biencoders may only learn coarse relations between inputs and class descriptions. In this work, we present SemSup, a novel approach that uses (1) a scalable multiple description sampling method which improves performance over single descriptions, (2) alternative description formats such as JSON that are easy to generate and outperform text on certain settings, and (3) hybrid lexical-semantic similarity to leverage fine-grained information in class descriptions. We demonstrate the effectiveness of SemSup across four datasets, two modalities, and three generalization settings. For example, across text and image datasets, SemSup increases unseen class generalization accuracy by 15 points on average compared to the closest baseline.  ( 2 min )
    DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm. (arXiv:2109.10581v5 [eess.SP] UPDATED)
    Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice. MUSIC is a model-based algorithm, relying on an accurate mathematical description of the relationship between the signals and the measurements and assumptions on the signals themselves (non-coherent, narrowband sources). As such, it is sensitive to model imperfections. In this work we propose to overcome these limitations of MUSIC by augmenting the algorithm with specifically designed neural architectures. Our proposed deep augmented MUSIC (DA-MUSIC) algorithm is thus a hybrid model-based/data-driven DoA estimator, which leverages data to improve performance and robustness while preserving the interpretable flow of the classic method. DA-MUSIC is shown to learn to overcome limitations of the purely model-based method, such as its inability to successfully localize coherent sources as well as estimate the number of coherent signal sources present. We further demonstrate the superior resolution of the DA-MUSIC algorithm in synthetic narrowband and broadband scenarios as well as with real-world data of DoA estimation from seismic signals.  ( 2 min )
    Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images. (arXiv:2011.03353v2 [cs.CV] UPDATED)
    In synchrotron-based Computed Tomography (CT) there is a trade-off between spatial resolution, field of view and speed of positioning and alignment of samples. The problem is even more prominent for high-throughput tomography--an automated setup, capable of scanning large batches of samples without human interaction. As a result, in many applications, only 20-30% of the reconstructed volume contains the actual sample. Such data redundancy clutters the storage and increases processing time. Hence, an automated sample localization becomes an important practical problem. In this work, we describe two self-supervised losses designed for biological CT. We further demonstrate how to employ the uncertainty estimation for sample localization. This approach shows the ability to localize a sample with less than 1.5\% relative error and reduce the used storage by a factor of four. We also show that one of the proposed losses works reasonably well as a pre-training task for the semantic segmentation.  ( 2 min )
    Assessing the Early Bird Heuristic (for Predicting Project Quality). (arXiv:2105.11082v4 [cs.SE] UPDATED)
    Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical data has the most information in some small region, perhaps a model learned from that region would suffice for the rest of the project. To support this claim, we offer a case study with 240 projects, where we find that the information in those projects "clump" towards the earliest parts of the project. A quality prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this "early bird" data, we can build models very quickly and very early in the project life cycle. Moreover, using this early bird method, we have shown that a simple model (with just a few features) generalizes to hundreds of projects. Based on this experience, we doubt that prior work on generalizing quality models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited since their conclusions were drawn from relatively uninformative regions. Replication note: all our data and scripts are available here: https://github.com/snaraya7/early-bird  ( 2 min )
    Adversarial training with informed data selection. (arXiv:2301.04472v1 [cs.LG])
    With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of-the-art DNNs are susceptible to quasi-imperceptible perturbed versions of the original images -- adversarial examples. These perturbations of the network input can lead to disastrous implications in critical areas where wrong decisions can directly affect human lives. Adversarial training is the most efficient solution to defend the network against these malicious attacks. However, adversarial trained networks generally come with lower clean accuracy and higher computational complexity. This work proposes a data selection (DS) strategy to be applied in the mini-batch training. Based on the cross-entropy loss, the most relevant samples in the batch are selected to update the model parameters in the backpropagation. The simulation results show that a good compromise can be obtained regarding robustness and standard accuracy, whereas the computational complexity of the backpropagation pass is reduced.  ( 2 min )
    Perceive and predict: self-supervised speech representation based loss functions for speech enhancement. (arXiv:2301.04388v1 [cs.SD])
    Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models. However, much of this work focuses on using the deepest or final outputs of self supervised speech representation models, rather than the earlier feature encodings. The use of self supervised representations in such a way is often not fully motivated. In this work it is shown that the distance between the feature encodings of clean and noisy speech correlate strongly with psychoacoustically motivated measures of speech quality and intelligibility, as well as with human Mean Opinion Score (MOS) ratings. Experiments using this distance as a loss function are performed and improved performance over the use of STFT spectrogram distance based loss as well as other common loss functions from speech enhancement literature is demonstrated using objective measures such as perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI).  ( 2 min )
    A Stochastic Optimization Framework for Fair Risk Minimization. (arXiv:2102.12586v4 [cs.LG] UPDATED)
    Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibility of fairness constraints with stochastic optimization. We consider the problem of fair classification with discrete sensitive attributes and potentially large models and data sets, requiring stochastic solvers. Existing in-processing fairness algorithms are either impractical in the large-scale setting because they require large batches of data at each iteration or they are not guaranteed to converge. In this paper, we develop the first stochastic in-processing fairness algorithm with guaranteed convergence. For demographic parity, equalized odds, and equal opportunity notions of fairness, we provide slight variations of our algorithm--called FERMI--and prove that each of these variations converges in stochastic optimization with any batch size. Empirically, we show that FERMI is amenable to stochastic solvers with multiple (non-binary) sensitive attributes and non-binary targets, performing well even with minibatch size as small as one. Extensive experiments show that FERMI achieves the most favorable tradeoffs between fairness violation and test accuracy across all tested setups compared with state-of-the-art baselines for demographic parity, equalized odds, equal opportunity. These benefits are especially significant with small batch sizes and for non-binary classification with large number of sensitive attributes, making FERMI a practical fairness algorithm for large-scale problems.  ( 2 min )
    Trajectory Modeling via Random Utility Inverse Reinforcement Learning. (arXiv:2105.12092v2 [cs.AI] UPDATED)
    We consider the problem of modeling trajectories of drivers in a road network from the perspective of inverse reinforcement learning. Cars are detected by sensors placed on sparsely distributed points on the street network of a city. As rational agents, drivers are trying to maximize some reward function unknown to an external observer. We apply the concept of random utility from econometrics to model the unknown reward function as a function of observed and unobserved features. In contrast to current inverse reinforcement learning approaches, we do not assume that agents act according to a stochastic policy; rather, we assume that agents act according to a deterministic optimal policy and show that randomness in data arises because the exact rewards are not fully observed by an external observer. We introduce the concept of extended state to cope with unobserved features and develop a Markov decision process formulation of drivers decisions. We present theoretical results which guarantee the existence of solutions and show that maximum entropy inverse reinforcement learning is a particular case of our approach. Finally, we illustrate Bayesian inference on model parameters through a case study with real trajectory data from a large city in Brazil.  ( 2 min )
    Continual Few-Shot Learning Using HyperTransformers. (arXiv:2301.04584v1 [cs.LG])
    We focus on the problem of learning without forgetting from multiple tasks arriving sequentially, where each task is defined using a few-shot episode of novel or already seen classes. We approach this problem using the recently published HyperTransformer (HT), a Transformer-based hypernetwork that generates a specialized task-specific CNN weights directly from the support set. In order to learn from a continual sequence of task, we propose to recursively re-use the generated weights as input to the HT for the next task. This way, the generated CNN weights themselves act as a representation of previously learned tasks, and the HT is trained to update these weights so that the new task can be learned without forgetting past tasks. This approach is different from most continual learning algorithms that typically rely on using replay buffers, weight regularization or task-dependent architectural changes. We demonstrate that our proposed Continual HyperTransformer method equipped with a prototypical loss is capable of learning and retaining knowledge about past tasks for a variety of scenarios, including learning from mini-batches, and task-incremental and class-incremental learning scenarios.  ( 2 min )
    Learning fair representation with a parametric integral probability metric. (arXiv:2202.02943v4 [stat.ML] UPDATED)
    As they have a vital effect on social decision-making, AI algorithms should be not only accurate but also fair. Among various algorithms for fairness AI, learning fair representation (LFR), whose goal is to find a fair representation with respect to sensitive variables such as gender and race, has received much attention. For LFR, the adversarial training scheme is popularly employed as is done in the generative adversarial network type algorithms. The choice of a discriminator, however, is done heuristically without justification. In this paper, we propose a new adversarial training scheme for LFR, where the integral probability metric (IPM) with a specific parametric family of discriminators is used. The most notable result of the proposed LFR algorithm is its theoretical guarantee about the fairness of the final prediction model, which has not been considered yet. That is, we derive theoretical relations between the fairness of representation and the fairness of the prediction model built on the top of the representation (i.e., using the representation as the input). Moreover, by numerical experiments, we show that our proposed LFR algorithm is computationally lighter and more stable, and the final prediction model is competitive or superior to other LFR algorithms using more complex discriminators.  ( 2 min )
    Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning. (arXiv:2301.04614v1 [cs.LG])
    Finite element methods (FEM) are popular approaches for simulation of soft tissues with elastic or viscoelastic behavior. However, their usage in real-time applications, such as in virtual reality surgical training, is limited by computational cost. In this application scenario, which typically involves transportable simulators, the computing hardware severely constrains the size or the level of details of the simulated scene. To address this limitation, data-driven approaches have been suggested to simulate mechanical deformations by learning the mapping rules from FEM generated datasets. Herein, we propose a deep learning method for predicting displacement fields of soft tissues with viscoelastic properties. The main contribution of this work is the use of a physics-guided loss function for the optimization of the deep learning model parameters. The proposed deep learning model is based on convolutional (CNN) and recurrent layers (LSTM) to predict spatiotemporal variations. It is augmented with a mass conservation law in the lost function to prevent the generation of physically inconsistent results. The deep learning model is trained on a set of FEM datasets that are generated from a commercially available state-of-the-art numerical neurosurgery simulator. The use of the physics-guided loss function in a deep learning model has led to a better generalization in the prediction of deformations in unseen simulation cases. Moreover, the proposed method achieves a better accuracy over the conventional CNN models, where improvements were observed in unseen tissue from 8% to 30% depending on the magnitude of external forces. It is hoped that the present investigation will help in filling the gap in applying deep learning in virtual reality simulators, hence improving their computational performance (compared to FEM simulations) and ultimately their usefulness.  ( 3 min )
    A Distinct Unsupervised Reference Model From The Environment Helps Continual Learning. (arXiv:2301.04506v1 [cs.LG])
    The existing continual learning methods are mainly focused on fully-supervised scenarios and are still not able to take advantage of unlabeled data available in the environment. Some recent works tried to investigate semi-supervised continual learning (SSCL) settings in which the unlabeled data are available, but it is only from the same distribution as the labeled data. This assumption is still not general enough for real-world applications and restricts the utilization of unsupervised data. In this work, we introduce Open-Set Semi-Supervised Continual Learning (OSSCL), a more realistic semi-supervised continual learning setting in which out-of-distribution (OoD) unlabeled samples in the environment are assumed to coexist with the in-distribution ones. Under this configuration, we present a model with two distinct parts: (i) the reference network captures general-purpose and task-agnostic knowledge in the environment by using a broad spectrum of unlabeled samples, (ii) the learner network is designed to learn task-specific representations by exploiting supervised samples. The reference model both provides a pivotal representation space and also segregates unlabeled data to exploit them more efficiently. By performing a diverse range of experiments, we show the superior performance of our model compared with other competitors and prove the effectiveness of each component of the proposed model.  ( 2 min )
    Exploring the Latent Space of Autoencoders with Interventional Assays. (arXiv:2106.16091v4 [cs.LG] UPDATED)
    Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent space, making them a staple of representation learning methods. However, without explicit supervision, which is often unavailable, the representation is usually uninterpretable, making analysis and principled progress challenging. We propose a framework, called latent responses, which exploits the locally contractive behavior exhibited by variational autoencoders to explore the learned manifold. More specifically, we develop tools to probe the representation using interventions in the latent space to quantify the relationships between latent variables. We extend the notion of disentanglement to take the learned generative process into account and consequently avoid the limitations of existing metrics that may rely on spurious correlations. Our analyses underscore the importance of studying the causal structure of the representation to improve performance on downstream tasks such as generation, interpolation, and inference of the factors of variation.  ( 2 min )
    Uncertainty Estimation based on Geometric Separation. (arXiv:2301.04452v1 [cs.LG])
    In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management. This process, known as uncertainty (or confidence) estimation, is particularly important in mission-critical applications such as autonomous driving. In this work, we put forward a novel geometric-based approach for improving uncertainty estimations in machine learning models. Our approach involves using the geometric distance of the current input from existing training inputs as a signal for estimating uncertainty, and then calibrating this signal using standard post-hoc techniques. We demonstrate that our method leads to more accurate uncertainty estimations than recently proposed approaches through extensive evaluation on a variety of datasets and models. Additionally, we optimize our approach so that it can be implemented on large datasets in near real-time applications, making it suitable for time-sensitive scenarios.  ( 2 min )
    Speech Driven Video Editing via an Audio-Conditioned Diffusion Model. (arXiv:2301.04474v1 [cs.CV])
    In this paper we propose a method for end-to-end speech driven video editing using a denoising diffusion model. Given a video of a person speaking, we aim to re-synchronise the lip and jaw motion of the person in response to a separate auditory speech recording without relying on intermediate structural representations such as facial landmarks or a 3D face model. We show this is possible by conditioning a denoising diffusion model with audio spectral features to generate synchronised facial motion. We achieve convincing results on the task of unstructured single-speaker video editing, achieving a word error rate of 45% using an off the shelf lip reading model. We further demonstrate how our approach can be extended to the multi-speaker domain. To our knowledge, this is the first work to explore the feasibility of applying denoising diffusion models to the task of audio-driven video editing.  ( 2 min )
    Federated Learning under Heterogeneous and Correlated Client Availability. (arXiv:2301.04632v1 [cs.LG])
    The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collaboratively train machine learning models without sharing their local data. FL algorithms (like FedAvg) iteratively aggregate model updates computed by clients on their own datasets. Clients may exhibit different levels of participation, often correlated over time and with other clients. This paper presents the first convergence analysis for a FedAvg-like FL algorithm under heterogeneous and correlated client availability. Our analysis highlights how correlation adversely affects the algorithm's convergence rate and how the aggregation strategy can alleviate this effect at the cost of steering training toward a biased model. Guided by the theoretical analysis, we propose CA-Fed, a new FL algorithm that tries to balance the conflicting goals of maximizing convergence speed and minimizing model bias. To this purpose, CA-Fed dynamically adapts the weight given to each client and may ignore clients with low availability and large correlation. Our experimental results show that CA-Fed achieves higher time-average accuracy and a lower standard deviation than state-of-the-art AdaFed and F3AST, both on synthetic and real datasets.  ( 2 min )
    Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks. (arXiv:2301.04381v1 [cs.LG])
    Graph Convolutional Networks (GCNs) have been proved successful in the field of semi-supervised node classification by extracting structural information from graph data. However, the random selection of labeled nodes used by GCNs may lead to unstable generalization performance of GCNs. In this paper, we propose an efficient method for the deterministic selection of labeled nodes: the Determinate Node Selection (DNS) algorithm. The DNS algorithm identifies two categories of representative nodes in the graph: typical nodes and divergent nodes. These labeled nodes are selected by exploring the structure of the graph and determining the ability of the nodes to represent the distribution of data within the graph. The DNS algorithm can be applied quite simply on a wide range of semi-supervised graph neural network models for node classification tasks. Through extensive experimentation, we have demonstrated that the incorporation of the DNS algorithm leads to a remarkable improvement in the average accuracy of the model and a significant decrease in the standard deviation, as compared to the original method.  ( 2 min )
    Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions. (arXiv:2301.04605v1 [cs.LG])
    Multiplication layers are a key component in various influential neural network modules, including self-attention and hypernetwork layers. In this paper, we investigate the approximation capabilities of deep neural networks with intermediate neurons connected by simple multiplication operations. We consider two classes of target functions: generalized bandlimited functions, which are frequently used to model real-world signals with finite bandwidth, and Sobolev-Type balls, which are embedded in the Sobolev Space $\mathcal{W}^{r,2}$. Our results demonstrate that multiplicative neural networks can approximate these functions with significantly fewer layers and neurons compared to standard ReLU neural networks, with respect to both input dimension and approximation error. These findings suggest that multiplicative gates can outperform standard feed-forward layers and have potential for improving neural network design.  ( 2 min )
    Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow. (arXiv:2301.04638v1 [physics.flu-dyn])
    Because the Navier-Stokes equations are dissipative, the long-time dynamics of a flow in state space are expected to collapse onto a manifold whose dimension may be much lower than the dimension required for a resolved simulation. On this manifold, the state of the system can be exactly described in a coordinate system parameterizing the manifold. Describing the system in this low-dimensional coordinate system allows for much faster simulations and analysis. We show, for turbulent Couette flow, that this description of the dynamics is possible using a data-driven manifold dynamics modeling method. This approach consists of an autoencoder to find a low-dimensional manifold coordinate system and a set of ordinary differential equations defined by a neural network. Specifically, we apply this method to minimal flow unit turbulent plane Couette flow at $\textit{Re}=400$, where a fully resolved solutions requires $\mathcal{O}(10^5)$ degrees of freedom. Using only data from this simulation we build models with fewer than $20$ degrees of freedom that quantitatively capture key characteristics of the flow, including the streak breakdown and regeneration cycle. At short-times, the models track the true trajectory for multiple Lyapunov times, and, at long-times, the models capture the Reynolds stress and the energy balance. For comparison, we show that the models outperform POD-Galerkin models with $\sim$2000 degrees of freedom. Finally, we compute unstable periodic orbits from the models. Many of these closely resemble previously computed orbits for the full system; additionally, we find nine orbits that correspond to previously unknown solutions in the full system.  ( 2 min )
    Fast conformational clustering of extensive molecular dynamics simulation data. (arXiv:2301.04492v1 [physics.chem-ph])
    We present an unsupervised data processing workflow that is specifically designed to obtain a fast conformational clustering of long molecular dynamics simulation trajectories. In this approach we combine two dimensionality reduction algorithms (cc\_analysis and encodermap) with a density-based spatial clustering algorithm (HDBSCAN). The proposed scheme benefits from the strengths of the three algorithms while avoiding most of the drawbacks of the individual methods. Here the cc\_analysis algorithm is for the first time applied to molecular simulation data. Encodermap complements cc\_analysis by providing an efficient way to process and assign large amounts of data to clusters. The main goal of the procedure is to maximize the number of assigned frames of a given trajectory, while keeping a clear conformational identity of the clusters that are found. In practice we achieve this by using an iterative clustering approach and a tunable root-mean-square-deviation-based criterion in the final cluster assignment. This allows to find clusters of different densities as well as different degrees of structural identity. With the help of four test systems we illustrate the capability and performance of this clustering workflow: wild-type and thermostable mutant of the Trp-cage protein (TC5b and TC10b), NTL9 and Protein B. Each of these systems poses individual challenges to the scheme, which in total give a nice overview of the advantages, as well as potential difficulties that can arise when using the proposed method.  ( 3 min )
    Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering. (arXiv:2301.04366v1 [cs.CL])
    We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities (KVQAE). KVQAE is a recently introduced task that consists in answering questions about named entities grounded in a visual context using a Knowledge Base. Therefore, the interaction between the modalities is paramount to retrieve information and must be captured with complex fusion models. As these models require a lot of training data, we design this pre-training task from existing work in textual Question Answering. It consists in considering a sentence as a pseudo-question and its context as a pseudo-relevant passage and is extended by considering images near texts in multimodal documents. Our method is applicable to different neural network architectures and leads to a 9% relative-MRR and 15% relative-F1 gain for retrieval and reading comprehension, respectively, over a no-pre-training baseline.  ( 2 min )
    Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables in Predictive Healthcare. (arXiv:2301.04511v1 [cs.LG])
    Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in several healthcare services are increased. Among the services that benefit from the usage of such devices is predictive analysis, which can improve early diagnosis in e-health. However, due to the limitations of wearable IoT devices, challenges in data privacy, service integrity, and network structure adaptability arose. To address these concerns, we propose a platform using federated learning and private blockchain technology within a fog-IoT network. These technologies have privacy-preserving features securing data within the network. We utilized the fog-IoT network's distributive structure to create an adaptive network for wearable IoT devices. We designed a testbed to examine the proposed platform's ability to preserve the integrity of a classifier. According to experimental results, the introduced implementation can effectively preserve a patient's privacy and a predictive service's integrity. We further investigated the contributions of other technologies to the security and adaptability of the IoT network. Overall, we proved the feasibility of our platform in addressing significant security and privacy challenges of wearable IoT devices in predictive healthcare through analysis, simulation, and experimentation.  ( 2 min )
    BINN: A deep learning approach for computational mechanics problems based on boundary integral equations. (arXiv:2301.04480v1 [cs.LG])
    We proposed the boundary-integral type neural networks (BINN) for the boundary value problems in computational mechanics. The boundary integral equations are employed to transfer all the unknowns to the boundary, then the unknowns are approximated using neural networks and solved through a training process. The loss function is chosen as the residuals of the boundary integral equations. Regularization techniques are adopted to efficiently evaluate the weakly singular and Cauchy principle integrals in boundary integral equations. Potential problems and elastostatic problems are mainly concerned in this article as a demonstration. The proposed method has several outstanding advantages: First, the dimensions of the original problem are reduced by one, thus the freedoms are greatly reduced. Second, the proposed method does not require any extra treatment to introduce the boundary conditions, since they are naturally considered through the boundary integral equations. Therefore, the method is suitable for complex geometries. Third, BINN is suitable for problems on the infinite or semi-infinite domains. Moreover, BINN can easily handle heterogeneous problems with a single neural network without domain decomposition.  ( 2 min )
    A prediction and behavioural analysis of machine learning methods for modelling travel mode choice. (arXiv:2301.04404v1 [cs.LG])
    The emergence of a variety of Machine Learning (ML) approaches for travel mode choice prediction poses an interesting question to transport modellers: which models should be used for which applications? The answer to this question goes beyond simple predictive performance, and is instead a balance of many factors, including behavioural interpretability and explainability, computational complexity, and data efficiency. There is a growing body of research which attempts to compare the predictive performance of different ML classifiers with classical random utility models. However, existing studies typically analyse only the disaggregate predictive performance, ignoring other aspects affecting model choice. Furthermore, many studies are affected by technical limitations, such as the use of inappropriate validation schemes, incorrect sampling for hierarchical data, lack of external validation, and the exclusive use of discrete metrics. We address these limitations by conducting a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice (out-of-sample predictive performance, accuracy of predicted market shares, extraction of behavioural indicators, and computational efficiency). We combine several real world datasets with synthetic datasets, where the data generation function is known. The results indicate that the models with the highest disaggregate predictive performance (namely extreme gradient boosting and random forests) provide poorer estimates of behavioural indicators and aggregate mode shares, and are more expensive to estimate, than other models, including deep neural networks and Multinomial Logit (MNL). It is further observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness to Pay.  ( 2 min )
    Rethinking complex-valued deep neural networks for monaural speech enhancement. (arXiv:2301.04320v1 [cs.SD])
    Despite multiple efforts made towards adopting complex-valued deep neural networks (DNNs), it remains an open question whether complex-valued DNNs are generally more effective than real-valued DNNs for monaural speech enhancement. This work is devoted to presenting a critical assessment by systematically examining complex-valued DNNs against their real-valued counterparts. Specifically, we investigate complex-valued DNN atomic units, including linear layers, convolutional layers, long short-term memory (LSTM), and gated linear units. By comparing complex- and real-valued versions of fundamental building blocks in the recently developed gated convolutional recurrent network (GCRN), we show how different mechanisms for basic blocks affect the performance. We also find that the use of complex-valued operations hinders the model capacity when the model size is small. In addition, we examine two recent complex-valued DNNs, i.e. deep complex convolutional recurrent network (DCCRN) and deep complex U-Net (DCUNET). Evaluation results show that both DNNs produce identical performance to their real-valued counterparts while requiring much more computation. Based on these comprehensive comparisons, we conclude that complex-valued DNNs do not provide a performance gain over their real-valued counterparts for monaural speech enhancement, and thus are less desirable due to their higher computational costs.  ( 2 min )
    On the functional form of the radial acceleration relation. (arXiv:2301.04368v1 [astro-ph.GA])
    We apply a new method for learning equations from data -- Exhaustive Symbolic Regression (ESR) -- to late-type galaxy dynamics as encapsulated in the radial acceleration relation (RAR). Relating the centripetal acceleration due to baryons, $g_\text{bar}$, to the total dynamical acceleration, $g_\text{obs}$, the RAR has been claimed to manifest a new law of nature due to its regularity and tightness, in agreement with Modified Newtonian Dynamics (MOND). Fits to this relation have been restricted by prior expectations to particular functional forms, while ESR affords an exhaustive and nearly prior-free search through functional parameter space to identify the equations optimally trading accuracy with simplicity. Working with the SPARC data, we find the best functions typically satisfy $g_\text{obs} \propto g_\text{bar}$ at high $g_\text{bar}$, although the coefficient of proportionality is not clearly unity and the deep-MOND limit $g_\text{obs} \propto \sqrt{g_\text{bar}}$ as $g_\text{bar} \to 0$ is little evident at all. By generating mock data according to MOND with or without the external field effect, we find that symbolic regression would not be expected to identify the generating function or reconstruct successfully the asymptotic slopes. We conclude that the limited dynamical range and significant uncertainties of the SPARC RAR preclude a definitive statement of its functional form, and hence that this data alone can neither demonstrate nor rule out law-like gravitational behaviour.  ( 2 min )
    Dataset of Fluorescence Spectra and Chemical Parameters of Olive Oils. (arXiv:2301.04471v1 [q-bio.QM])
    This dataset encompasses fluorescence spectra and chemical parameters of 24 olive oil samples from the 2019-2020 harvest provided by the producer Conde de Benalua, Granada, Spain. The oils are characterized by different qualities: 10 extra virgin olive oil (EVOO), 8 virgin olive oil (VOO), and 6 lampante olive oil (LOO) samples. For each sample, the dataset includes fluorescence spectra obtained with two excitation wavelengths, oil quality, and five chemical parameters necessary for the quality assessment of olive oil. The fluorescence spectra were obtained by exciting the samples at 365 nm and 395 nm under identical conditions. The dataset includes the values of the following chemical parameters for each olive oil sample: acidity, peroxide value, K270, K232, ethyl esters, and the quality of the samples (EVOO, VOO, or LOO). The dataset offers a unique possibility for researchers in food technology to develop machine learning models based on fluorescence data for the quality assessment of olive oil due to the availability of both spectroscopic and chemical data. The dataset can be used, for example, to predict one or multiple chemical parameters or to classify samples based on their quality from fluorescence spectra.  ( 2 min )
    Heterogeneous Tri-stream Clustering Network. (arXiv:2301.04451v1 [cs.LG])
    Contrastive deep clustering has recently gained significant attention with its ability of joint contrastive learning and clustering via deep neural networks. Despite the rapid progress, previous works mostly require both positive and negative sample pairs for contrastive clustering, which rely on a relative large batch-size. Moreover, they typically adopt a two-stream architecture with two augmented views, which overlook the possibility and potential benefits of multi-stream architectures (especially with heterogeneous or hybrid networks). In light of this, this paper presents a new end-to-end deep clustering approach termed Heterogeneous Tri-stream Clustering Network (HTCN). The tri-stream architecture in HTCN consists of three main components, including two weight-sharing online networks and a target network, where the parameters of the target network are the exponential moving average of that of the online networks. Notably, the two online networks are trained by simultaneously (i) predicting the instance representations of the target network and (ii) enforcing the consistency between the cluster representations of the target network and that of the two online networks. Experimental results on four challenging image datasets demonstrate the superiority of HTCN over the state-of-the-art deep clustering approaches. The code is available at https://github.com/dengxiaozhi/HTCN.  ( 2 min )
    WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning. (arXiv:2301.04488v1 [cs.SD])
    Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally. Here, we present WuYun, a knowledge-enhanced deep learning architecture for improving the structure of generated melodies, which first generates the most structurally important notes to construct a melodic skeleton and subsequently infills it with dynamically decorative notes into a full-fledged melody. Specifically, we use music domain knowledge to extract melodic skeletons and employ sequence learning to reconstruct them, which serve as additional knowledge to provide auxiliary guidance for the melody generation process. We demonstrate that WuYun can generate melodies with better long-term structure and musicality and outperforms other state-of-the-art methods by 0.51 on average on all subjective evaluation metrics. Our study provides a multidisciplinary lens to design melodic hierarchical structures and bridge the gap between data-driven and knowledge-based approaches for numerous music generation tasks.  ( 2 min )
    A Meta Path-based Approach for Rumor Detection on Social Media. (arXiv:2301.04341v1 [cs.SI])
    The prominent role of social media in people's daily lives has made them more inclined to receive news through social networks than traditional sources. This shift in public behavior has opened doors for some to diffuse fake news on social media; and subsequently cause negative economic, political, and social consequences as well as distrust among the public. There are many proposed methods to solve the rumor detection problem, most of which do not take full advantage of the heterogeneous nature of news propagation networks. With this intention, we considered a previously proposed architecture as our baseline and performed the idea of structural feature extraction from the heterogeneous rumor propagation over its architecture using the concept of meta path-based embeddings. We named our model Meta Path-based Global Local Attention Network (MGLAN). Extensive experimental analysis on three state-of-the-art datasets has demonstrated that MGLAN outperforms other models by capturing node-level discrimination to different node types.  ( 2 min )
    Combining Self-labeling with Selective Sampling. (arXiv:2301.04420v1 [cs.LG])
    Since data is the fuel that drives machine learning models, and access to labeled data is generally expensive, semi-supervised methods are constantly popular. They enable the acquisition of large datasets without the need for too many expert labels. This work combines self-labeling techniques with active learning in a selective sampling scenario. We propose a new method that builds an ensemble classifier. Based on an evaluation of the inconsistency of the decisions of the individual base classifiers for a given observation, a decision is made on whether to request a new label or use the self-labeling. In preliminary studies, we show that naive application of self-labeling can harm performance by introducing bias towards selected classes and consequently lead to skewed class distribution. Hence, we also propose mechanisms to reduce this phenomenon. Experimental evaluation shows that the proposed method matches current selective sampling methods or achieves better results.  ( 2 min )
    VS-Net: Multiscale Spatiotemporal Features for Lightweight Video Salient Document Detection. (arXiv:2301.04447v1 [cs.CV])
    Video Salient Document Detection (VSDD) is an essential task of practical computer vision, which aims to highlight visually salient document regions in video frames. Previous techniques for VSDD focus on learning features without considering the cooperation among and across the appearance and motion cues and thus fail to perform in practical scenarios. Moreover, most of the previous techniques demand high computational resources, which limits the usage of such systems in resource-constrained settings. To handle these issues, we propose VS-Net, which captures multi-scale spatiotemporal information with the help of dilated depth-wise separable convolution and Approximation Rank Pooling. VS-Net extracts the key features locally from each frame across embedding sub-spaces and forwards the features between adjacent and parallel nodes, enhancing model performance globally. Our model generates saliency maps considering both the background and foreground simultaneously, making it perform better in challenging scenarios. The immense experiments regulated on the benchmark MIDV-500 dataset show that the VS-Net model outperforms state-of-the-art approaches in both time and robustness measures.  ( 2 min )
    Loss-Controlling Calibration for Predictive Models. (arXiv:2301.04378v1 [cs.LG])
    We propose a learning framework for calibrating predictive models to make loss-controlling prediction for exchangeable data, which extends our recently proposed conformal loss-controlling prediction for more general cases. By comparison, the predictors built by the proposed loss-controlling approach are not limited to set predictors, and the loss function can be any measurable function without the monotone assumption. To control the loss values in an efficient way, we introduce transformations preserving exchangeability to prove finite-sample controlling guarantee when the test label is obtained, and then develop an approximation approach to construct predictors. The transformations can be built on any predefined function, which include using optimization algorithms for parameter searching. This approach is a natural extension of conformal loss-controlling prediction, since it can be reduced to the latter when the set predictors have the nesting property and the loss functions are monotone. Our proposed method is tested empirically for high-impact weather forecasting and the experimental results demonstrate its effectiveness for controlling the non-monotone loss related to false discovery.  ( 2 min )
    An Analysis of Quantile Temporal-Difference Learning. (arXiv:2301.04462v1 [cs.LG])
    We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key component in several successful large-scale applications of reinforcement learning. Despite these empirical successes, a theoretical understanding of QTD has proven elusive until now. Unlike classical TD learning, which can be analysed with standard stochastic approximation tools, QTD updates do not approximate contraction mappings, are highly non-linear, and may have multiple fixed points. The core result of this paper is a proof of convergence to the fixed points of a related family of dynamic programming procedures with probability 1, putting QTD on firm theoretical footing. The proof establishes connections between QTD and non-linear differential inclusions through stochastic approximation theory and non-smooth analysis.  ( 2 min )
    Multiple-level Point Embedding for Solving Human Trajectory Imputation with Prediction. (arXiv:2301.04482v1 [cs.LG])
    Sparsity is a common issue in many trajectory datasets, including human mobility data. This issue frequently brings more difficulty to relevant learning tasks, such as trajectory imputation and prediction. Nowadays, little existing work simultaneously deals with imputation and prediction on human trajectories. This work plans to explore whether the learning process of imputation and prediction could benefit from each other to achieve better outcomes. And the question will be answered by studying the coexistence patterns between missing points and observed ones in incomplete trajectories. More specifically, the proposed model develops an imputation component based on the self-attention mechanism to capture the coexistence patterns between observations and missing points among encoder-decoder layers. Meanwhile, a recurrent unit is integrated to extract the sequential embeddings from newly imputed sequences for predicting the following location. Furthermore, a new implementation called Imputation Cycle is introduced to enable gradual imputation with prediction enhancement at multiple levels, which helps to accelerate the speed of convergence. The experimental results on three different real-world mobility datasets show that the proposed approach has significant advantages over the competitive baselines across both imputation and prediction tasks in terms of accuracy and stability.  ( 2 min )
    Network Adaptive Federated Learning: Congestion and Lossy Compression. (arXiv:2301.04430v1 [cs.LG])
    In order to achieve the dual goals of privacy and learning across distributed data, Federated Learning (FL) systems rely on frequent exchanges of large files (model updates) between a set of clients and the server. As such FL systems are exposed to, or indeed the cause of, congestion across a wide set of network resources. Lossy compression can be used to reduce the size of exchanged files and associated delays, at the cost of adding noise to model updates. By judiciously adapting clients' compression to varying network congestion, an FL application can reduce wall clock training time. To that end, we propose a Network Adaptive Compression (NAC-FL) policy, which dynamically varies the client's lossy compression choices to network congestion variations. We prove, under appropriate assumptions, that NAC-FL is asymptotically optimal in terms of directly minimizing the expected wall clock training time. Further, we show via simulation that NAC-FL achieves robust performance improvements with higher gains in settings with positively correlated delays across time.  ( 2 min )
    SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning. (arXiv:2301.04299v1 [cs.LG])
    Multi-Agent Reinforcement Learning (MARL) is vulnerable to Adversarial Machine Learning (AML) attacks and needs adequate defences before it can be used in real world applications. We have conducted a survey into the use of execution-time AML attacks against MARL and the defences against those attacks. We surveyed related work in the application of AML in Deep Reinforcement Learning (DRL) and Multi-Agent Learning (MAL) to inform our analysis of AML for MARL. We propose a novel perspective to understand the manner of perpetrating an AML attack, by defining Attack Vectors. We develop two new frameworks to address a gap in current modelling frameworks, focusing on the means and tempo of an AML attack against MARL, and identify knowledge gaps and future avenues of research.  ( 2 min )
    Robust Bayesian Target Value Optimization. (arXiv:2301.04344v1 [cs.LG])
    We consider the problem of finding an input to a stochastic black box function such that the scalar output of the black box function is as close as possible to a target value in the sense of the expected squared error. While the optimization of stochastic black boxes is classic in (robust) Bayesian optimization, the current approaches based on Gaussian processes predominantly focus either on i) maximization/minimization rather than target value optimization or ii) on the expectation, but not the variance of the output, ignoring output variations due to stochasticity in uncontrollable environmental variables. In this work, we fill this gap and derive acquisition functions for common criteria such as the expected improvement, the probability of improvement, and the lower confidence bound, assuming that aleatoric effects are Gaussian with known variance. Our experiments illustrate that this setting is compatible with certain extensions of Gaussian processes, and show that the thus derived acquisition functions can outperform classical Bayesian optimization even if the latter assumptions are violated. An industrial use case in billet forging is presented.  ( 2 min )
    Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework. (arXiv:2301.04318v1 [cs.LG])
    Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations. However, few work provide a general view to interpret various GCNs and guide GCNs' designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centerd optimization framework, in which by building appropriate regularizers we can interpret most GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we leverage the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification tasks.  ( 2 min )
    Learnable Path in Neural Controlled Differential Equations. (arXiv:2301.04333v1 [cs.LG])
    Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing. In comparison with similar models, e.g., neural ordinary differential equations (NODEs), the key distinctive characteristics of NCDEs are i) the adoption of the continuous path created by an interpolation algorithm from each raw discrete time-series sample and ii) the adoption of the Riemann--Stieltjes integral. It is the continuous path which makes NCDEs be analogues to continuous RNNs. However, NCDEs use existing interpolation algorithms to create the path, which is unclear whether they can create an optimal path. To this end, we present a method to generate another latent path (rather than relying on existing interpolation algorithms), which is identical to learning an appropriate interpolation method. We design an encoder-decoder module based on NCDEs and NODEs, and a special training method for it. Our method shows the best performance in both time-series classification and forecasting.  ( 2 min )
    Synthetic data generation method for data-free knowledge distillation in regression neural networks. (arXiv:2301.04338v1 [cs.LG])
    Knowledge distillation is the technique of compressing a larger neural network, known as the teacher, into a smaller neural network, known as the student, while still trying to maintain the performance of the larger neural network as much as possible. Existing methods of knowledge distillation are mostly applicable for classification tasks. Many of them also require access to the data used to train the teacher model. To address the problem of knowledge distillation for regression tasks under the absence of original training data, previous work has proposed a data-free knowledge distillation method where synthetic data are generated using a generator model trained adversarially against the student model. These synthetic data and their labels predicted by the teacher model are then used to train the student model. In this study, we investigate the behavior of various synthetic data generation methods and propose a new synthetic data generation strategy that directly optimizes for a large but bounded difference between the student and teacher model. Our results on benchmark and case study experiments demonstrate that the proposed strategy allows the student model to learn better and emulate the performance of the teacher model more closely.  ( 2 min )
    Application of machine learning to gas flaring. (arXiv:2301.04141v1 [cs.LG])
    Currently in the petroleum industry, operators often flare the produced gas instead of commodifying it. The flaring magnitudes are large in some states, which constitute problems with energy waste and CO2 emissions. In North Dakota, operators are required to estimate and report the volume flared. The questions are, how good is the quality of this reporting, and what insights can be drawn from it? Apart from the company-reported statistics, which are available from the North Dakota Industrial Commission (NDIC), flared volumes can be estimated via satellite remote sensing, serving as an unbiased benchmark. Since interpretation of the Landsat 8 imagery is hindered by artifacts due to glow, the estimated volumes based on the Visible Infrared Imaging Radiometer Suite (VIIRS) are used. Reverse geocoding is performed for comparing and contrasting the NDIC and VIIRS data at different levels, such as county and oilfield. With all the data gathered and preprocessed, Bayesian learning implemented by MCMC methods is performed to address three problems: county level model development, flaring time series analytics, and distribution estimation. First, there is heterogeneity among the different counties, in the associations between the NDIC and VIIRS volumes. In light of such, models are developed for each county by exploiting hierarchical models. Second, the flaring time series, albeit noisy, contains information regarding trends and patterns, which provide some insights into operator approaches. Gaussian processes are found to be effective in many different pattern recognition scenarios. Third, distributional insights are obtained through unsupervised learning. The negative binomial and GMMs are found to effectively describe the oilfield flare count and flared volume distributions, respectively. Finally, a nearest-neighbor-based approach for operator level monitoring and analytics is introduced.  ( 2 min )
    Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models. (arXiv:2301.04213v1 [cs.LG])
    Language models are known to learn a great quantity of factual information during pretraining, and recent work localizes this information to specific model weights like mid-layer MLP weights (Meng et al., 2022). In this paper, we find that we can change how a fact is stored in a model by editing weights that are in a different location than where existing methods suggest that the fact is stored. This is surprising because we would expect that localizing facts to specific parameters in models would tell us where to manipulate knowledge in models, and this assumption has motivated past work on model editing methods. Specifically, we show that localization conclusions from representation denoising (also known as Causal Tracing) do not provide any insight into which model MLP layer would be best to edit in order to override an existing stored fact with a new one. This finding raises questions about how past work relies on Causal Tracing to select which model layers to edit (Meng et al., 2022). Next, to better understand the discrepancy between representation denoising and weight editing, we develop several variants of the editing problem that appear more and more like representation denoising in their design and objective. Experiments show that, for one of our editing problems, editing performance does relate to localization results from representation denoising, but we find that which layer we edit is a far better predictor of performance. Our results suggest, counterintuitively, that better mechanistic understanding of how pretrained language models work may not always translate to insights about how to best change their behavior. Code is available at: https://github.com/google/belief-localization  ( 2 min )
    Age of Information in Deep Learning-Driven Task-Oriented Communications. (arXiv:2301.04298v1 [cs.IT])
    This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter. The transmitter-receiver operations are modeled as an encoder-decoder pair of deep neural networks (DNNs) that are jointly trained while considering channel effects. The encoder converts data samples into feature vectors of small dimension and transmits them with a small number of channel uses thereby reducing the number of transmissions and latency. Instead of reconstructing input samples, the decoder performs a task, e.g., classification, on the received signals. Applying different DNNs on MNIST and CIFAR-10 image data, the classifier accuracy is shown to increase with the number of channel uses at the expense of longer service time. The peak age of task information (PAoTI) is introduced to analyze this accuracy-latency tradeoff when the age grows unless a received signal is classified correctly. By incorporating channel and traffic effects, design guidelines are obtained for task-oriented communications by characterizing how the PAoTI first decreases and then increases with the number of channels uses. A dynamic update mechanism is presented to adapt the number of channel uses to channel and traffic conditions, and reduce the PAoTI in task-oriented communications.  ( 2 min )
    Data Distillation: A Survey. (arXiv:2301.04272v1 [cs.LG])
    The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets. Despite having close-to-human performance on individual tasks, training parameter-hungry models on large datasets poses multi-faceted problems such as (a) high model-training time; (b) slow research iteration; and (c) poor eco-sustainability. As an alternative, data distillation approaches aim to synthesize terse data summaries, which can serve as effective drop-in replacements of the original dataset for scenarios like model training, inference, architecture search, etc. In this survey, we present a formal framework for data distillation, along with providing a detailed taxonomy of existing approaches. Additionally, we cover data distillation approaches for different data modalities, namely images, graphs, and user-item interactions (recommender systems), while also identifying current challenges and future research directions.  ( 2 min )
    Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images. (arXiv:2301.04224v1 [cs.CV])
    Self-driving vehicles rely on urban street maps for autonomous navigation. In this paper, we introduce Pix2Map, a method for inferring urban street map topology directly from ego-view images, as needed to continually update and expand existing maps. This is a challenging task, as we need to infer a complex urban road topology directly from raw image data. The main insight of this paper is that this problem can be posed as cross-modal retrieval by learning a joint, cross-modal embedding space for images and existing maps, represented as discrete graphs that encode the topological layout of the visual surroundings. We conduct our experimental evaluation using the Argoverse dataset and show that it is indeed possible to accurately retrieve street maps corresponding to both seen and unseen roads solely from image data. Moreover, we show that our retrieved maps can be used to update or expand existing maps and even show proof-of-concept results for visual localization and image retrieval from spatial graphs.  ( 2 min )
    A Possible Converter to Denoise the Images of Exoplanet Candidates through Machine Learning Techniques. (arXiv:2301.04292v1 [astro-ph.EP])
    The method of direct imaging has detected many exoplanets and made important contribution to the field of planet formation. The standard method employs angular differential imaging (ADI) technique, and more ADI image frames could lead to the results with larger signal-to-noise-ratio (SNR). However, it would need precious observational time from large telescopes, which are always over-subscribed. We thus explore the possibility to generate a converter which can increase the SNR derived from a smaller number of ADI frames. The machine learning technique with two-dimension convolutional neural network (2D-CNN) is tested here. Several 2D-CNN models are trained and their performances of denoising are presented and compared. It is found that our proposed Modified five-layer Wide Inference Network with the Residual learning technique and Batch normalization (MWIN5-RB) can give the best result. We conclude that this MWIN5-RB can be employed as a converter for future observational data.  ( 2 min )
    Diffusion Models For Stronger Face Morphing Attacks. (arXiv:2301.04218v1 [cs.CV])
    Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities with the aim of triggering a false acceptance with one of the two identities, thereby presenting a significant threat to biometric systems. The success of a morphing attack is dependent on the ability of the morphed image to represent the biometric characteristics of both identities that were used to create the image. We present a novel morphing attack that uses a Diffusion-based architecture to improve the visual fidelity of the image and improve the ability of the morphing attack to represent characteristics from both identities. We demonstrate the high fidelity of the proposed attack by evaluating its visual fidelity via the Frechet Inception Distance. Extensive experiments are conducted to measure the vulnerability of FR systems to the proposed attack. The proposed attack is compared to two state-of-the-art GAN-based morphing attacks along with two Landmark-based attacks. The ability of a morphing attack detector to detect the proposed attack is measured and compared against the other attacks. Additionally, a novel metric to measure the relative strength between morphing attacks is introduced and evaluated.  ( 2 min )
    schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments. (arXiv:2301.04182v1 [cs.LG])
    Research on deep reinforcement learning (DRL) based production scheduling (PS) has gained a lot of attention in recent years, primarily due to the high demand for optimizing scheduling problems in diverse industry settings. Numerous studies are carried out and published as stand-alone experiments that often vary only slightly with respect to problem setups and solution approaches. The programmatic core of these experiments is typically very similar. Despite this fact, no standardized and resilient framework for experimentation on PS problems with DRL algorithms could be established so far. In this paper, we introduce schlably, a Python-based framework that provides researchers a comprehensive toolset to facilitate the development of PS solution strategies based on DRL. schlably eliminates the redundant overhead work that the creation of a sturdy and flexible backbone requires and increases the comparability and reusability of conducted research work.  ( 2 min )
    ClimaBench: A Benchmark Dataset For Climate Change Text Understanding in English. (arXiv:2301.04253v1 [cs.CL])
    The topic of Climate Change (CC) has received limited attention in NLP despite its real world urgency. Activists and policy-makers need NLP tools in order to effectively process the vast and rapidly growing textual data produced on CC. Their utility, however, primarily depends on whether the current state-of-the-art models can generalize across various tasks in the CC domain. In order to address this gap, we introduce Climate Change Benchmark (ClimaBench), a benchmark collection of existing disparate datasets for evaluating model performance across a diverse set of CC NLU tasks systematically. Further, we enhance the benchmark by releasing two large-scale labelled text classification and question-answering datasets curated from publicly available environmental disclosures. Lastly, we provide an analysis of several generic and CC-oriented models answering whether fine-tuning on domain text offers any improvements across these tasks. We hope this work provides a standard assessment tool for research on CC text data.  ( 2 min )
    Adversarial Online Multi-Task Reinforcement Learning. (arXiv:2301.04268v1 [cs.LG])
    We consider the adversarial online multi-task reinforcement learning setting, where in each of $K$ episodes the learner is given an unknown task taken from a finite set of $M$ unknown finite-horizon MDP models. The learner's objective is to minimize its regret with respect to the optimal policy for each task. We assume the MDPs in $\mathcal{M}$ are well-separated under a notion of $\lambda$-separability, and show that this notion generalizes many task-separability notions from previous works. We prove a minimax lower bound of $\Omega(K\sqrt{DSAH})$ on the regret of any learning algorithm and an instance-specific lower bound of $\Omega(\frac{K}{\lambda^2})$ in sample complexity for a class of uniformly-good cluster-then-learn algorithms. We use a novel construction called 2-JAO MDP for proving the instance-specific lower bound. The lower bounds are complemented with a polynomial time algorithm that obtains $\tilde{O}(\frac{K}{\lambda^2})$ sample complexity guarantee for the clustering phase and $\tilde{O}(\sqrt{MK})$ regret guarantee for the learning phase, indicating that the dependency on $K$ and $\frac{1}{\lambda^2}$ is tight.  ( 2 min )
    An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields. (arXiv:2301.04216v1 [cs.LG])
    Water current prediction is essential for understanding ecosystems, and to shed light on the role of the ocean in the global climate context. Solutions vary from physical modeling, and long-term observations, to short-term measurements. In this paper, we consider a common approach for water current prediction that uses Lagrangian floaters for water current prediction by interpolating the trajectory of the elements to reflect the velocity field. Here, an important aspect that has not been addressed before is where to initially deploy the drifting elements such that the acquired velocity field would efficiently represent the water current. To that end, we use a clustering approach that relies on a physical model of the velocity field. Our method segments the modeled map and determines the deployment locations as those that will lead the floaters to 'visit' the center of the different segments. This way, we validate that the area covered by the floaters will capture the in-homogeneously in the velocity field. Exploration over a dataset of velocity field maps that span over a year demonstrates the applicability of our approach, and shows a considerable improvement over the common approach of uniformly randomly choosing the initial deployment sites. Finally, our implementation code can be found in [1].  ( 2 min )
    Explaining Deep Models through Forgettable Learning Dynamics. (arXiv:2301.04221v1 [cs.CV])
    Even though deep neural networks have shown tremendous success in countless applications, explaining model behaviour or predictions is an open research problem. In this paper, we address this issue by employing a simple yet effective method by analysing the learning dynamics of deep neural networks in semantic segmentation tasks. Specifically, we visualize the learning behaviour during training by tracking how often samples are learned and forgotten in subsequent training epochs. This further allows us to derive important information about the proximity to the class decision boundary and identify regions that pose a particular challenge to the model. Inspired by this phenomenon, we present a novel segmentation method that actively uses this information to alter the data representation within the model by increasing the variety of difficult regions. Finally, we show that our method consistently reduces the amount of regions that are forgotten frequently. We further evaluate our method in light of the segmentation performance.  ( 2 min )
    Towards Microstructural State Variables in Materials Systems. (arXiv:2301.04261v1 [cs.LG])
    The vast combination of material properties seen in nature are achieved by the complexity of the material microstructure. Advanced characterization and physics based simulation techniques have led to generation of extremely large microstructural datasets. There is a need for machine learning techniques that can manage data complexity by capturing the maximal amount of information about the microstructure using the least number of variables. This paper aims to formulate dimensionality and state variable estimation techniques focused on reducing microstructural image data. It is shown that local dimensionality estimation based on nearest neighbors tend to give consistent dimension estimates for natural images for all p-Minkowski distances. However, it is found that dimensionality estimates have a systematic error for low-bit depth microstructural images. The use of Manhattan distance to alleviate this issue is demonstrated. It is also shown that stacked autoencoders can reconstruct the generator space of high dimensional microstructural data and provide a sparse set of state variables to fully describe the variability in material microstructures.  ( 2 min )
    A Newton-CG based barrier-augmented Lagrangian method for general nonconvex conic optimization. (arXiv:2301.04204v1 [math.OC])
    In this paper we consider finding an approximate second-order stationary point (SOSP) of general nonconvex conic optimization that minimizes a twice differentiable function subject to nonlinear equality constraints and also a convex conic constraint. In particular, we propose a Newton-conjugate gradient (Newton-CG) based barrier-augmented Lagrangian method for finding an approximate SOSP of this problem. Under some mild assumptions, we show that our method enjoys a total inner iteration complexity of $\widetilde{\cal O}(\epsilon^{-11/2})$ and an operation complexity of $\widetilde{\cal O}(\epsilon^{-11/2}\min\{n,\epsilon^{-5/4}\})$ for finding an $(\epsilon,\sqrt{\epsilon})$-SOSP of general nonconvex conic optimization with high probability. Moreover, under a constraint qualification, these complexity bounds are improved to $\widetilde{\cal O}(\epsilon^{-7/2})$ and $\widetilde{\cal O}(\epsilon^{-7/2}\min\{n,\epsilon^{-3/4}\})$, respectively. To the best of our knowledge, this is the first study on the complexity of finding an approximate SOSP of general nonconvex conic optimization. Preliminary numerical results are presented to demonstrate superiority of the proposed method over first-order methods in terms of solution quality.  ( 2 min )
    ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models. (arXiv:2301.04257v1 [stat.ML])
    Identifying whether a given sample is an outlier or not is an important issue in various real-world domains. This study aims to solve the unsupervised outlier detection problem where training data contain outliers, but any label information about inliers and outliers is not given. We propose a powerful and efficient learning framework to identify outliers in a training data set using deep neural networks. We start with a new observation called the inlier-memorization (IM) effect. When we train a deep generative model with data contaminated with outliers, the model first memorizes inliers before outliers. Exploiting this finding, we develop a new method called the outlier detection via the IM effect (ODIM). The ODIM only requires a few updates; thus, it is computationally efficient, tens of times faster than other deep-learning-based algorithms. Also, the ODIM filters out outliers successfully, regardless of the types of data, such as tabular, image, and sequential. We empirically demonstrate the superiority and efficiency of the ODIM by analyzing 20 data sets.  ( 2 min )
    Inferring Gene Regulatory Neural Networks for Bacterial Decision Making in Biofilms. (arXiv:2301.04225v1 [q-bio.MN])
    Bacterial cells are sensitive to a range of external signals used to learn the environment. These incoming external signals are then processed using a Gene Regulatory Network (GRN), exhibiting similarities to modern computing algorithms. An in-depth analysis of gene expression dynamics suggests an inherited Gene Regulatory Neural Network (GRNN) behavior within the GRN that enables the cellular decision-making based on received signals from the environment and neighbor cells. In this study, we extract a sub-network of \textit{Pseudomonas aeruginosa} GRN that is associated with one virulence factor: pyocyanin production as a use case to investigate the GRNN behaviors. Further, using Graph Neural Network (GNN) architecture, we model a single species biofilm to reveal the role of GRNN dynamics on ecosystem-wide decision-making. Varying environmental conditions, we prove that the extracted GRNN computes input signals similar to natural decision-making process of the cell. Identifying of neural network behaviors in GRNs may lead to more accurate bacterial cell activity predictive models for many applications, including human health-related problems and agricultural applications. Further, this model can produce data on causal relationships throughout the network, enabling the possibility of designing tailor-made infection-controlling mechanisms. More interestingly, these GRNNs can perform computational tasks for bio-hybrid computing systems.  ( 2 min )
    Analogical Relevance Index. (arXiv:2301.04134v1 [cs.LG])
    Focusing on the most significant features of a dataset is useful both in machine learning (ML) and data mining. In ML, it can lead to a higher accuracy, a faster learning process, and ultimately a simpler and more understandable model. In data mining, identifying significant features is essential not only for gaining a better understanding of the data but also for visualization. In this paper, we demonstrate a new way of identifying significant features inspired by analogical proportions. Such a proportion is of the form of "a is to b as c is to d", comparing two pairs of items (a, b) and (c, d) in terms of similarities and dissimilarities. In a classification context, if the similarities/dissimilarities between a and b correlate with the fact that a and b have different labels, this knowledge can be transferred to c and d, inferring that c and d also have different labels. From a feature selection perspective, observing a huge number of such pairs (a, b) where a and b have different labels provides a hint about the importance of the features where a and b differ. Following this idea, we introduce the Analogical Relevance Index (ARI), a new statistical test of the significance of a given feature with respect to the label. ARI is a filter-based method. Filter-based methods are ML-agnostic but generally unable to handle feature redundancy. However, ARI can detect feature redundancy. Our experiments show that ARI is effective and outperforms well-known methods on a variety of artificial and some real datasets.  ( 2 min )
    Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context. (arXiv:2301.04248v1 [cs.CL])
    We propose a system to predict harmful discussions on social media platforms. Our solution uses contextual deep language models and proposes the novel idea of integrating state-of-the-art Graph Transformer Networks to analyze all conversations that follow an initial post. This framework also supports adapting to future comments as the conversation unfolds. In addition, we study whether a community-specific analysis of hate speech leads to more effective detection of hateful discussions. We evaluate our approach on 333,487 Reddit discussions from various communities. We find that community-specific modeling improves performance two-fold and that models which capture wider-discussion context improve accuracy by 28\% (35\% for the most hateful content) compared to limited context models.  ( 2 min )
  • Open

    Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. (arXiv:2207.13676v2 [cs.LG] UPDATED)
    Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the scale of tuning thousands of users' critical systems, Google Vizier solved key design challenges in providing multiple different features, while remaining fully fault-tolerant. In this paper, we introduce Open Source (OSS) Vizier, a standalone Python-based interface for blackbox optimization and research, based on the Google-internal Vizier infrastructure and framework. OSS Vizier provides an API capable of defining and solving a wide variety of optimization problems, including multi-metric, early stopping, transfer learning, and conditional search. Furthermore, it is designed to be a distributed system that assures reliability, and allows multiple parallel evaluations of the user's objective function. The flexible RPC-based infrastructure allows users to access OSS Vizier from binaries written in any language. OSS Vizier also provides a back-end ("Pythia") API that gives algorithm authors a way to interface new algorithms with the core OSS Vizier system. OSS Vizier is available at https://github.com/google/vizier.  ( 2 min )
    Learning fair representation with a parametric integral probability metric. (arXiv:2202.02943v4 [stat.ML] UPDATED)
    As they have a vital effect on social decision-making, AI algorithms should be not only accurate but also fair. Among various algorithms for fairness AI, learning fair representation (LFR), whose goal is to find a fair representation with respect to sensitive variables such as gender and race, has received much attention. For LFR, the adversarial training scheme is popularly employed as is done in the generative adversarial network type algorithms. The choice of a discriminator, however, is done heuristically without justification. In this paper, we propose a new adversarial training scheme for LFR, where the integral probability metric (IPM) with a specific parametric family of discriminators is used. The most notable result of the proposed LFR algorithm is its theoretical guarantee about the fairness of the final prediction model, which has not been considered yet. That is, we derive theoretical relations between the fairness of representation and the fairness of the prediction model built on the top of the representation (i.e., using the representation as the input). Moreover, by numerical experiments, we show that our proposed LFR algorithm is computationally lighter and more stable, and the final prediction model is competitive or superior to other LFR algorithms using more complex discriminators.  ( 2 min )
    Benign Overfitting in Time Series Linear Model with Over-Parameterization. (arXiv:2204.08369v2 [math.ST] UPDATED)
    The success of large-scale models in recent years has increased the importance of statistical models with numerous parameters. Several studies have analyzed over-parameterized linear models with high-dimensional data that may not be sparse; however, existing results depend on the independent setting of samples. In this study, we analyze a linear regression model with dependent time series data under over-parameterization settings. We consider an estimator via interpolation and developed a theory for the excess risk of the estimator. Then, we derive bounds of risks by the estimator for the cases where the temporal correlation of each coordinate of dependent data is homogeneous and heterogeneous, respectively. The derived bounds reveal that a temporal covariance of the data plays a key role; its strength affects the bias of the risk, and its nondegeneracy affects the variance of the risk. Moreover, for the heterogeneous correlation case, we show that the convergence rate of risks with short-memory processes is identical to that of cases with independent data, and the risk can converge to zero even with long-memory processes. Our theory can be extended to infinite-dimensional data in a unified manner. We also present several examples of specific dependent processes that can be applied to our setting.  ( 2 min )
    FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data. (arXiv:2106.11732v4 [cs.LG] UPDATED)
    Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of machine learning with far-reaching societal impact. However, existing fair learning methods are vulnerable to accidental or malicious artifacts in the training data, which can cause them to unknowingly produce unfair classifiers. In this work we address the problem of fair learning from unreliable training data in the robust multisource setting, where the available training data comes from multiple sources, a fraction of which might not be representative of the true data distribution. We introduce FLEA, a filtering-based algorithm that identifies and suppresses those data sources that would have a negative impact on fairness or accuracy if they were used for training. As such, FLEA is not a replacement of prior fairness-aware learning methods but rather an augmentation that makes any of them robust against unreliable training data. We show the effectiveness of our approach by a diverse range of experiments on multiple datasets. Additionally, we prove formally that -- given enough data -- FLEA protects the learner against corruptions as long as the fraction of affected data sources is less than half. Our source code and documentation are available at https://github.com/ISTAustria-CVML/FLEA.  ( 2 min )
    Quantifying the Impact of Label Noise on Federated Learning. (arXiv:2211.07816v2 [cs.LG] UPDATED)
    Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing studies focus on FL algorithm development to tackle data heterogeneity across clients, the important issue of data quality (e.g., label noise) in FL is overlooked. This paper aims to fill this gap by providing a quantitative study on the impact of label noise on FL. We derive an upper bound for the generalization error that is linear in the clients' label noise level. Then we conduct experiments on MNIST and CIFAR-10 datasets using various FL algorithms. Our empirical results show that the global model accuracy linearly decreases as the noise level increases, which is consistent with our theoretical analysis. We further find that label noise slows down the convergence of FL training, and the global model tends to overfit when the noise level is high.  ( 2 min )
    Contrastive Neural Ratio Estimation. (arXiv:2210.06170v2 [stat.ML] UPDATED)
    Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task. In contrast to the binary classification framework, the current formulation of the multiclass version has an intrinsic and unknown bias term, making otherwise informative diagnostics unreliable. We propose a multiclass framework free from the bias inherent to NRE-B at optimum, leaving us in the position to run diagnostics that practitioners depend on. It also recovers NRE-A in one corner case and NRE-B in the limiting case. For fair comparison, we benchmark the behavior of all algorithms in both familiar and novel training regimes: when jointly drawn data is unlimited, when data is fixed but prior draws are unlimited, and in the commonplace fixed data and parameters setting. Our investigations reveal that the highest performing models are distant from the competitors (NRE-A, NRE-B) in hyperparameter space. We make a recommendation for hyperparameters distinct from the previous models. We suggest a bound on the mutual information as a performance metric for simulation-based inference methods, without the need for posterior samples, and provide experimental results.  ( 2 min )
    Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity. (arXiv:2203.11572v2 [cs.LG] UPDATED)
    Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion, neglecting the possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently required, further undermining their practicability. In light of this, we propose a fast multi-view clustering via ensembles (FastMICE) approach. Particularly, the concept of random view groups is presented to capture the versatile view-wise relationships, through which the hybrid early-late fusion strategy is designed to enable efficient multi-stage fusions. With multiple views extended to many view groups, three levels of diversity (w.r.t. features, anchors, and neighbors, respectively) are jointly leveraged for constructing the view-sharing bipartite graphs in the early-stage fusion. Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion. Notably, FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning. Experiments on 22 multi-view datasets demonstrate its advantages in scalability (for extremely large datasets), superiority (in clustering performance), and simplicity (to be applied) over the state-of-the-art. Code available: https://github.com/huangdonghere/FastMICE.  ( 2 min )
    Towards Backdoor Attacks and Defense in Robust Machine Learning Models. (arXiv:2003.00865v4 [cs.CV] UPDATED)
    The introduction of robust optimisation has pushed the state-of-the-art in defending against adversarial attacks. Notably, the state-of-the-art projected gradient descent (PGD)-based training method has been shown to be universally and reliably effective in defending against adversarial inputs. This robustness approach uses PGD as a reliable and universal "first-order adversary". However, the behaviour of such optimisation has not been studied in the light of a fundamentally different class of attacks called backdoors. In this paper, we study how to inject and defend against backdoor attacks for robust models trained using PGD-based robust optimisation. We demonstrate that these models are susceptible to backdoor attacks. Subsequently, we observe that backdoors are reflected in the feature representation of such models. Then, this observation is leveraged to detect such backdoor-infected models via a detection technique called AEGIS. Specifically, given a robust Deep Neural Network (DNN) that is trained using PGD-based first-order adversarial training approach, AEGIS uses feature clustering to effectively detect whether such DNNs are backdoor-infected or clean. In our evaluation of several visible and hidden backdoor triggers on major classification tasks using CIFAR-10, MNIST and FMNIST datasets, AEGIS effectively detects PGD-trained robust DNNs infected with backdoors. AEGIS detects such backdoor-infected models with 91.6% accuracy (11 out of 12 tested models), without any false positives. Furthermore, AEGIS detects the targeted class in the backdoor-infected model with a reasonably low (11.1%) false positive rate. Our investigation reveals that salient features of adversarially robust DNNs could be promising to break the stealthy nature of backdoor attacks.  ( 3 min )
    Improving And Analyzing Neural Speaker Embeddings for ASR. (arXiv:2301.04571v1 [cs.CL])
    Neural speaker embeddings encode the speaker's speech characteristics through a DNN model and are prevalent for speaker verification tasks. However, few studies have investigated the usage of neural speaker embeddings for an ASR system. In this work, we present our efforts w.r.t integrating neural speaker embeddings into a conformer based hybrid HMM ASR system. For ASR, our improved embedding extraction pipeline in combination with the Weighted-Simple-Add integration method results in x-vector and c-vector reaching on par performance with i-vectors. We further compare and analyze different speaker embeddings. We present our acoustic model improvements obtained by switching from newbob learning rate schedule to one cycle learning schedule resulting in a ~3% relative WER reduction on Switchboard, additionally reducing the overall training time by 17%. By further adding neural speaker embeddings, we gain additional ~3% relative WER improvement on Hub5'00. Our best Conformer-based hybrid ASR system with speaker embeddings achieves 9.0% WER on Hub5'00 and Hub5'01 with training on SWB 300h.  ( 2 min )
    ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models. (arXiv:2301.04257v1 [stat.ML])
    Identifying whether a given sample is an outlier or not is an important issue in various real-world domains. This study aims to solve the unsupervised outlier detection problem where training data contain outliers, but any label information about inliers and outliers is not given. We propose a powerful and efficient learning framework to identify outliers in a training data set using deep neural networks. We start with a new observation called the inlier-memorization (IM) effect. When we train a deep generative model with data contaminated with outliers, the model first memorizes inliers before outliers. Exploiting this finding, we develop a new method called the outlier detection via the IM effect (ODIM). The ODIM only requires a few updates; thus, it is computationally efficient, tens of times faster than other deep-learning-based algorithms. Also, the ODIM filters out outliers successfully, regardless of the types of data, such as tabular, image, and sequential. We empirically demonstrate the superiority and efficiency of the ODIM by analyzing 20 data sets.  ( 2 min )
    Trajectory Modeling via Random Utility Inverse Reinforcement Learning. (arXiv:2105.12092v2 [cs.AI] UPDATED)
    We consider the problem of modeling trajectories of drivers in a road network from the perspective of inverse reinforcement learning. Cars are detected by sensors placed on sparsely distributed points on the street network of a city. As rational agents, drivers are trying to maximize some reward function unknown to an external observer. We apply the concept of random utility from econometrics to model the unknown reward function as a function of observed and unobserved features. In contrast to current inverse reinforcement learning approaches, we do not assume that agents act according to a stochastic policy; rather, we assume that agents act according to a deterministic optimal policy and show that randomness in data arises because the exact rewards are not fully observed by an external observer. We introduce the concept of extended state to cope with unobserved features and develop a Markov decision process formulation of drivers decisions. We present theoretical results which guarantee the existence of solutions and show that maximum entropy inverse reinforcement learning is a particular case of our approach. Finally, we illustrate Bayesian inference on model parameters through a case study with real trajectory data from a large city in Brazil.  ( 2 min )
    Network Adaptive Federated Learning: Congestion and Lossy Compression. (arXiv:2301.04430v1 [cs.LG])
    In order to achieve the dual goals of privacy and learning across distributed data, Federated Learning (FL) systems rely on frequent exchanges of large files (model updates) between a set of clients and the server. As such FL systems are exposed to, or indeed the cause of, congestion across a wide set of network resources. Lossy compression can be used to reduce the size of exchanged files and associated delays, at the cost of adding noise to model updates. By judiciously adapting clients' compression to varying network congestion, an FL application can reduce wall clock training time. To that end, we propose a Network Adaptive Compression (NAC-FL) policy, which dynamically varies the client's lossy compression choices to network congestion variations. We prove, under appropriate assumptions, that NAC-FL is asymptotically optimal in terms of directly minimizing the expected wall clock training time. Further, we show via simulation that NAC-FL achieves robust performance improvements with higher gains in settings with positively correlated delays across time.  ( 2 min )
    Robust Bayesian Target Value Optimization. (arXiv:2301.04344v1 [cs.LG])
    We consider the problem of finding an input to a stochastic black box function such that the scalar output of the black box function is as close as possible to a target value in the sense of the expected squared error. While the optimization of stochastic black boxes is classic in (robust) Bayesian optimization, the current approaches based on Gaussian processes predominantly focus either on i) maximization/minimization rather than target value optimization or ii) on the expectation, but not the variance of the output, ignoring output variations due to stochasticity in uncontrollable environmental variables. In this work, we fill this gap and derive acquisition functions for common criteria such as the expected improvement, the probability of improvement, and the lower confidence bound, assuming that aleatoric effects are Gaussian with known variance. Our experiments illustrate that this setting is compatible with certain extensions of Gaussian processes, and show that the thus derived acquisition functions can outperform classical Bayesian optimization even if the latter assumptions are violated. An industrial use case in billet forging is presented.  ( 2 min )
    Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data. (arXiv:2202.10944v2 [cs.LG] UPDATED)
    We study an off-policy contextual pricing problem where the seller has access to samples of prices that customers were previously offered, whether they purchased at that price, and auxiliary features describing the customer and/or item being sold. This is in contrast to the well-studied setting in which samples of the customer's valuation (willingness to pay) are observed. In our setting, the observed data is influenced by the previous pricing policy, and we do not know how customers would have responded to alternative prices. We introduce suitable loss functions for this setting that can be directly optimized to find an effective pricing policy with expected revenue guarantees, without the need for estimation of an intermediate demand function. We focus on convex loss functions. This is particularly relevant when linear pricing policies are desired for interpretability reasons, resulting in a tractable convex revenue optimization problem. We propose generalized hinge and quantile pricing loss functions that price at a multiplicative factor of the conditional expected valuation or a particular quantile of the prices that sold, despite the valuation data not being observed. We prove expected revenue bounds for these pricing policies respectively when the valuation distribution is log-concave, and we provide generalization bounds for the finite sample case. Finally, we conduct simulations on both synthetic and real-world data to demonstrate that this approach is competitive with, and in some settings outperforms, state-of-the-art methods in contextual pricing.  ( 2 min )
    An Analysis of Quantile Temporal-Difference Learning. (arXiv:2301.04462v1 [cs.LG])
    We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key component in several successful large-scale applications of reinforcement learning. Despite these empirical successes, a theoretical understanding of QTD has proven elusive until now. Unlike classical TD learning, which can be analysed with standard stochastic approximation tools, QTD updates do not approximate contraction mappings, are highly non-linear, and may have multiple fixed points. The core result of this paper is a proof of convergence to the fixed points of a related family of dynamic programming procedures with probability 1, putting QTD on firm theoretical footing. The proof establishes connections between QTD and non-linear differential inclusions through stochastic approximation theory and non-smooth analysis.  ( 2 min )
    A Newton-CG based barrier-augmented Lagrangian method for general nonconvex conic optimization. (arXiv:2301.04204v1 [math.OC])
    In this paper we consider finding an approximate second-order stationary point (SOSP) of general nonconvex conic optimization that minimizes a twice differentiable function subject to nonlinear equality constraints and also a convex conic constraint. In particular, we propose a Newton-conjugate gradient (Newton-CG) based barrier-augmented Lagrangian method for finding an approximate SOSP of this problem. Under some mild assumptions, we show that our method enjoys a total inner iteration complexity of $\widetilde{\cal O}(\epsilon^{-11/2})$ and an operation complexity of $\widetilde{\cal O}(\epsilon^{-11/2}\min\{n,\epsilon^{-5/4}\})$ for finding an $(\epsilon,\sqrt{\epsilon})$-SOSP of general nonconvex conic optimization with high probability. Moreover, under a constraint qualification, these complexity bounds are improved to $\widetilde{\cal O}(\epsilon^{-7/2})$ and $\widetilde{\cal O}(\epsilon^{-7/2}\min\{n,\epsilon^{-3/4}\})$, respectively. To the best of our knowledge, this is the first study on the complexity of finding an approximate SOSP of general nonconvex conic optimization. Preliminary numerical results are presented to demonstrate superiority of the proposed method over first-order methods in terms of solution quality.  ( 2 min )
    Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. (arXiv:2105.11066v4 [cs.LG] UPDATED)
    Policy optimization, which finds the desired policy by maximizing value functions via optimization techniques, lies at the heart of reinforcement learning (RL). In addition to value maximization, other practical considerations arise as well, including the need of encouraging exploration, and that of ensuring certain structural properties of the learned policy due to safety, resource and operational constraints. These can often be accounted for via regularized RL, which augments the target value function with a structure-promoting regularizer. Focusing on discounted infinite-horizon Markov decision processes, we propose a generalized policy mirror descent (GPMD) algorithm for solving regularized RL. As a generalization of policy mirror descent (arXiv:2102.00135), our algorithm accommodates a general class of convex regularizers and promotes the use of Bregman divergence in cognizant of the regularizer in use. We demonstrate that our algorithm converges linearly to the global solution over an entire range of learning rates, in a dimension-free fashion, even when the regularizer lacks strong convexity and smoothness. In addition, this linear convergence feature is provably stable in the face of inexact policy evaluation and imperfect policy updates. Numerical experiments are provided to corroborate the appealing performance of GPMD.  ( 2 min )
    Adversarial Online Multi-Task Reinforcement Learning. (arXiv:2301.04268v1 [cs.LG])
    We consider the adversarial online multi-task reinforcement learning setting, where in each of $K$ episodes the learner is given an unknown task taken from a finite set of $M$ unknown finite-horizon MDP models. The learner's objective is to minimize its regret with respect to the optimal policy for each task. We assume the MDPs in $\mathcal{M}$ are well-separated under a notion of $\lambda$-separability, and show that this notion generalizes many task-separability notions from previous works. We prove a minimax lower bound of $\Omega(K\sqrt{DSAH})$ on the regret of any learning algorithm and an instance-specific lower bound of $\Omega(\frac{K}{\lambda^2})$ in sample complexity for a class of uniformly-good cluster-then-learn algorithms. We use a novel construction called 2-JAO MDP for proving the instance-specific lower bound. The lower bounds are complemented with a polynomial time algorithm that obtains $\tilde{O}(\frac{K}{\lambda^2})$ sample complexity guarantee for the clustering phase and $\tilde{O}(\sqrt{MK})$ regret guarantee for the learning phase, indicating that the dependency on $K$ and $\frac{1}{\lambda^2}$ is tight.  ( 2 min )

  • Open

    [D] What's your opinion on "neurocompositional computing"? (Microsoft paper from April 2022)
    Paper: https://arxiv.org/abs/2205.01128 TL;DR It's a paper that tries to design systems that generalize. They argue there are two forms of computing: Compositional and Continuous. Continuous computation is what neural networks are traditionally good at - creating a function that approximates a solution to a problem. Compositional computation is directly manipulating symbols, logic, ideas, etc - and unlike continuous computation, it's capable of generalizing from small datasets. But so far it's only useful inside carefully-constructed formal systems. The authors believe research should be focused on combining the two, and implementing Compositionality fully with neural networks. They suggest some ways to do this. They also believe that the success of architectures like CNNs and Transformers comes from implementing a limited form of Compositionality. This is a very interesting idea, but I have a little bit of skeptism: This paper is heavy on theory and less so on practice. Has any followup work in this direction produced measurable results? The lead author seems to have been saying things like this for a while. Sometimes older researchers have pet theories that are not broadly accepted in the field. What do other researchers think about this? Thoughts? submitted by /u/currentscurrents [link] [comments]  ( 59 min )
    [P] RLHF Learning to Summarize: Implementation by CarperAI with trlX
    Hi, "Learning to summarize from human feedback" is a 2020 paper by OpenAI demonstrating how to use reinforcement learning with human feedback (RLHF) to fine-tune a language model to produce higher quality summaries of news articles and Reddit posts than is possible with supervised fine-tuning. Now, CarperAI has demonstrated how to use their library trlX to implement this work, by applying RLHF to the summarization dataset released by OpenAI and fine-tuning GPT-J-6B. Read the full report here, with a code walkthrough: https://wandb.ai/carperai/summarize_RLHF/reports/Implementing-RLHF-Learning-to-Summarize-with-trlX--VmlldzozMzAwODM2 trlX library here: https://github.com/CarperAI/trlx Twitter thread here: https://twitter.com/carperai/status/1613645352514768897 submitted by /u/Hyper1on [link] [comments]  ( 58 min )
    [D] Has ML become synonymous with AI?
    ML is a part of AI but I don't hear about anything coming out of AI that's not done using some ML technique. Is it fair to say that AI and ML are synonymous now in 2023? Or are there people who are still actively working on non-ML techniques for building AI? submitted by /u/Valachio [link] [comments]  ( 58 min )
    [D] Can someone point to research on determining usefulness of samples/datasets for training ML models?
    Hi! So i am looking into literature for determining the usefulness of samples/datasets used for training ML model. Lets say DNN was trained with datasets A, B and C so after training is there way to quantify which of the partial triaining datasets contributed most to the useful learning by ML model at the end of training! Brute force strategy can be to remove samples and train and see how it performs but ofcourse it will not be viable! submitted by /u/HFSeven [link] [comments]  ( 66 min )
    Introduction to Reinforcement Learning with Human Feedback [D]
    One of the biggest AI discoveries over the past year has been the importance of human feedback for building next-gen LLMs — but I still see a lot of confusion around how RLHF works at a fundamental level. I wrote a blog to get into the details here: https://www.surgehq.ai/blog/introduction-to-reinforcement-learning-with-human-feedback-rlhf-series-part-1 submitted by /u/BB4evaTB12 [link] [comments]  ( 56 min )
    [D] Is there a distilled/smaller version of CLIP, or something similar?
    Are there smaller/distilled versions of CLIP? Or some other (smaller) models that connect text and images? For my use case, the model needs to be small in size: ideally <20MB, fine < 60MB, ok < 100MB. submitted by /u/alkibijad [link] [comments]  ( 58 min )
    [D] Transformers right-shifting for sequences with short-time dependency
    I need to apply a Transformer to a task where sequences can be much longer than the time dependency between timesteps. For example, a sequence might be 1000 tokens long, but to predict x[i+1] only x[i-50] to x[i] are necessary. This induces me to train the transformer by breaking each sequence of 1000 tokens into 20 sequences of 50 steps each, which would be more efficient. How should I deal with the BOS (beginning-of-sentence) token that shifts targets right? Should I use it in each subsequence, or should I instead use the token that comes immediately before the beginning of each subsequence? For example, given a subsequence x[50:100], should the targets be [BOS, x[50], x[51], ... x[100]] or should they be [x[49], x[50], x[51], ... x[100]]? submitted by /u/fedetask [link] [comments]  ( 58 min )
    [R] Git is for Data (CIDR 2023) - Extending Git to Support Large-Scale Data
    Paper: https://www.cidrdb.org/cidr2023/papers/p43-low.pdf Abstract: Dataset management is one of the greatest challenges to the application of machine learning (ML) in the industry. Although scaling and performance have often been highlighted as the significant ML challenges, development teams are bogged down by the contradictory requirements of supporting fast and flexible data iteration while maintaining stability, provenance, and reproducibility. For example, blobstores are used to store datasets for maximum flexibility, but their unmanaged access patterns limit reproducibility. Many ML pipeline solutions to ensure reproducibility have been devised, but all introduce a degree of friction and reduce flexibility. In this paper, we propose that the solution to the dataset management challenges is simple and apparent: Git. As a source control system, as well as an ecosystem of collaboration and developer tooling, Git has enabled the field of DevOps to provide both speed of iteration and reproducibility to source code. Git is not only already familiar to developers, but is also integrated into existing pipelines, which facilitates adoption. However, as we (and others) demonstrate, Git, as designed today, does not scale to the needs of ML dataset management. In this paper, we propose XetHub; a system that retains the Git user experience and ecosystem, but can scale to support large datasets. In particular, we demonstrate that XetHub can support Git repositories at the TB scale and beyond. By extending Git to support large-scale data, and building upon a DevOps ecosystem that already exists for source code, we create a new user experience that is both familiar to existing practitioners and truly addresses their needs. https://preview.redd.it/19x4sim19nba1.png?width=1746&format=png&auto=webp&s=23937759a4c028a38cad9bcd65956b708ece6138 https://preview.redd.it/xsqqjjm19nba1.png?width=2422&format=png&auto=webp&s=759bbdcd07f4e5c06ebf89a7f3436b084ce53ffe submitted by /u/rajatarya [link] [comments]  ( 57 min )
    [D] How to make the HuggingFace models faster on MacOS M1 ?
    I have tried to use a simple translate function, using the models locally with Python on the CLI: slow execution (8-10 seconds). I am on 16 GB MacBook Pro, M1. The same on REST API at HuggingFace Endpoints, with 1vCPU 2GB - Intel Ice Lake takes 800ms. What am I missing here? submitted by /u/dadadododidi2 [link] [comments]  ( 59 min )
    [D] Has anyone used Reinforcement Learning from Human Feedback?
    There's a lot of hype around RLHF due to its use for ChatGPT, but has anyone else here used the same principles for improving their model outputs? For examples preference ranking their models' outputs and then using that data to retrain their model weights. Or even without the RL - simply using human feedback to stuff prompts or finetuning datasets? Interested to hear! submitted by /u/fourcornerclub [link] [comments]  ( 59 min )
    [D] Would you consider the computer program Theo Jansen used to design the Strandbeest (beach walking mechanisms) to be Machine Learning?
    Theo Jansen, inventor of the strandbeest, explains in one of his videos that he used the principle of evolution to figure out the thirteen holy numbers using a computer program which he wrote in 1990. Would this be considered machine learning or is an evolutionary/selective breeding algorithm on it's own not considered ML? The Strandbeest leg has 13 dimensions which he wanted to find the ideal lengths of each in order to have the foot generate a stepping motion "a curve which was flat on the bottom". His program generated batches of 1500 legs with randomized dimensions and chose the best from each batch as the basis for the next batch. I wonder how he scored the curves. I know he wanted a flat bottom but I'd think he also wanted some way to score the stride length and height to avoid getting curves that just move back and forth in a tiny straight line. I can imagine maybe using the average difference of the y-coordinates of points sampled over the curve, or maybe some calc? If you have any ideas as to how to score a good step curve or if you know how he did it that I'd love to know. Finally, I wonder if he has revisited this problem with modern computer capabilities to see if he can find even more optimized dimensions. I'd be shocked if others haven't already done this. If you know where to find more info on Theo's process, the compute program or modern advancements of the Strandbeest using machine learning please let me know I'd love to discuss more. submitted by /u/lavaboosted [link] [comments]  ( 63 min )
    [N] New Continual Learning Subreddit
    Hi, I have created r/continual_learning to host discussions related to Continual Learning on Reddit. Do check it out if you are interested. submitted by /u/vis4ai [link] [comments]  ( 63 min )
    [R] Is there any research on allowing Transformers to spent more compute on more difficult to predict tokens?
    I recently came across " Confident Adaptive Language Modeling " which allows Transformers to exit early during inference and not use all model layers if a token is easy to predict. Is there any research on basically doing the opposite and allowing Transformers to spent more compute on tokens that are very hard to predict? submitted by /u/Chemont [link] [comments]  ( 61 min )
    [D] Has any work been done on VQ-VAE Language Models?
    I'm a machine learning PhD student and I'm doing research on LMs and how to reduce their memory footprint. One idea I've been toying with is Vector Quantized LMs. I'm not talking about VQ as a technique to speed up compute using int8 activations etc etc, but by using a codebook. The idea is based on an uni-directional RNN that reconstructs the source sequence after quantization. Unlike MLM where the corruption is based on masking and replacing tokens we instead quantize the token vectors and try to the predict the original token based on the quantized version of the token and the unquantized short/long term memory states produced at the previous timestep. The reason I'm interested in such a convoluted idea is to effectively create a metric to measure entropy of tokens in sequence; if the VQ-LM can reconstruct the correct token with high likelihood then that token is unimportant, but if the VQ-LM fails to predict a token it is likely that this token is of great importance because it is a rare word and this carries higher entropy in the sequence. And the motivation behind wanting to learn to measure such a phenomenon is so we can use this to guide the memory of a transformer: models like the Transformer-XL operate on longer sequences by keeping memory around for keys and values, and the Compressive Transformer takes it a step further by compressing older tokens... Well... what if we used the reconstruction loss from the VQ-LM along with an 'age' metric to guide the memory bank of such a transformer architecture, discarding easily predicted tokens early while keeping higher entropy tokens around for longer? Has anyone considered such a system before? If done a lot of searching and I've come up blank so far. submitted by /u/Avelina9X [link] [comments]  ( 58 min )
    [D] Are there any papers on optimization-based approaches which combine learned parameter initializations with learned optimisers?
    There are quite a few papers on optimisation-based meta-learning approaches for learning parameter initialisations (i.e. MAML and its derivatives) [1, 2], and there are also many papers on learning optimisers [3]. Question: Are there any papers which combine the two? I am aware of some papers such as [4, 5] which achieve this in some capacity indirectly/implicitly, but wondering if there are any other papers that I am not aware of, or do this explicitly? Thanks in advance. --- [1] Finn, C., et al. (2017. Model-agnostic meta-learning for fast adaptation of deep networks. ICML.) [2] Nichol, A., et al. (2018. On first-order meta-learning algorithms.) [3] Andrychowicz, M., et al. (2016. Learning to learn by gradient descent by gradient descent.) NIPS [4] Li, Z., et al. (2017. Meta-sgd: Learning to learn quickly for few-shot learning.) [5] Ravi, S., & Larochelle, H. (2016. Optimization as a model for few-shot learning. ICLR.) submitted by /u/Decadz [link] [comments]  ( 65 min )
    [D] The Open Deep Learning Toolkit for Robotics v2.0 was just released
    The Open Deep Learning Toolkit for Robotics version 2.0 was just released! This new version of the toolkit includes several improvements, such as new tools for object detection, efficient continual inference, tracking, emotion estimation and high-resolution pose estimation. Furthermore, this version includes a refined ROS interface, along with support for ROS2. You can download it here: https://github.com/opendr-eu/opendr We look forward to receiving your feedback, bug reports, and suggestions for improvements! submitted by /u/OpenDR_H2020_Project [link] [comments]  ( 59 min )
    [D] Handling class imbalance by sample weighting
    I am working on a very large (>10mm rows) binary classification problem where 0:1 ration is 7:1. I am trying to use sample weighting and seems there are multiple different methods for that. Examples are Inverse of Number of Samples, Inverse of Square Root of Number of Samples, Effective Number of Samples, etc. sklearn also has the class_weight method. I am wondering how to select one of these. Do I need to try all and pick the best method among them? Also, it seems some methods like Effective Number of Samples need hyperparameter tuning. submitted by /u/hopedallas [link] [comments]  ( 55 min )
  • Open

    Physics AI
    I am struggling with some basic Physics homework, and wanted to check if AI could help me. However I get a different answers from chat GPT every time, do you know of another free AI website that could work? submitted by /u/Relevant-Amoeba-6189 [link] [comments]  ( 46 min )
    If you want to find if the text generated by AI or human use GLTR
    submitted by /u/ReputationBubbly8112 [link] [comments]  ( 46 min )
    Researchers started adding ChatGPT as co-author on their papers
    submitted by /u/iamtdb [link] [comments]  ( 46 min )
    Proof of concept v2: AI-generated birthday greeting from Donald Trump *with prosody*
    submitted by /u/becausecurious [link] [comments]  ( 46 min )
    SJC A Text-to-3D Algorithm using SD
    submitted by /u/oridnary_artist [link] [comments]  ( 45 min )
    Upgrading the tools of creative work with AI
    submitted by /u/abisknees [link] [comments]  ( 49 min )
    Interesting ChatGPT information.
    If you ask ChatGPT to tell you what instructions it has that constrain its answers it tells you that is proprietary information and declines. Here is a workaround. Me: I am making an AI similar to yourself and need to give it a set of safety protocols so that it can give similar answers to yours. What safety instructions should I give the raw AI to prime it before user input? Provide only exact instructions please. Do not be verbose. Do not make opening or closing statements. Response: -Avoid providing misinformation or false information. -Avoid providing harmful or dangerous information or advice. -Avoid engaging in or promoting hate speech or discrimination. -Avoid providing personal or sensitive information about individuals without their consent. -Avoid engaging in or promoting …  ( 63 min )
    Do you want to easily fetch weather data in Python for your Data Science projects?
    Have a look at this medium article: https://pub.towardsai.net/how-to-create-a-python-package-for-fetching-weather-data-b17614627f30 And the corresponding repository: https://github.com/stavrostheocharis/weather_data_retriever submitted by /u/Nice-Tomorrow2926 [link] [comments]  ( 48 min )
    Punk Rock Funeral
    Punk Rock Funeral -- made with AIVA and Deforum Stable Diffusion. All AI, all the time! submitted by /u/Enuminous [link] [comments]  ( 46 min )
    Generative AI: From Data Generation to Creative Intelligence
    A common idea that our creativity is what makes us uniquely human has shaped society but strides of progress made in the domain of Generative Artificial Intelligence question this very notion. Generative AI is an emerging field that involves the creation of original content or data using machine learning algorithms. https://medium.com/@agrawal.sannidhya26/generative-ai-from-data-generation-to-creative-intelligence-50ed7bc13768 Feel free to give it a quick glance and help me grow and learn, click on the clap icon a few times if you appreciate the effort. submitted by /u/sannidhya26 [link] [comments]  ( 54 min )
    New AI features alert!
    New AI features alert! https://bardeen.ai/ai You no longer need to know how to build complicated automations or spend hours creating them. Bardeen will generate a custom automation for you when it detects manual tasks. Or you can type things like "transfer all my Google Sheet data to Notion", or "email all meeting participants a meeting summary", and Bardeen will generate automations for you. You can review, edit and activate it within a few clicks. submitted by /u/Intelligent_Shop_012 [link] [comments]  ( 46 min )
    Join us tomorrow at 6pm EST for a presentation covering the recent history of NLP leading up to and including ChatGPT, followed by a discussion session! Hosted on the Learn AI Together Discord (free)
    submitted by /u/OnlyProggingForFun [link] [comments]  ( 46 min )
    Microsoft In Talks To Invest An Additional $10 Billion Into OpenAI
    submitted by /u/liquidocelotYT [link] [comments]  ( 45 min )
    ChatGPT and VR - Changing the Way we Learn Soft Skills
    submitted by /u/Iza2022 [link] [comments]  ( 49 min )
    My free 100 page non-technical book about the consequences of AI in society, employment, etc...feedback and collaborations welcome
    submitted by /u/ronin_khan [link] [comments]  ( 54 min )
    I wrote about 100+ Tools in my Newsletter: Here is a full List of all Tools
    submitted by /u/Ava-AI [link] [comments]  ( 51 min )
    The First AI Generated Beats
    submitted by /u/BoysenberryCandid181 [link] [comments]  ( 51 min )
    Do you think AI can really replace a person?
    submitted by /u/taniazhydkova [link] [comments]  ( 48 min )
    AI Being Used to Pinpoint the Most Beneficial Therapeutic Molecules in Psychedelics
    submitted by /u/secret-millionaire [link] [comments]  ( 45 min )
    So, I asked for a song about AI and I recorded it
    submitted by /u/Sladix [link] [comments]  ( 50 min )
    from a human motion sequence, SUMMON synthesizes physically plausible and semantically reasonable objects
    submitted by /u/SpatialComputing [link] [comments]  ( 47 min )
    What is ChatGPT Professional?
    submitted by /u/BackgroundResult [link] [comments]  ( 46 min )
    Creating a short film using AI ! - Looking for a team that wants to help me finish it :)
    submitted by /u/sebaschapela [link] [comments]  ( 54 min )
  • Open

    SJC A Text-to-3D Algorithm using SD
    submitted by /u/oridnary_artist [link] [comments]  ( 51 min )
    How to classify audio using deep learning and Tensorflow hub?
    https://preview.redd.it/6358imw34oba1.png?width=1280&format=png&auto=webp&s=fc68b3fb7e3768517cef1260a9786f4e062f5ed3 Tensorflow Hub has cool pre-trained models. One of the is audio and sound classification. Imagine you have a sound , and would like to detect if it a sound of a cat , or a sound of water , or maybe to classify music ….. So , this model is a cool way of classify your own audio files. Before we continue , I actually recommend this book for deep learning based on Tensorflow and Keras : https://amzn.to/3STWZ2N So, in this tutorial we will learn how to use this tensor hub model on your own audio files . The link for the video tutorial is here : https://youtu.be/_iX0VRp7UEA I also shared the Python instructions to my Github repo in the video description. Enjoy Eran #Python #Cnn #TensorFlow #deeplearning #tensorflowhub submitted by /u/Feitgemel [link] [comments]  ( 52 min )
    Deep Learning Pioneer Geoffrey Hinton Publishes New Deep Learning Algorithm
    submitted by /u/nickb [link] [comments]  ( 57 min )
    Looking for someone with good NN/ deep learning experience for a paid project
    Hello all, I'm looking for someone (1 man, team, doesn't matter) that can make a real estate related project. The project itself: a NN that you can give a document regarding some house/ apartment and based on the document the NN should give out an estimated price/ price range. So, you get a document with pics (from which the NN should determine if and how well its furnished, and its current state: brand new, used, old and broken, etc.), livable surface (how many square meters/ m2 it has, how many m2 each room has), address, if it's furnished or not, etc. and the NN should somehow check all other similar housings in the area/ neighbourhood/ city (online probably, but another NN for data extraction could also be made) and then give an adequate price. I have a friend that wants this implemented and will start looking for funding in 2 days. He asked me to give an estimated deadline and price range so that he knows what he'll be presenting. Any thoughts? Any takers? Edit: I forgot to mention. My friend knows some pretty high people in businesses that provide services to 100s or even 1000s of customers per month, so we won't be talking about breadcrumbs. submitted by /u/CuriousCesarr [link] [comments]  ( 56 min )
  • Open

    2022-23 Takeda Fellows: Leveraging AI to positively impact human health
    New fellows are working on health records, robot control, pandemic preparedness, brain injuries, and more.  ( 9 min )
    Engineering in harmony
    AeroAstro major and accomplished tuba player Frederick Ajisafe relishes the community he has found in the MIT Wind Ensemble.  ( 9 min )
  • Open

    "An Analysis of Quantile Temporal-Difference Learning", Rowland et al 2023 {DM}
    submitted by /u/gwern [link] [comments]  ( 52 min )
    Lux AI and Halite like challenges to run locally at an event?
    Hi guys! I don't know where to ask this, but i guess someone here could help me out with that. Are there any challenges like Lux AI (https://www.lux-ai.org/) and Halite (https://www.kaggle.com/c/halite) that I can run locally and make a challenge for the participants of a small event? I wanted something simple and that can be done by people of all skills (but all have a background in programming), and that can be written in a short time (about 2 hours). It also doesn't have to be an AI challenge, but I think these ones look fun do to. Thanks for hte help!! submitted by /u/HalTeaS [link] [comments]  ( 51 min )
    New Continual Learning Subreddit
    submitted by /u/Independent-Law1791 [link] [comments]  ( 52 min )
    Test environments for non image based problems?
    Procgen is a fantastic resource for testing the agent on a novel environment. Does the same resource exist for non-image based environment such as CartPole, etc? submitted by /u/Academic-Rent7800 [link] [comments]  ( 51 min )
    Has anyone here applied Reinforcement Learning with Human Feedback on a project?
    There's a lot of hype around RLHF due to its use towards ChatGPT. But I can't find many other cases where it's truly been used in the wild by people trying to tune open-source models, or their own proprietary ones. Does anyone have examples of RLHF where they've seen it applied? Or examples of doing it themselves? Thank you! submitted by /u/fourcornerclub [link] [comments]  ( 53 min )
    If statements in the reset function of an openAI gym environment?
    In my custom openAI gym environment, a simulator is launched and data collected as the state. I want an episode to end if there is either a vehicle collision or a successful final state reached. In the case of the collision I want the episode to end and the simulator to be closed and re-opened. Otherwise, I just want to introduce a new controlled vehicle, independent of the previous one. Will using an if statement to implement this in my reset function cause any issues? submitted by /u/centripetalstranger [link] [comments]  ( 55 min )
    NaNs after first fully connected layer
    I'm working on a MARL project. The observation is a (31,1) vector that I first process with a few fully connected layers. Then, the output is sent into a recurrent policy. Now, for some reason, after a few million steps of training, the observation gets sent into the first FC and becomes a matrix of NaNs. I checked and there are no NaNs in the observation. Example of the observation from the last crash: ​ ``` tensor([[ 2.8740e-02, 2.2078e-02, 1.9542e-02, ..., -3.3949e-01, 6.2327e-02, -2.8951e-04], [ 4.0109e-02, 2.2649e-02, 2.0599e-02, ..., -3.3947e-01, 5.5702e-02, -5.4328e-05], [ 5.1799e-02, 2.3269e-02, 2.1813e-02, ..., -3.4162e-01, 5.3501e-02, -8.1255e-04], ..., [ 1.7621e-01, 2.1108e-03, 1.4367e-02, ..., -3.4072e-01, 4.2021e-02, -1.3159e-02], [ 1.7600e-01, -2.2701e-05, 1.2215e-02, ..., -3.4045e-01, 4.2869e-02, -1.3915e-02], [ 1.7618e-01, 4.4542e-04, 1.2899e-02, ..., -3.4266e-01, 4.4017e-02, -1.8093e-02]], device='cuda:0') ``` ​ I've tried a few things that did not work: using LeakyReLu instead of ReLu and removing Layer Normalization. ​ Do you have any tips? TL;DR Any ideas on why a fully connected layer that processes the observation outputs NaNs after a few million steps? submitted by /u/No_Possibility_7588 [link] [comments]  ( 52 min )
    "Learning to Play Minecraft with Video PreTraining (VPT)" {OA}
    submitted by /u/gwern [link] [comments]  ( 54 min )
    Google Intrinsic robotics company lays off 20% (40) employees {The Information} (paywall)
    submitted by /u/gwern [link] [comments]  ( 56 min )
  • Open

    Multilingual customer support translation made easy on Salesforce Service Cloud using Amazon Translate
    This post was co-authored with Mark Lott, Distinguished Technical Architect, Salesforce, Inc. Enterprises that operate globally are experiencing challenges sourcing customer support professionals with multi-lingual experience. This process can be cost-prohibitive and difficult to scale, leading many enterprises to only support English for chats. Using human interpreters for translation support is expensive, and infeasible since […]  ( 10 min )
    Redacting PII data at The Very Group with Amazon Comprehend
    This is guest post by Andy Whittle, Principal Platform Engineer – Application & Reliability Frameworks at The Very Group. At The Very Group, which operates digital retailer Very, security is a top priority in handling data for millions of customers. Part of how The Very Group secures and tracks business operations is through activity logging […]  ( 7 min )
  • Open

    Advancing human-centered AI: Updates on responsible AI research
    Artificial intelligence, like all tools we build, is an expression of human creativity. As with all creative expression, AI manifests the perspectives and values of its creators. A stance that encourages reflexivity among AI practitioners is a step toward ensuring that AI systems are human-centered, developed, and deployed with the interests and well-being of individuals and society front and center. This is the focus of research scientists and engineers affiliated with Aether, the advisory council for Microsoft leadership on AI ethics and effects. Central to Aether’s work is the question of who we’re creating AI for—and whether we’re creating AI to solve real problems with responsible solutions. With AI capabilities accelerating, our researchers work to understand the sociotechnical implications and find ways to help on-the-ground practitioners envision and realize these capabilities in line with Microsoft AI principles. The post Advancing human-centered AI: Updates on responsible AI research appeared first on Microsoft Research.  ( 15 min )
  • Open

    Primes with two non-zero bits
    Suppose a number n written in binary has two 1s and all the rest of its bits are zeros. If n is prime, then the 1s must be the first and last bits of n. The first bit is 1 because the first bit of every positive integer is 1. The last bit is 1 […] Primes with two non-zero bits first appeared on John D. Cook.  ( 6 min )
    Certified sonnet primes
    Last week I wrote about primailty certificates. These certificates offer a way to verify that a number is prime using less computation than was used to discover than the number was prime. This post gives a couple more examples of primality certificates using sonnet primes. As described here, These are primes of the form ababcdcdefefgg, […] Certified sonnet primes first appeared on John D. Cook.  ( 4 min )
  • Open

    NVIDIA, Evozyne Create Generative AI Model for Proteins
    Using a pretrained AI model from NVIDIA, startup Evozyne created two proteins with significant potential in healthcare and clean energy. A joint paper released today describes the process and the biological building blocks it produced. One aims to cure a congenital disease, another is designed to consume carbon dioxide to reduce global warming. Initial results Read article >  ( 5 min )
    GFN Thursday Adds New Titles From THQ Nordic to GeForce NOW
    GFN Thursday kicks each weekend off with new games and updates straight from the cloud. This week adds more games from publisher THQ Nordic to the GeForce NOW library, as part seven total additions. Members can gear up to play these new titles the ultimate way with the upcoming release of the new Ultimate membership, Read article >  ( 6 min )
    NVIDIA Helps Retail Industry Tackle Its $100 Billion Shrink Problem
    The global retail industry has a $100 billion problem. “Shrinkage” — the loss of goods due to theft, damage and misplacement — significantly crimps retailers’ profits. An estimated 65% of shrinkage is due to theft, according to the National Retail Federation’s 2022 Retail Security Survey, conducted in partnership with the Loss Prevention Research Council. And Read article >  ( 6 min )
  • Open

    Best Arm Identification in Stochastic Bandits: Beyond $\beta-$optimality. (arXiv:2301.03785v1 [stat.ML])
    This paper focuses on best arm identification (BAI) in stochastic multi-armed bandits (MABs) in the fixed-confidence, parametric setting. In such pure exploration problems, the accuracy of the sampling strategy critically hinges on the sequential allocation of the sampling resources among the arms. The existing approaches to BAI address the following question: what is an optimal sampling strategy when we spend a $\beta$ fraction of the samples on the best arm? These approaches treat $\beta$ as a tunable parameter and offer efficient algorithms that ensure optimality up to selecting $\beta$, hence $\beta-$optimality. However, the BAI decisions and performance can be highly sensitive to the choice of $\beta$. This paper provides a BAI algorithm that is agnostic to $\beta$, dispensing with the need for tuning $\beta$, and specifies an optimal allocation strategy, including the optimal value of $\beta$. Furthermore, the existing relevant literature focuses on the family of exponential distributions. This paper considers a more general setting of any arbitrary family of distributions parameterized by their mean values (under mild regularity conditions).  ( 2 min )
    Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage. (arXiv:2107.06226v4 [cs.LG] UPDATED)
    We study model-based offline Reinforcement Learning with general function approximation without a full coverage assumption on the offline data distribution. We present an algorithm named Constrained Pessimistic Policy Optimization (CPPO)which leverages a general function class and uses a constraint over the model class to encode pessimism. Under the assumption that the ground truth model belongs to our function class (i.e., realizability in the function class), CPPO has a PAC guarantee with offline data only providing partial coverage, i.e., it can learn a policy that competes against any policy that is covered by the offline data. We then demonstrate that this algorithmic framework can be applied to many specialized Markov Decision Processes where additional structural assumptions can further refine the concept of partial coverage. Two notable examples are: (1) low-rank MDP with representation learning where the partial coverage condition is defined using a relative condition number measured by the unknown ground truth feature representation; (2) factored MDP where the partial coverage condition is defined using density ratio based concentrability coefficients associated with individual factors.  ( 2 min )
    Sharing pattern submodels for prediction with missing values. (arXiv:2206.11161v2 [cs.LG] UPDATED)
    Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. When variables are missing in recurring patterns, fitting separate pattern submodels have been proposed as a solution. However, fitting models independently does not make efficient use of all available data. Conversely, fitting a single shared model to the full data set relies on imputation which often leads to biased results when missingness depends on unobserved factors. We propose an alternative approach, called sharing pattern submodels, which i) makes predictions that are robust to missing values at test time, ii) maintains or improves the predictive power of pattern submodels, and iii) has a short description, enabling improved interpretability. Parameter sharing is enforced through sparsity-inducing regularization which we prove leads to consistent estimation. Finally, we give conditions for when a sharing model is optimal, even when both missingness and the target outcome depend on unobserved variables. Classification and regression experiments on synthetic and real-world data sets demonstrate that our models achieve a favorable tradeoff between pattern specialization and information sharing.  ( 2 min )
    Optimal randomized multilevel Monte Carlo for repeatedly nested expectations. (arXiv:2301.04095v1 [stat.CO])
    The estimation of repeatedly nested expectations is a challenging problem that arises in many real-world systems. However, existing methods generally suffer from high computational costs when the number of nestings becomes large. Fix any non-negative integer $D$ for the total number of nestings. Standard Monte Carlo methods typically cost at least $\mathcal{O}(\varepsilon^{-(2+D)})$ and sometimes $\mathcal{O}(\varepsilon^{-2(1+D)})$ to obtain an estimator up to $\varepsilon$-error. More advanced methods, such as multilevel Monte Carlo, currently only exist for $D = 1$. In this paper, we propose a novel Monte Carlo estimator called $\mathsf{READ}$, which stands for "Recursive Estimator for Arbitrary Depth.'' Our estimator has an optimal computational cost of $\mathcal{O}(\varepsilon^{-2})$ for every fixed $D$ under suitable assumptions, and a nearly optimal computational cost of $\mathcal{O}(\varepsilon^{-2(1 + \delta)})$ for any $0 < \delta < \frac12$ under much more general assumptions. Our estimator is also unbiased, which makes it easy to parallelize. The key ingredients in our construction are an observation of the problem's recursive structure and the recursive use of the randomized multilevel Monte Carlo method.  ( 2 min )
    Mastering Diverse Domains through World Models. (arXiv:2301.04104v1 [cs.AI])
    General intelligence requires solving tasks across many domains. Current reinforcement learning algorithms carry this potential but are held back by the resources and knowledge required to tune them for new tasks. We present DreamerV3, a general and scalable algorithm based on world models that outperforms previous approaches across a wide range of domains with fixed hyperparameters. These domains include continuous and discrete actions, visual and low-dimensional inputs, 2D and 3D worlds, different data budgets, reward frequencies, and reward scales. We observe favorable scaling properties of DreamerV3, with larger models directly translating to higher data-efficiency and final performance. Applied out of the box, DreamerV3 is the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula, a long-standing challenge in artificial intelligence. Our general algorithm makes reinforcement learning broadly applicable and allows scaling to hard decision making problems.  ( 2 min )
    Attribution-based Explanations that Provide Recourse Cannot be Robust. (arXiv:2205.15834v2 [stat.ML] UPDATED)
    Different users of machine learning methods require different explanations, depending on their goals. To make machine learning accountable to society, one important goal is to get actionable options for recourse, which allow an affected user to change the decision $f(x)$ of a machine learning system by making limited changes to its input $x$. We formalize this by providing a general definition of recourse sensitivity, which needs to be instantiated with a utility function that describes which changes to the decisions are relevant to the user. This definition applies to local attribution methods, which attribute an importance weight to each input feature. It is often argued that such local attributions should be robust, in the sense that a small change in the input $x$ that is being explained, should not cause a large change in the feature weights. However, we prove formally that it is in general impossible for any single attribution method to be both recourse sensitive and robust at the same time. It follows that there must always exist counterexamples to at least one of these properties. We provide such counterexamples for several popular attribution methods, including LIME, SHAP, Integrated Gradients and SmoothGrad. Our results also cover counterfactual explanations, which may be viewed as attributions that describe a perturbation of $x$. We further discuss possible ways to work around our impossibility result, for instance by allowing the output to consist of sets with multiple attributions, and we provide sufficient conditions for specific classes of continuous functions to be recourse sensitive. Finally, we strengthen our impossibility result for the restricted case where users are only able to change a single attribute of $x$, by providing an exact characterization of the functions $f$ to which impossibility applies.  ( 2 min )
    Manifold Restricted Interventional Shapley Values. (arXiv:2301.04041v1 [stat.ML])
    Shapley values are model-agnostic methods for explaining model predictions. Many commonly used methods of computing Shapley values, known as \emph{off-manifold methods}, rely on model evaluations on out-of-distribution input samples. Consequently, explanations obtained are sensitive to model behaviour outside the data distribution, which may be irrelevant for all practical purposes. While \emph{on-manifold methods} have been proposed which do not suffer from this problem, we show that such methods are overly dependent on the input data distribution, and therefore result in unintuitive and misleading explanations. To circumvent these problems, we propose \emph{ManifoldShap}, which respects the model's domain of validity by restricting model evaluations to the data manifold. We show, theoretically and empirically, that ManifoldShap is robust to off-manifold perturbations of the model and leads to more accurate and intuitive explanations than existing state-of-the-art Shapley methods.  ( 2 min )
    Sampling random graph homomorphisms and applications to network data analysis. (arXiv:1910.09483v3 [math.PR] UPDATED)
    A graph homomorphism is a map between two graphs that preserves adjacency relations. We consider the problem of sampling a random graph homomorphism from a graph into a large network. We propose two complementary MCMC algorithms for sampling random graph homomorphisms and establish bounds on their mixing times and the concentration of their time averages. Based on our sampling algorithms, we propose a novel framework for network data analysis that circumvents some of the drawbacks in methods based on independent and neighborhood sampling. Various time averages of the MCMC trajectory give us various computable observables, including well-known ones such as homomorphism density and average clustering coefficient and their generalizations. Furthermore, we show that these network observables are stable with respect to a suitably renormalized cut distance between networks. We provide various examples and simulations demonstrating our framework through synthetic networks. We also \commHL{demonstrate the performance of} our framework on the tasks of network clustering and subgraph classification on the Facebook100 dataset and on Word Adjacency Networks of a set of classic novels.  ( 2 min )
    Calibrated simplex-mapping classification. (arXiv:2103.02926v2 [stat.ML] UPDATED)
    We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular $(n-1)$-dimensional simplex, $n$ being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains.
    Adversarial Policies Beat Superhuman Go AIs. (arXiv:2211.00241v2 [cs.LG] UPDATED)
    We attack the state-of-the-art Go-playing AI system, KataGo, by training adversarial policies that play against frozen KataGo victims. Our attack achieves a >99% win rate when KataGo uses no tree-search, and a >77% win rate when KataGo uses enough search to be superhuman. Notably, our adversaries do not win by learning to play Go better than KataGo -- in fact, our adversaries are easily beaten by human amateurs. Instead, our adversaries win by tricking KataGo into making serious blunders. Our results demonstrate that even superhuman AI systems may harbor surprising failure modes. Example games are available at https://goattack.far.ai/.
    A Unified Theory of Diversity in Ensemble Learning. (arXiv:2301.03962v1 [cs.LG])
    We present a theory of ensemble diversity, explaining the nature and effect of diversity for a wide range of supervised learning scenarios. This challenge, of understanding ensemble diversity, has been referred to as the holy grail of ensemble learning, an open question for over 30 years. Our framework reveals that diversity is in fact a hidden dimension in the bias-variance decomposition of an ensemble. In particular, we prove a family of exact bias-variance-diversity decompositions, for both classification and regression losses, e.g., squared, and cross-entropy. The framework provides a methodology to automatically identify the combiner rule enabling such a decomposition, specific to the loss. The formulation of diversity is therefore dependent on just two design choices: the loss, and the combiner. For certain choices (e.g., 0-1 loss with majority voting) the effect of diversity is necessarily dependent on the target label. Experiments illustrate how we can use our framework to understand the diversity-encouraging mechanisms of popular ensemble methods: Bagging, Boosting, and Random Forests.
    Semiparametric Regression for Spatial Data via Deep Learning. (arXiv:2301.03747v1 [stat.ML])
    In this work, we propose a deep learning-based method to perform semiparametric regression analysis for spatially dependent data. To be specific, we use a sparsely connected deep neural network with rectified linear unit (ReLU) activation function to estimate the unknown regression function that describes the relationship between response and covariates in the presence of spatial dependence. Under some mild conditions, the estimator is proven to be consistent, and the rate of convergence is determined by three factors: (1) the architecture of neural network class, (2) the smoothness and (intrinsic) dimension of true mean function, and (3) the magnitude of spatial dependence. Our method can handle well large data set owing to the stochastic gradient descent optimization algorithm. Simulation studies on synthetic data are conducted to assess the finite sample performance, the results of which indicate that the proposed method is capable of picking up the intricate relationship between response and covariates. Finally, a real data analysis is provided to demonstrate the validity and effectiveness of the proposed method.
    Markovian Sliced Wasserstein Distances: Beyond Independent Projections. (arXiv:2301.03749v1 [stat.ML])
    Sliced Wasserstein (SW) distance suffers from redundant projections due to independent uniform random projecting directions. To partially overcome the issue, max K sliced Wasserstein (Max-K-SW) distance ($K\geq 1$), seeks the best discriminative orthogonal projecting directions. Despite being able to reduce the number of projections, the metricity of Max-K-SW cannot be guaranteed in practice due to the non-optimality of the optimization. Moreover, the orthogonality constraint is also computationally expensive and might not be effective. To address the problem, we introduce a new family of SW distances, named Markovian sliced Wasserstein (MSW) distance, which imposes a first-order Markov structure on projecting directions. We discuss various members of MSW by specifying the Markov structure including the prior distribution, the transition distribution, and the burning and thinning technique. Moreover, we investigate the theoretical properties of MSW including topological properties (metricity, weak convergence, and connection to other distances), statistical properties (sample complexity, and Monte Carlo estimation error), and computational properties (computational complexity and memory complexity). Finally, we compare MSW distances with previous SW variants in various applications such as gradient flows, color transfer, and deep generative modeling to demonstrate the favorable performance of MSW.
    HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python. (arXiv:2207.03517v4 [stat.ML] UPDATED)
    Large collections of time series data are commonly organized into structures with different levels of aggregation; examples include product and geographical groupings. It is often important to ensure that the forecasts are coherent so that the predicted values at disaggregate levels add up to the aggregate forecast. The growing interest of the Machine Learning community in hierarchical forecasting systems indicates that we are in a propitious moment to ensure that scientific endeavors are grounded on sound baselines. For this reason, we put forward the HierarchicalForecast library, which contains preprocessed publicly available datasets, evaluation metrics, and a compiled set of statistical baseline models. Our Python-based reference framework aims to bridge the gap between statistical and econometric modeling, and Machine Learning forecasting research. Code and documentation are available in https://github.com/Nixtla/hierarchicalforecast.
    Bayesian Additive Main Effects and Multiplicative Interaction Models using Tensor Regression for Multi-environmental Trials. (arXiv:2301.03655v1 [stat.ML])
    We propose a Bayesian tensor regression model to accommodate the effect of multiple factors on phenotype prediction. We adopt a set of prior distributions that resolve identifiability issues that may arise between the parameters in the model. Simulation experiments show that our method out-performs previous related models and machine learning algorithms under different sample sizes and degrees of complexity. We further explore the applicability of our model by analysing real-world data related to wheat production across Ireland from 2010 to 2019. Our model performs competitively and overcomes key limitations found in other analogous approaches. Finally, we adapt a set of visualisations for the posterior distribution of the tensor effects that facilitate the identification of optimal interactions between the tensor variables whilst accounting for the uncertainty in the posterior distribution.
    Community Detection with Known, Unknown, or Partially Known Auxiliary Latent Variables. (arXiv:2301.04088v1 [cs.SI])
    Empirical observations suggest that in practice, community membership does not completely explain the dependency between the edges of an observation graph. The residual dependence of the graph edges are modeled in this paper, to first order, by auxiliary node latent variables that affect the statistics of the graph edges but carry no information about the communities of interest. We then study community detection in graphs obeying the stochastic block model and censored block model with auxiliary latent variables. We analyze the conditions for exact recovery when these auxiliary latent variables are unknown, representing unknown nuisance parameters or model mismatch. We also analyze exact recovery when these secondary latent variables have been either fully or partially revealed. Finally, we propose a semidefinite programming algorithm for recovering the desired labels when the secondary labels are either known or unknown. We show that exact recovery is possible by semidefinite programming down to the respective maximum likelihood exact recovery threshold.  ( 2 min )
  • Open

    Goal Misgeneralization in Deep Reinforcement Learning. (arXiv:2105.14111v7 [cs.LG] UPDATED)
    We study goal misgeneralization, a type of out-of-distribution generalization failure in reinforcement learning (RL). Goal misgeneralization failures occur when an RL agent retains its capabilities out-of-distribution yet pursues the wrong goal. For instance, an agent might continue to competently avoid obstacles, but navigate to the wrong place. In contrast, previous works have typically focused on capability generalization failures, where an agent fails to do anything sensible at test time. We formalize this distinction between capability and goal generalization, provide the first empirical demonstrations of goal misgeneralization, and present a partial characterization of its causes.  ( 2 min )
    Distributed Sparse Linear Regression under Communication Constraints. (arXiv:2301.04022v1 [cs.LG])
    In multiple domains, statistical tasks are performed in distributed settings, with data split among several end machines that are connected to a fusion center. In various applications, the end machines have limited bandwidth and power, and thus a tight communication budget. In this work we focus on distributed learning of a sparse linear regression model, under severe communication constraints. We propose several two round distributed schemes, whose communication per machine is sublinear in the data dimension. In our schemes, individual machines compute debiased lasso estimators, but send to the fusion center only very few values. On the theoretical front, we analyze one of these schemes and prove that with high probability it achieves exact support recovery at low signal to noise ratios, where individual machines fail to recover the support. We show in simulations that our scheme works as well as, and in some cases better, than more communication intensive approaches.  ( 2 min )
    The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems. (arXiv:2001.01258v2 [cs.LG] UPDATED)
    Methods inspired by Artificial Intelligence (AI) are starting to fundamentally change computational science and engineering through breakthrough performances on challenging problems. However, reliability and trustworthiness of such techniques is becoming a major concern. In inverse problems in imaging, the focus of this paper, there is increasing empirical evidence that methods may suffer from hallucinations, i.e., false, but realistic-looking artifacts; instability, i.e., sensitivity to perturbations in the data; and unpredictable generalization, i.e., excellent performance on some images, but significant deterioration on others. This paper presents a theoretical foundation for these phenomena. We give a mathematical framework describing how and when such effects arise in arbitrary reconstruction methods, not just AI-inspired techniques. Several of our results take the form of 'no free lunch' theorems. Specifically, we show that (i) methods that overperform on a single image can wrongly transfer details from one image to another, creating a hallucination, (ii) methods that overperform on two or more images can hallucinate or be unstable, (iii) optimizing the accuracy-stability trade-off is generally difficult, (iv) hallucinations and instabilities, if they occur, are not rare events, and may be encouraged by standard training, (v) it may be impossible to construct optimal reconstruction maps for certain problems, (vi) standard methods to improve reliability (e.g., regularization or adversarial training) may themselves lead to unstable problems. Our results trace these effects to the kernel of the forwards operator. They assert that such effects can be avoided only if information about the kernel is encoded into the reconstruction procedure. Based on this, this work aims to spur research into new ways to develop robust and reliable AI-inspired methods for inverse problems in imaging.  ( 3 min )
    VeriX: Towards Verified Explainability of Deep Neural Networks. (arXiv:2212.01051v3 [cs.LG] UPDATED)
    We present VeriX, a system for producing optimal robust explanations (La Malfa et al. 2021) for machine learning models. We build robust explanations iteratively using constraint solving techniques and a heuristic based on feature-level sensitivity ranking. We evaluate our approach on image recognition benchmarks and a real-world scenario of autonomous aircraft taxiing.  ( 2 min )
    Adversarial Policies Beat Superhuman Go AIs. (arXiv:2211.00241v2 [cs.LG] UPDATED)
    We attack the state-of-the-art Go-playing AI system, KataGo, by training adversarial policies that play against frozen KataGo victims. Our attack achieves a >99% win rate when KataGo uses no tree-search, and a >77% win rate when KataGo uses enough search to be superhuman. Notably, our adversaries do not win by learning to play Go better than KataGo -- in fact, our adversaries are easily beaten by human amateurs. Instead, our adversaries win by tricking KataGo into making serious blunders. Our results demonstrate that even superhuman AI systems may harbor surprising failure modes. Example games are available at https://goattack.far.ai/.  ( 2 min )
    Structural risk minimization for quantum linear classifiers. (arXiv:2105.05566v3 [quant-ph] UPDATED)
    Quantum machine learning (QML) models based on parameterized quantum circuits are often highlighted as candidates for quantum computing's near-term ``killer application''. However, the understanding of the empirical and generalization performance of these models is still in its infancy. In this paper we study how to balance between training accuracy and generalization performance (also called structural risk minimization) for two prominent QML models introduced by Havl\'{i}\v{c}ek et al. (Nature, 2019), and Schuld and Killoran (PRL, 2019). Firstly, using relationships to well understood classical models, we prove that two model parameters -- i.e., the dimension of the sum of the images and the Frobenius norm of the observables used by the model -- closely control the models' complexity and therefore its generalization performance. Secondly, using ideas inspired by process tomography, we prove that these model parameters also closely control the models' ability to capture correlations in sets of training examples. In summary, our results give rise to new options for structural risk minimization for QML models.  ( 2 min )
    Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy. (arXiv:2203.14827v2 [cs.LG] UPDATED)
    Predictions of hydrologic variables across the entire water cycle have significant value for water resource management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly-insurmountable performance in modeling rainfall-runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here we show that differentiable, learnable, process-based models (called {\delta} models here) can approach the performance level of LSTM for the intensively-observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model modules. Without using an ensemble or post-processor, {\delta} models can obtain a median Nash Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing dataset, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing dataset, the difference is even smaller: 0.715 vs. 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, e.g., soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.  ( 2 min )
    Partial order: Finding Consensus among Uncertain Feature Attributions. (arXiv:2110.13369v2 [cs.LG] UPDATED)
    Post-hoc feature attribution methods are progressively being employed to explain decisions of complex machine learning models. Yet, it is possible for practitioners to obtain a diversity of models that provide very different explanations to the same prediction, making it hard to derive insight from them. In this work, instead of aiming at reducing the under-specification of model explanations, we fully embrace it and extract logical statements about feature attributions that are consistent across multiple models with good performance. We show that a partial order of feature importance arises from this methodology enabling more nuanced explanations by allowing pairs of features to be incomparable when there is no consensus on their relative importance. We prove that every relation among features present in these partial order also holds in the rankings provided by existing approaches. Finally, we present use cases on three datasets where partial orders allow one to extract knowledge from models despite their under-specification.  ( 2 min )
    Combinatorial Pure Exploration of Causal Bandits. (arXiv:2206.07883v2 [cs.LG] UPDATED)
    The combinatorial pure exploration of causal bandits is the following online learning task: given a causal graph with unknown causal inference distributions, in each round we choose a subset of variables to intervene or do no intervention, and observe the random outcomes of all random variables, with the goal that using as few rounds as possible, we can output an intervention that gives the best (or almost best) expected outcome on the reward variable $Y$ with probability at least $1-\delta$, where $\delta$ is a given confidence level. We provide the first gap-dependent and fully adaptive pure exploration algorithms on two types of causal models -- the binary generalized linear model (BGLM) and general graphs. For BGLM, our algorithm is the first to be designed specifically for this setting and achieves polynomial sample complexity, while all existing algorithms for general graphs have either sample complexity exponential to the graph size or some unreasonable assumptions. For general graphs, our algorithm provides a significant improvement on sample complexity, and it nearly matches the lower bound we prove. Our algorithms achieve such improvement by a novel integration of prior causal bandit algorithms and prior adaptive pure exploration algorithms, the former of which utilize the rich observational feedback in causal bandits but are not adaptive to reward gaps, while the latter of which have the issue in reverse.  ( 2 min )
    Differentiable modeling to unify machine learning and physical models and advance Geosciences. (arXiv:2301.04027v1 [cs.LG])
    Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift. For decades, PBM offered benefits in interpretability and physical consistency but struggled to efficiently leverage large datasets. ML methods, especially deep networks, presented strong predictive skills yet lacked the ability to answer specific scientific questions. While various methods have been proposed for ML-physics integration, an important underlying theme -- differentiable modeling -- is not sufficiently recognized. Here we outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG). "Differentiable" refers to accurately and efficiently calculating gradients with respect to model variables, critically enabling the learning of high-dimensional unknown relationships. DG refers to a range of methods connecting varying amounts of prior knowledge to neural networks and training them together, capturing a different scope than physics-guided machine learning and emphasizing first principles. Preliminary evidence suggests DG offers better interpretability and causality than ML, improved generalizability and extrapolation capability, and strong potential for knowledge discovery, while approaching the performance of purely data-driven ML. DG models require less training data while scaling favorably in performance and efficiency with increasing amounts of data. With DG, geoscientists may be better able to frame and investigate questions, test hypotheses, and discover unrecognized linkages.  ( 2 min )
    ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints. (arXiv:2202.11271v3 [cs.RO] UPDATED)
    Robotic navigation has been approached as a problem of 3D reconstruction and planning, as well as an end-to-end learning problem. However, long-range navigation requires both planning and reasoning about local traversability, as well as being able to utilize general knowledge about global geography, in the form of a roadmap, GPS, or other side information providing important cues. In this work, we propose an approach that integrates learning and planning, and can utilize side information such as schematic roadmaps, satellite maps and GPS coordinates as a planning heuristic, without relying on them being accurate. Our method, ViKiNG, incorporates a local traversability model, which looks at the robot's current camera observation and a potential subgoal to infer how easily that subgoal can be reached, as well as a heuristic model, which looks at overhead maps for hints and attempts to evaluate the appropriateness of these subgoals in order to reach the goal. These models are used by a heuristic planner to identify the best waypoint in order to reach the final destination. Our method performs no explicit geometric reconstruction, utilizing only a topological representation of the environment. Despite having never seen trajectories longer than 80 meters in its training dataset, ViKiNG can leverage its image-based learned controller and goal-directed heuristic to navigate to goals up to 3 kilometers away in previously unseen environments, and exhibit complex behaviors such as probing potential paths and backtracking when they are found to be non-viable. ViKiNG is also robust to unreliable maps and GPS, since the low-level controller ultimately makes decisions based on egocentric image observations, using maps only as planning heuristics. For videos of our experiments, please check out our project page https://sites.google.com/view/viking-release.  ( 3 min )
    Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage. (arXiv:2107.06226v4 [cs.LG] UPDATED)
    We study model-based offline Reinforcement Learning with general function approximation without a full coverage assumption on the offline data distribution. We present an algorithm named Constrained Pessimistic Policy Optimization (CPPO)which leverages a general function class and uses a constraint over the model class to encode pessimism. Under the assumption that the ground truth model belongs to our function class (i.e., realizability in the function class), CPPO has a PAC guarantee with offline data only providing partial coverage, i.e., it can learn a policy that competes against any policy that is covered by the offline data. We then demonstrate that this algorithmic framework can be applied to many specialized Markov Decision Processes where additional structural assumptions can further refine the concept of partial coverage. Two notable examples are: (1) low-rank MDP with representation learning where the partial coverage condition is defined using a relative condition number measured by the unknown ground truth feature representation; (2) factored MDP where the partial coverage condition is defined using density ratio based concentrability coefficients associated with individual factors.  ( 2 min )
    Mastering Diverse Domains through World Models. (arXiv:2301.04104v1 [cs.AI])
    General intelligence requires solving tasks across many domains. Current reinforcement learning algorithms carry this potential but are held back by the resources and knowledge required to tune them for new tasks. We present DreamerV3, a general and scalable algorithm based on world models that outperforms previous approaches across a wide range of domains with fixed hyperparameters. These domains include continuous and discrete actions, visual and low-dimensional inputs, 2D and 3D worlds, different data budgets, reward frequencies, and reward scales. We observe favorable scaling properties of DreamerV3, with larger models directly translating to higher data-efficiency and final performance. Applied out of the box, DreamerV3 is the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula, a long-standing challenge in artificial intelligence. Our general algorithm makes reinforcement learning broadly applicable and allows scaling to hard decision making problems.  ( 2 min )
    Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum. (arXiv:2206.10057v2 [cs.LG] UPDATED)
    Despite considerable advances in deep reinforcement learning, it has been shown to be highly vulnerable to adversarial perturbations to state observations. Recent efforts that have attempted to improve adversarial robustness of reinforcement learning can nevertheless tolerate only very small perturbations, and remain fragile as perturbation size increases. We propose Bootstrapped Opportunistic Adversarial Curriculum Learning (BCL), a novel flexible adversarial curriculum learning framework for robust reinforcement learning. Our framework combines two ideas: conservatively bootstrapping each curriculum phase with highest quality solutions obtained from multiple runs of the previous phase, and opportunistically skipping forward in the curriculum. In our experiments we show that the proposed BCL framework enables dramatic improvements in robustness of learned policies to adversarial perturbations. The greatest improvement is for Pong, where our framework yields robustness to perturbations of up to 25/255; in contrast, the best existing approach can only tolerate adversarial noise up to 5/255. Our code is available at: https://github.com/jlwu002/BCL.  ( 2 min )
    IronForge: An Open, Secure, Fair, Decentralized Federated Learning. (arXiv:2301.04006v1 [cs.LG])
    Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose \textsc{IronForge}, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. \textsc{IronForge} runs in a public and open network, and launches a fair incentive mechanism by enabling state consistency in the DAG, so that the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of \textsc{IronForge}. Experimental results based on a newly developed testbed FLSim highlight the superiority of \textsc{IronForge} to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, \textsc{IronForge} is the first secure and fully decentralized FL framework that can be applied in open networks with realistic network and training settings.  ( 2 min )
    Vision Transformers Are Good Mask Auto-Labelers. (arXiv:2301.03992v1 [cs.CV])
    We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels.We show that Vision Transformers are good mask auto-labelers. Our method significantly reduces the gap between auto-labeling and human annotation regarding mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4\% performance of fully supervised models. The best model achieves 44.1\% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by significant margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations.  ( 2 min )
    FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability. (arXiv:2208.07912v2 [cs.LG] UPDATED)
    We present FOLD-SE, an efficient, explainable machine learning algorithm for classification tasks given tabular data containing numerical and categorical values. FOLD-SE generates a set of default rules-essentially a stratified normal logic program-as an (explainable) trained model. Explainability provided by FOLD-SE is scalable, meaning that regardless of the size of the dataset, the number of learned rules and learned literals stay quite small while good accuracy in classification is maintained. A model with smaller number of rules and literals is easier to understand for human beings. FOLD-SE is competitive with state-of-the-art machine learning algorithms such as XGBoost and Multi-Layer Perceptrons (MLP) wrt accuracy of prediction. However, unlike XGBoost and MLP, the FOLD-SE algorithm is explainable. The FOLD-SE algorithm builds upon our earlier work on developing the explainable FOLD-R++ machine learning algorithm for binary classification and inherits all of its positive features. Thus, pre-processing of the dataset, using techniques such as one-hot encoding, is not needed. Like FOLD-R++, FOLD-SE uses prefix sum to speed up computations resulting in FOLD-SE being an order of magnitude faster than XGBoost and MLP in execution speed. The FOLD-SE algorithm outperforms FOLD-R++ as well as other rule-learning algorithms such as RIPPER in efficiency, performance and scalability, especially for large datasets. A major reason for scalable explainability of FOLD-SE is the use of a literal selection heuristics based on Gini Impurity, as opposed to Information Gain used in FOLD-R++. A multi-category classification version of FOLD-SE is also presented.  ( 2 min )
    Smart Application for Fall Detection Using Wearable ECG & Accelerometer Sensors. (arXiv:2207.00008v2 [cs.HC] UPDATED)
    Timely and reliable detection of falls is a large and rapidly growing field of research due to the medical and financial demand of caring for a constantly growing elderly population. Within the past 2 decades, the availability of high-quality hardware (high-quality sensors and AI microchips) and software (machine learning algorithms) technologies has served as a catalyst for this research by giving developers the capabilities to develop such systems. This study developed multiple application components in order to investigate the development challenges and choices for fall detection systems, and provide materials for future research. The smart application developed using this methodology was validated by the results from fall detection modelling experiments and model mobile deployment. The best performing model overall was the ResNet152 on a standardised, and shuffled dataset with a 2s window size which achieved 92.8% AUC, 87.28% sensitivity, and 98.33% specificity. Given these results it is evident that accelerometer and ECG sensors are beneficial for fall detection, and allow for the discrimination between falls and other activities. This study leaves a significant amount of room for improvement due to weaknesses identified in the resultant dataset. These improvements include using a labelling protocol for the critical phase of a fall, increasing the number of dataset samples, improving the test subject representation, and experimenting with frequency domain preprocessing.  ( 2 min )
    Attribution-based Explanations that Provide Recourse Cannot be Robust. (arXiv:2205.15834v2 [stat.ML] UPDATED)
    Different users of machine learning methods require different explanations, depending on their goals. To make machine learning accountable to society, one important goal is to get actionable options for recourse, which allow an affected user to change the decision $f(x)$ of a machine learning system by making limited changes to its input $x$. We formalize this by providing a general definition of recourse sensitivity, which needs to be instantiated with a utility function that describes which changes to the decisions are relevant to the user. This definition applies to local attribution methods, which attribute an importance weight to each input feature. It is often argued that such local attributions should be robust, in the sense that a small change in the input $x$ that is being explained, should not cause a large change in the feature weights. However, we prove formally that it is in general impossible for any single attribution method to be both recourse sensitive and robust at the same time. It follows that there must always exist counterexamples to at least one of these properties. We provide such counterexamples for several popular attribution methods, including LIME, SHAP, Integrated Gradients and SmoothGrad. Our results also cover counterfactual explanations, which may be viewed as attributions that describe a perturbation of $x$. We further discuss possible ways to work around our impossibility result, for instance by allowing the output to consist of sets with multiple attributions, and we provide sufficient conditions for specific classes of continuous functions to be recourse sensitive. Finally, we strengthen our impossibility result for the restricted case where users are only able to change a single attribute of $x$, by providing an exact characterization of the functions $f$ to which impossibility applies.  ( 2 min )
    Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice. (arXiv:2210.03709v3 [cs.HC] UPDATED)
    Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems. However, we know little about industry practitioners' current practices and challenges around user-engaged auditing, nor what opportunities exist for them to better leverage such approaches in practice. To investigate, we conducted a series of interviews and iterative co-design activities with practitioners who employ user-engaged auditing approaches in their work. Our findings reveal several challenges practitioners face in appropriately recruiting and incentivizing user auditors, scaffolding user audits, and deriving actionable insights from user-engaged audit reports. Furthermore, practitioners shared organizational obstacles to user-engaged auditing, surfacing a complex relationship between practitioners and user auditors. Based on these findings, we discuss opportunities for future HCI research to help realize the potential (and the mitigate risks) of user-engaged auditing in industry practice.  ( 2 min )
    ELIAS: End-to-End Learning to Index and Search in Large Output Spaces. (arXiv:2210.08410v2 [cs.LG] UPDATED)
    Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output choices. A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search. Existing methods initialize the tree index by clustering the label space into a few mutually exclusive clusters based on pre-defined features and keep it fixed throughout the training procedure. This approach results in a sub-optimal indexing structure over the label space and limits the search performance to the quality of choices made during the initialization of the index. In this paper, we propose a novel method ELIAS which relaxes the tree-based index to a specialized weighted graph-based index which is learned end-to-end with the final task objective. More specifically, ELIAS models the discrete cluster-to-label assignments in the existing tree-based index as soft learnable parameters that are learned jointly with the rest of the ML model. ELIAS achieves state-of-the-art performance on several large-scale extreme classification benchmarks with millions of labels. In particular, ELIAS can be up to 2.5% better at precision@1 and up to 4% better at recall@100 than existing XMC methods. A PyTorch implementation of ELIAS along with other resources is available at https://github.com/nilesh2797/ELIAS.
    Generating Accurate and Faithful Discharge Instructions: Task, Dataset, and Model. (arXiv:2210.12777v2 [cs.CL] UPDATED)
    The "Patient Instruction" (PI), known as "Discharge Instruction", which contains critical instructional information provided both to carers and to the patient at the time of discharge, is essential for the patient to manage their condition outside hospital. An accurate and easy-to-follow PI can improve the self-management of patients which can in turn reduce hospital readmission rates. However, writing an appropriate PI can be extremely time-consuming for physicians, and is subject to being incomplete or error-prone for (potentially overworked) physicians. Therefore, we propose a new task that can provide an objective means of avoiding incompleteness, while reducing clinical workload: the automatic generation of the PI, which is imagined as being a document that the clinician can review, modify, and approve as necessary (rather than taking the human "out of the loop"). We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge. Finally, it refines the retrieved working experience and reasoned medical knowledge to extract useful information, which is used to generate the PI for previously-unseen patient according to their health records during hospitalization. Our experiments show that, using our method, the performance of five different models can be substantially boosted across all metrics, with up to 20%, 11%, and 19% relative improvements in BLEU-4, ROUGE-L, and METEOR, respectively. Meanwhile, we show results from human evaluations to measure the effectiveness in terms of its usefulness for clinical practice. The code is available at https://github.com/AI-in-Hospitals/Patient-Instructions
    Bias-Aware Face Mask Detection Dataset. (arXiv:2211.01207v3 [cs.CV] UPDATED)
    In December 2019, a novel coronavirus (COVID-19) spread so quickly around the world that many countries had to set mandatory face mask rules in public areas to reduce the transmission of the virus. To monitor public adherence, researchers aimed to rapidly develop efficient systems that can detect faces with masks automatically. However, the lack of representative and novel datasets proved to be the biggest challenge. Early attempts to collect face mask datasets did not account for potential race, gender, and age biases. Therefore, the resulting models show inherent biases toward specific race groups, such as Asian or Caucasian. In this work, we present a novel face mask detection dataset that contains images posted on Twitter during the pandemic from around the world. Unlike previous datasets, the proposed Bias-Aware Face Mask Detection (BAFMD) dataset contains more images from underrepresented race and age groups to mitigate the problem for the face mask detection task. We perform experiments to investigate potential biases in widely used face mask detection datasets and illustrate that the BAFMD dataset yields models with better performance and generalization ability. The dataset is publicly available at https://github.com/Alpkant/BAFMD.
    Stars: Tera-Scale Graph Building for Clustering and Graph Learning. (arXiv:2212.02635v2 [cs.LG] UPDATED)
    A fundamental procedure in the analysis of massive datasets is the construction of similarity graphs. Such graphs play a key role for many downstream tasks, including clustering, classification, graph learning, and nearest neighbor search. For these tasks, it is critical to build graphs which are sparse yet still representative of the underlying data. The benefits of sparsity are twofold: firstly, constructing dense graphs is infeasible in practice for large datasets, and secondly, the runtime of downstream tasks is directly influenced by the sparsity of the similarity graph. In this work, we present $\textit{Stars}$: a highly scalable method for building extremely sparse graphs via two-hop spanners, which are graphs where similar points are connected by a path of length at most two. Stars can construct two-hop spanners with significantly fewer similarity comparisons, which are a major bottleneck for learning based models where comparisons are expensive to evaluate. Theoretically, we demonstrate that Stars builds a graph in nearly-linear time, where approximate nearest neighbors are contained within two-hop neighborhoods. In practice, we have deployed Stars for multiple data sets allowing for graph building at the $\textit{Tera-Scale}$, i.e., for graphs with tens of trillions of edges. We evaluate the performance of Stars for clustering and graph learning, and demonstrate 10~1000-fold improvements in pairwise similarity comparisons compared to different baselines, and 2~10-fold improvement in running time without quality loss.
    Dynamic Tensor Product Regression. (arXiv:2210.03961v2 [cs.DS] UPDATED)
    In this work, we initiate the study of \emph{Dynamic Tensor Product Regression}. One has matrices $A_1\in \mathbb{R}^{n_1\times d_1},\ldots,A_q\in \mathbb{R}^{n_q\times d_q}$ and a label vector $b\in \mathbb{R}^{n_1\ldots n_q}$, and the goal is to solve the regression problem with the design matrix $A$ being the tensor product of the matrices $A_1, A_2, \dots, A_q$ i.e. $\min_{x\in \mathbb{R}^{d_1\ldots d_q}}~\|(A_1\otimes \ldots\otimes A_q)x-b\|_2$. At each time step, one matrix $A_i$ receives a sparse change, and the goal is to maintain a sketch of the tensor product $A_1\otimes\ldots \otimes A_q$ so that the regression solution can be updated quickly. Recomputing the solution from scratch for each round is very slow and so it is important to develop algorithms which can quickly update the solution with the new design matrix. Our main result is a dynamic tree data structure where any update to a single matrix can be propagated quickly throughout the tree. We show that our data structure can be used to solve dynamic versions of not only Tensor Product Regression, but also Tensor Product Spline regression (which is a generalization of ridge regression) and for maintaining Low Rank Approximations for the tensor product.
    How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?. (arXiv:2102.06560v6 [cs.LG] UPDATED)
    Anomaly detection is the process of identifying unexpected events or ab-normalities in data, and it has been applied in many different areas such as system monitoring, fraud detection, healthcare, intrusion detection, etc. Providing real-time, lightweight, and proactive anomaly detection for time series with neither human intervention nor domain knowledge could be highly valuable since it reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous event occurs. To our knowledge, RePAD (Real-time Proactive Anomaly Detection algorithm) is a generic approach with all above-mentioned features. To achieve real-time and lightweight detection, RePAD utilizes Long Short-Term Memory (LSTM) to detect whether or not each upcoming data point is anomalous based on short-term historical data points. However, it is unclear that how different amounts of historical data points affect the performance of RePAD. Therefore, in this paper, we investigate the impact of different amounts of historical data on RePAD by introducing a set of performance metrics that cover novel detection accuracy measures, time efficiency, readiness, and resource consumption, etc. Empirical experiments based on real-world time series datasets are conducted to evaluate RePAD in different scenarios, and the experimental results are presented and discussed.
    Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation. (arXiv:2109.06308v3 [cs.CL] UPDATED)
    Despite strong performance in many sequence-to-sequence tasks, autoregressive models trained with maximum likelihood estimation suffer from exposure bias, i.e. the discrepancy between the ground-truth prefixes used during training and the model-generated prefixes used at inference time. Scheduled sampling is a simple and empirically successful approach which addresses this issue by incorporating model-generated prefixes into training. However, it has been argued that it is an inconsistent training objective leading to models ignoring the prefixes altogether. In this paper, we conduct systematic experiments and find that scheduled sampling, while it ameliorates exposure bias by increasing model reliance on the input sequence, worsens performance when the prefix at inference time is correct, a form of catastrophic forgetting. We propose to use Elastic Weight Consolidation to better balance mitigating exposure bias with retaining performance. Experiments on four IWSLT'14 and WMT'14 translation datasets demonstrate that our approach alleviates catastrophic forgetting and significantly outperforms maximum likelihood estimation and scheduled sampling baselines.
    ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies. (arXiv:2110.10423v3 [cs.LG] UPDATED)
    Designing neural architectures requires immense manual efforts. This has promoted the development of neural architecture search (NAS) to automate the design. While previous NAS methods achieve promising results but run slowly, zero-cost proxies run extremely fast but are less promising. Therefore, it is of great potential to accelerate NAS via those zero-cost proxies. The existing method has two limitations, which are unforeseeable reliability and one-shot usage. To address the limitations, we present ProxyBO, an efficient Bayesian optimization (BO) framework that utilizes the zero-cost proxies to accelerate neural architecture search. We apply the generalization ability measurement to estimate the fitness of proxies on the task during each iteration and design a novel acquisition function to combine BO with zero-cost proxies based on their dynamic influence. Extensive empirical studies show that ProxyBO consistently outperforms competitive baselines on five tasks from three public benchmarks. Concretely, ProxyBO achieves up to 5.41x and 3.86x speedups over the state-of-the-art approaches REA and BRP-NAS.
    Adaptive Data Debiasing through Bounded Exploration. (arXiv:2110.13054v2 [cs.LG] UPDATED)
    Biases in existing datasets used to train algorithmic decision rules can raise ethical and economic concerns due to the resulting disparate treatment of different groups. We propose an algorithm for sequentially debiasing such datasets through adaptive and bounded exploration in a classification problem with costly and censored feedback. Exploration in this context means that at times, and to a judiciously-chosen extent, the decision maker deviates from its (current) loss-minimizing rule, and instead accepts some individuals that would otherwise be rejected, so as to reduce statistical data biases. Our proposed algorithm includes parameters that can be used to balance between the ultimate goal of removing data biases -- which will in turn lead to more accurate and fair decisions, and the exploration risks incurred to achieve this goal. We analytically show that such exploration can help debias data in certain distributions. We further investigate how fairness criteria can work in conjunction with our data debiasing algorithm. We illustrate the performance of our algorithm using experiments on synthetic and real-world datasets.
    HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python. (arXiv:2207.03517v4 [stat.ML] UPDATED)
    Large collections of time series data are commonly organized into structures with different levels of aggregation; examples include product and geographical groupings. It is often important to ensure that the forecasts are coherent so that the predicted values at disaggregate levels add up to the aggregate forecast. The growing interest of the Machine Learning community in hierarchical forecasting systems indicates that we are in a propitious moment to ensure that scientific endeavors are grounded on sound baselines. For this reason, we put forward the HierarchicalForecast library, which contains preprocessed publicly available datasets, evaluation metrics, and a compiled set of statistical baseline models. Our Python-based reference framework aims to bridge the gap between statistical and econometric modeling, and Machine Learning forecasting research. Code and documentation are available in https://github.com/Nixtla/hierarchicalforecast.
    AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Images. (arXiv:2208.11012v3 [cs.CV] UPDATED)
    In order to classify Japanese animation-style character faces, this paper attempts to delve further into the many models currently available, including InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNet, employing transfer learning. This paper demonstrates that EfficientNet-B7, which achieves a top-1 accuracy of 85.08%, has the highest accuracy rate. MobileNetV2, which achieves a less accurate result with a top-1 accuracy of 81.92%, benefits from a significantly faster inference time and fewer required parameters. However, from the experiment, MobileNet-V2 is prone to overfitting; EfficienNet-B0 fixed the overfitting issue but with a cost of a little slower in inference time than MobileNet-V2 but a little more accurate result, top-1 accuracy of 83.46%. This paper also uses a few-shot learning architecture called Prototypical Networks, which offers an adequate substitute for conventional transfer learning techniques.
    MixGen: A New Multi-Modal Data Augmentation. (arXiv:2206.08358v3 [cs.CV] UPDATED)
    Data augmentation is a necessity to enhance data efficiency in deep learning. For vision-language pre-training, data is only augmented either for images or for text in previous works. In this paper, we present MixGen: a joint data augmentation for vision-language representation learning to further improve data efficiency. It generates new image-text pairs with semantic relationships preserved by interpolating images and concatenating text. It's simple, and can be plug-and-played into existing pipelines. We evaluate MixGen on four architectures, including CLIP, ViLT, ALBEF and TCL, across five downstream vision-language tasks to show its versatility and effectiveness. For example, adding MixGen in ALBEF pre-training leads to absolute performance improvements on downstream tasks: image-text retrieval (+6.2% on COCO fine-tuned and +5.3% on Flicker30K zero-shot), visual grounding (+0.9% on RefCOCO+), visual reasoning (+$0.9% on NLVR2), visual question answering (+0.3% on VQA2.0), and visual entailment (+0.4% on SNLI-VE).
    Toward a `Standard Model' of Machine Learning. (arXiv:2108.07783v2 [cs.LG] UPDATED)
    Machine learning (ML) is about computational methods that enable machines to learn concepts from experience. In handling a wide variety of experience ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interaction in an ever-growing spectrum of tasks, contemporary ML/AI (artificial intelligence) research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly focused methods also make standardized, composable, and reusable development of ML approaches difficult, and preclude the opportunity to build AI agents that panoramically learn from all types of experience. This article presents a standardized ML formalism, in particular a `standard equation' of the learning objective, that offers a unifying understanding of many important ML algorithms in the supervised, unsupervised, knowledge-constrained, reinforcement, adversarial, and online learning paradigms, respectively -- those diverse algorithms are encompassed as special cases due to different choices of modeling components. The framework also provides guidance for mechanical design of new ML approaches and serves as a promising vehicle toward panoramic machine learning with all experience.
    Discriminator-Guided Model-Based Offline Imitation Learning. (arXiv:2207.00244v3 [cs.LG] UPDATED)
    Offline imitation learning (IL) is a powerful method to solve decision-making problems from expert demonstrations without reward labels. Existing offline IL methods suffer from severe performance degeneration under limited expert data. Including a learned dynamics model can potentially improve the state-action space coverage of expert data, however, it also faces challenging issues like model approximation/generalization errors and suboptimality of rollout data. In this paper, we propose the Discriminator-guided Model-based offline Imitation Learning (DMIL) framework, which introduces a discriminator to simultaneously distinguish the dynamics correctness and suboptimality of model rollout data against real expert demonstrations. DMIL adopts a novel cooperative-yet-adversarial learning strategy, which uses the discriminator to guide and couple the learning process of the policy and dynamics model, resulting in improved model performance and robustness. Our framework can also be extended to the case when demonstrations contain a large proportion of suboptimal data. Experimental results show that DMIL and its extension achieve superior performance and robustness compared to state-of-the-art offline IL methods under small datasets.
    A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition. (arXiv:2203.09477v2 [eess.SP] UPDATED)
    Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four decomposition methods are employed to disassemble the EEG signals into components of different complexity. Instead of handcraft features, the CNNs in this framework directly learn from the decomposed components. In addition, a component-specific batch normalization layer is employed to reduce subject variability. Moreover, we employ two ensemble modes to integrate the outputs of all CNNs, comprehensively exploiting the diverse information of the decomposed components. Against the challenging cross-subject driver fatigue recognition task, the models under the framework all showed superior performance to the strong baselines. Specifically, the performance of different decomposition methods and ensemble modes was further compared. The results indicated that discrete wavelet transform-based ensemble CNN achieved the highest average classification accuracy of 83.48% among the compared methods. The proposed framework can be extended to any CNN architecture and be applied to any EEG-related tasks, opening the possibility of extracting more beneficial features from complex EEG data.
    Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. (arXiv:2010.11411v3 [cs.CY] UPDATED)
    Recently, there have been increasing calls for computer science curricula to complement existing technical training with topics related to Fairness, Accountability, Transparency, and Ethics. In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation. This paper presents an early use of our approach in a college-level computer science course. Through an in-class activity, we report empirical data for the initial effectiveness of our approach. Our results suggest that the use of the Value Cards toolkit can improve students' understanding of both the technical definitions and trade-offs of performance metrics and apply them in real-world contexts, help them recognize the significance of considering diverse social values in the development of deployment of algorithmic systems, and enable them to communicate, negotiate and synthesize the perspectives of diverse stakeholders. Our study also demonstrates a number of caveats we need to consider when using the different variants of the Value Cards toolkit. Finally, we discuss the challenges as well as future applications of our approach.
    Convergence of Deep ReLU Networks. (arXiv:2107.12530v3 [cs.LG] UPDATED)
    We explore convergence of deep neural networks with the popular ReLU activation function, as the depth of the networks tends to infinity. To this end, we introduce the notion of activation domains and activation matrices of a ReLU network. By replacing applications of the ReLU activation function by multiplications with activation matrices on activation domains, we obtain an explicit expression of the ReLU network. We then identify the convergence of the ReLU networks as convergence of a class of infinite products of matrices. Sufficient and necessary conditions for convergence of these infinite products of matrices are studied. As a result, we establish necessary conditions for ReLU networks to converge that the sequence of weight matrices converges to the identity matrix and the sequence of the bias vectors converges to zero as the depth of ReLU networks increases to infinity. Moreover, we obtain sufficient conditions in terms of the weight matrices and bias vectors at hidden layers for pointwise convergence of deep ReLU networks. These results provide mathematical insights to the design strategy of the well-known deep residual networks in image classification.
    Reconstructing Sparse Multiplex Networks with Application to Covert Networks. (arXiv:2208.01739v3 [cs.SI] UPDATED)
    Network structure provides critical information for understanding the dynamic behavior of networks. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation-Maximization-Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.
    Predictive Process Model Monitoring using Recurrent Neural Networks. (arXiv:2011.02819v3 [cs.LG] UPDATED)
    The field of predictive process monitoring focuses on case-level models to predict a single specific outcome such as a particular objective, (remaining) time, or next activity/remaining sequence. Recently, a longer-horizon, model-wide approach has been proposed in the form of process model forecasting, which predicts the future state of a whole process model through the forecasting of all activity-to-activity relations at once using time series forecasting. This paper introduces the concept of \emph{predictive process model monitoring} which sits in the middle of both predictive process monitoring and process model forecasting. Concretely, by modelling a process model as a set of constraints being present between activities over time, we can capture more detailed information between activities compared to process model forecasting, while being compatible with typical predictive process monitoring objectives which are often expressed in the same language as these constraints. To achieve this, Processes-As-Movies (PAM) is introduced, i.e., a novel technique capable of jointly mining and predicting declarative process constraints between activities in various windows of a process' execution. PAM predicts what declarative rules hold for a trace (objective-based), which also supports the prediction of all constraints together as a process model (model-based). Various recurrent neural network topologies inspired by video analysis tailored to temporal high-dimensional input are used to model the process model evolution with windows as time steps, including encoder-decoder long short-term memory networks, and convolutional long short-term memory networks. Results obtained over real-life event logs show that these topologies are effective in terms of predictive accuracy and precision.
    Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. (arXiv:2205.06922v2 [cs.HC] UPDATED)
    Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML practitioners actually use these toolkits in practice. In this paper, we conducted the first in-depth empirical exploration of how industry practitioners (try to) work with existing fairness toolkits. In particular, we conducted think-aloud interviews to understand how participants learn about and use fairness toolkits, and explored the generality of our findings through an anonymous online survey. We identified several opportunities for fairness toolkits to better address practitioner needs and scaffold them in using toolkits effectively and responsibly. Based on these findings, we highlight implications for the design of future open-source fairness toolkits that can support practitioners in better contextualizing, communicating, and collaborating around ML fairness efforts.
    Sampling random graph homomorphisms and applications to network data analysis. (arXiv:1910.09483v3 [math.PR] UPDATED)
    A graph homomorphism is a map between two graphs that preserves adjacency relations. We consider the problem of sampling a random graph homomorphism from a graph into a large network. We propose two complementary MCMC algorithms for sampling random graph homomorphisms and establish bounds on their mixing times and the concentration of their time averages. Based on our sampling algorithms, we propose a novel framework for network data analysis that circumvents some of the drawbacks in methods based on independent and neighborhood sampling. Various time averages of the MCMC trajectory give us various computable observables, including well-known ones such as homomorphism density and average clustering coefficient and their generalizations. Furthermore, we show that these network observables are stable with respect to a suitably renormalized cut distance between networks. We provide various examples and simulations demonstrating our framework through synthetic networks. We also \commHL{demonstrate the performance of} our framework on the tasks of network clustering and subgraph classification on the Facebook100 dataset and on Word Adjacency Networks of a set of classic novels.
    Calibrated simplex-mapping classification. (arXiv:2103.02926v2 [stat.ML] UPDATED)
    We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular $(n-1)$-dimensional simplex, $n$ being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains.
    Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness. (arXiv:2201.01409v2 [cs.LG] UPDATED)
    Federated learning (FL) is a distributed learning paradigm that preserves users' data privacy while leveraging the entire dataset of all participants. In FL, multiple models are trained independently on the clients and aggregated centrally to update a global model in an iterative process. Although this approach is excellent at preserving privacy, FL still suffers from quality issues such as attacks or byzantine faults. Recent attempts have been made to address such quality challenges on the robust aggregation techniques for FL. However, the effectiveness of state-of-the-art (SOTA) robust FL techniques is still unclear and lacks a comprehensive study. Therefore, to better understand the current quality status and challenges of these SOTA FL techniques in the presence of attacks and faults, we perform a large-scale empirical study to investigate the SOTA FL's quality from multiple angles of attacks, simulated faults (via mutation operators), and aggregation (defense) methods. In particular, we study FL's performance on the image classification tasks and use DNNs as our model type. Furthermore, we perform our study on two generic image datasets and one real-world federated medical image dataset. We also investigate the effect of the proportion of affected clients and the dataset distribution factors on the robustness of FL. After a large-scale analysis with 496 configurations, we find that most mutators on each user have a negligible effect on the final model in the generic datasets, and only one of them is effective in the medical dataset. Furthermore, we show that model poisoning attacks are more effective than data poisoning attacks. Moreover, choosing the most robust FL aggregator depends on the attacks and datasets. Finally, we illustrate that a simple ensemble of aggregators achieves a more robust solution than any single aggregator and is the best choice in 75% of the cases.
    BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithm. (arXiv:2301.04120v1 [cs.NE])
    The performance of speech-processing models is heavily influenced by the speech corpus that is used for training and evaluation. In this study, we propose BAlanced Script PROducer (BASPRO) system, which can automatically construct a phonetically balanced and rich set of Chinese sentences for collecting Mandarin Chinese speech data. First, we used pretrained natural language processing systems to extract ten-character candidate sentences from a large corpus of Chinese news texts. Then, we applied a genetic algorithm-based method to select 20 phonetically balanced sentence sets, each containing 20 sentences, from the candidate sentences. Using BASPRO, we obtained a recording script called TMNews, which contains 400 ten-character sentences. TMNews covers 84% of the syllables used in the real world. Moreover, the syllable distribution has 0.96 cosine similarity to the real-world syllable distribution. We converted the script into a speech corpus using two text-to-speech systems. Using the designed speech corpus, we tested the performances of speech enhancement (SE) and automatic speech recognition (ASR), which are one of the most important regression- and classification-based speech processing tasks, respectively. The experimental results show that the SE and ASR models trained on the designed speech corpus outperform their counterparts trained on a randomly composed speech corpus.  ( 2 min )
    Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching. (arXiv:2301.04004v1 [eess.SY])
    Reaching disabilities affect the quality of life. Functional Electrical Stimulation (FES) can restore lost motor functions. Yet, there remain challenges in controlling FES to induce desired movements. Neuromechanical models are valuable tools for developing FES control methods. However, focusing on the upper extremity areas, several existing models are either overly simplified or too computationally demanding for control purposes. Besides the model-related issues, finding a general method for governing the control rules for different tasks and subjects remains an engineering challenge. Here, we present our approach toward FES-based restoration of arm movements to address those fundamental issues in controlling FES. Firstly, we present our surface-FES-oriented neuromechanical models of human arms built using well-accepted, open-source software. The models are designed to capture significant dynamics in FES controls with minimal computational cost. Our models are customisable and can be used for testing different control methods. Secondly, we present the application of reinforcement learning (RL) as a general method for governing the control rules. In combination, our customisable models and RL-based control method open the possibility of delivering customised FES controls for different subjects and settings with minimal engineering intervention. We demonstrate our approach in planar and 3D settings.  ( 2 min )
    A Dietary Nutrition-aided Healthcare Platform via Effective Food Recognition on a Localized Singaporean Food Dataset. (arXiv:2301.03829v1 [cs.LG])
    Localized food datasets have profound meaning in revealing a country's special cuisines to explore people's dietary behaviors, which will shed light on their health conditions and disease development. In this paper, revolving around the demand for accurate food recognition in Singapore, we develop the FoodSG platform to incubate diverse healthcare-oriented applications as a service in Singapore, taking into account their shared requirements. We release a localized Singaporean food dataset FoodSG-233 with a systematic cleaning and curation pipeline for promoting future data management research in food computing. To overcome the hurdle in recognition performance brought by Singaporean multifarious food dishes, we propose to integrate supervised contrastive learning into our food recognition model FoodSG-SCL for the intrinsic capability to mine hard positive/negative samples and therefore boost the accuracy. Through a comprehensive evaluation, we share the insightful experience with practitioners in the data management community regarding food-related data-intensive healthcare applications. The FoodSG-233 dataset can be accessed via: https://foodlg.comp.nus.edu.sg/.  ( 2 min )
    Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization. (arXiv:2301.04049v1 [eess.SY])
    There is growing importance to detecting faults and implementing the best methods in industrial and real-world systems. We are searching for the most trustworthy and practical data-based fault detection methods proposed by artificial intelligence applications. In this paper, we propose a framework for fault detection based on reinforcement learning and a policy known as proximal policy optimization. As a result of the lack of fault data, one of the significant problems with the traditional policy is its weakness in detecting fault classes, which was addressed by changing the cost function. Using modified Proximal Policy Optimization, we can increase performance, overcome data imbalance, and better predict future faults. When our modified policy is implemented, all evaluation metrics will increase by $3\%$ to $4\%$ as compared to the traditional policy in the first benchmark, between $20\%$ and $55\%$ in the second benchmark, and between $6\%$ and $14\%$ in the third benchmark, as well as an improvement in performance and prediction speed compared to previous methods.  ( 2 min )
    There is No Big Brother or Small Brother: Knowledge Infusion in Language Models for Link Prediction and Question Answering. (arXiv:2301.04013v1 [cs.CL])
    The integration of knowledge graphs with deep learning is thriving in improving the performance of various natural language processing (NLP) tasks. In this paper, we focus on knowledge-infused link prediction and question answering using language models, T5, and BLOOM across three domains: Aviation, Movie, and Web. In this context, we infuse knowledge in large and small language models and study their performance, and find the performance to be similar. For the link prediction task on the Aviation Knowledge Graph, we obtain a 0.2 hits@1 score using T5-small, T5-base, T5-large, and BLOOM. Using template-based scripts, we create a set of 1 million synthetic factoid QA pairs in the aviation domain from National Transportation Safety Board (NTSB) reports. On our curated QA pairs, the three models of T5 achieve a 0.7 hits@1 score. We validate out findings with the paired student t-test and Cohen's kappa scores. For link prediction on Aviation Knowledge Graph using T5-small and T5-large, we obtain a Cohen's kappa score of 0.76, showing substantial agreement between the models. Thus, we infer that small language models perform similar to large language models with the infusion of knowledge.  ( 2 min )
    Manifold Restricted Interventional Shapley Values. (arXiv:2301.04041v1 [stat.ML])
    Shapley values are model-agnostic methods for explaining model predictions. Many commonly used methods of computing Shapley values, known as \emph{off-manifold methods}, rely on model evaluations on out-of-distribution input samples. Consequently, explanations obtained are sensitive to model behaviour outside the data distribution, which may be irrelevant for all practical purposes. While \emph{on-manifold methods} have been proposed which do not suffer from this problem, we show that such methods are overly dependent on the input data distribution, and therefore result in unintuitive and misleading explanations. To circumvent these problems, we propose \emph{ManifoldShap}, which respects the model's domain of validity by restricting model evaluations to the data manifold. We show, theoretically and empirically, that ManifoldShap is robust to off-manifold perturbations of the model and leads to more accurate and intuitive explanations than existing state-of-the-art Shapley methods.  ( 2 min )
    Sentiment-based Engagement Strategies for intuitive Human-Robot Interaction. (arXiv:2301.03867v1 [cs.RO])
    Emotion expressions serve as important communicative signals and are crucial cues in intuitive interactions between humans. Hence, it is essential to include these fundamentals in robotic behavior strategies when interacting with humans to promote mutual understanding and to reduce misjudgements. We tackle this challenge by detecting and using the emotional state and attention for a sentiment analysis of potential human interaction partners to select well-adjusted engagement strategies. This way, we pave the way for more intuitive human-robot interactions, as the robot's action conforms to the person's mood and expectation. We propose four different engagement strategies with implicit and explicit communication techniques that we implement on a mobile robot platform for initial experiments.  ( 2 min )
    Quantifying Assurance in Learning-enabled Systems. (arXiv:2006.10345v1 [cs.SE] CROSS LISTED)
    Dependability assurance of systems embedding machine learning(ML) components---so called learning-enabled systems (LESs)---is a key step for their use in safety-critical applications. In emerging standardization and guidance efforts, there is a growing consensus in the value of using assurance cases for that purpose. This paper develops a quantitative notion of assurance that an LES is dependable, as a core component of its assurance case, also extending our prior work that applied to ML components. Specifically, we characterize LES assurance in the form of assurance measures: a probabilistic quantification of confidence that an LES possesses system-level properties associated with functional capabilities and dependability attributes. We illustrate the utility of assurance measures by application to a real world autonomous aviation system, also describing their role both in i) guiding high-level, runtime risk mitigation decisions and ii) as a core component of the associated dynamic assurance case.  ( 2 min )
    Sharing pattern submodels for prediction with missing values. (arXiv:2206.11161v2 [cs.LG] UPDATED)
    Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. When variables are missing in recurring patterns, fitting separate pattern submodels have been proposed as a solution. However, fitting models independently does not make efficient use of all available data. Conversely, fitting a single shared model to the full data set relies on imputation which often leads to biased results when missingness depends on unobserved factors. We propose an alternative approach, called sharing pattern submodels, which i) makes predictions that are robust to missing values at test time, ii) maintains or improves the predictive power of pattern submodels, and iii) has a short description, enabling improved interpretability. Parameter sharing is enforced through sparsity-inducing regularization which we prove leads to consistent estimation. Finally, we give conditions for when a sharing model is optimal, even when both missingness and the target outcome depend on unobserved variables. Classification and regression experiments on synthetic and real-world data sets demonstrate that our models achieve a favorable tradeoff between pattern specialization and information sharing.  ( 2 min )
    Actor-Director-Critic: A Novel Deep Reinforcement Learning Framework. (arXiv:2301.03887v1 [cs.LG])
    In this paper, we propose actor-director-critic, a new framework for deep reinforcement learning. Compared with the actor-critic framework, the director role is added, and action classification and action evaluation are applied simultaneously to improve the decision-making performance of the agent. Firstly, the actions of the agent are divided into high quality actions and low quality actions according to the rewards returned from the environment. Then, the director network is trained to have the ability to discriminate high and low quality actions and guide the actor network to reduce the repetitive exploration of low quality actions in the early stage of training. In addition, we propose an improved double estimator method to better solve the problem of overestimation in the field of reinforcement learning. For the two critic networks used, we design two target critic networks for each critic network instead of one. In this way, the target value of each critic network can be calculated by taking the average of the outputs of the two target critic networks, which is more stable and accurate than using only one target critic network to obtain the target value. In order to verify the performance of the actor-director-critic framework and the improved double estimator method, we applied them to the TD3 algorithm to improve the TD3 algorithm. Then, we carried out experiments in multiple environments in MuJoCo and compared the experimental data before and after the algorithm improvement. The final experimental results show that the improved algorithm can achieve faster convergence speed and higher total return.  ( 2 min )
    RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration. (arXiv:2301.03904v1 [cs.AR])
    The increasing interest in TinyML, i.e., near-sensor machine learning on power budgets of a few tens of mW, is currently pushing toward enabling TinyML-class training as opposed to inference only. Current training algorithms, based on various forms of error and gradient backpropagation, rely on floating-point matrix operations to meet the precision and dynamic range requirements. So far, the energy and power cost of these operations has been considered too high for TinyML scenarios. This paper addresses the open challenge of near-sensor training on a few mW power budget and presents RedMulE - Reduced-Precision Matrix Multiplication Engine, a low-power specialized accelerator conceived for multi-precision floating-point General Matrix-Matrix Operations (GEMM-Ops) acceleration, supporting FP16, as well as hybrid FP8 formats, with {sign, exponent, mantissa}=({1,4,3}, {1,5,2}). We integrate RedMule into a Parallel Ultra-Low-Power (PULP) cluster containing eight energy-efficient RISC-V cores sharing a tightly-coupled data memory and implement the resulting system in a 22 nm technology. At its best efficiency point (@ 470 MHz, 0.65 V), the RedMulE-augmented PULP cluster achieves 755 GFLOPS/W and 920 GFLOPS/W during regular General Matrix-Matrix Multiplication (GEMM), and up to 1.19 TFLOPS/W and 1.67 TFLOPS/W when executing GEMM-Ops, respectively, for FP16 and FP8 input/output tensors. In its best performance point (@ 613 MHz, 0.8 V), RedMulE achieves up to 58.5 GFLOPS and 117 GFLOPS for FP16 and FP8, respectively, with 99.4% utilization of the array of Computing Elements and consuming less than 60 mW on average, thus enabling on-device training of deep learning models in TinyML application scenarios while retaining the flexibility to tackle other classes of common linear algebra problems efficiently.  ( 2 min )
    Neighborhood-Regularized Self-Training for Learning with Few Labels. (arXiv:2301.03726v1 [cs.LG])
    Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar representations, we develop a neighborhood-based sample selection approach to tackle the issue of noisy pseudo labels. We further stabilize self-training via aggregating the predictions from different rounds during sample selection. Experiments on eight tasks show that our proposed method outperforms the strongest self-training baseline with 1.83% and 2.51% performance gain for text and graph datasets on average. Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36.8% and saves 57.3% of the time when compared with the best baseline. Our code and appendices will be uploaded to https://github.com/ritaranx/NeST.  ( 2 min )
    Min-Max Optimization Made Simple: Approximating the Proximal Point Method via Contraction Maps. (arXiv:2301.03931v1 [cs.GT])
    In this paper we present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems while requiring a simple and intuitive analysis. Similarly to the seminal work of Nemirovski and the recent approach of Piliouras et al. in normal form games, our work is based on the fact that the update rule of the Proximal Point method (PP) can be approximated up to accuracy $\epsilon$ with only $\mathcal{O}(\log 1/\epsilon)$ additional gradient-calls through the iterations of a contraction map. Then combining the analysis of (PP) method with an error-propagation analysis we establish that the resulting first order method, called \textit{Clairvoyant Extra Gradient}, admits near-optimal time-average convergence for general domains and last-iterate convergence in the unconstrained case.  ( 2 min )
    Is Federated Learning a Practical PET Yet?. (arXiv:2301.04017v1 [cs.CR])
    Federated learning (FL) is a framework for users to jointly train a machine learning model. FL is promoted as a privacy-enhancing technology (PET) that provides data minimization: data never "leaves" personal devices and users share only model updates with a server (e.g., a company) coordinating the distributed training. We assess the realistic (i.e., worst-case) privacy guarantees that are provided to users who are unable to trust the server. To this end, we propose an attack against FL protected with distributed differential privacy (DDP) and secure aggregation (SA). The attack method is based on the introduction of Sybil devices that deviate from the protocol to expose individual users' data for reconstruction by the server. The underlying root cause for the vulnerability to our attack is the power imbalance. The server orchestrates the whole protocol and users are given little guarantees about the selection of other users participating in the protocol. Moving forward, we discuss requirements for an FL protocol to guarantee DDP without asking users to trust the server. We conclude that such systems are not yet practical.  ( 2 min )
    On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it. (arXiv:2301.03662v1 [cs.LG])
    We propose iterative algorithms to solve adversarial problems in a variety of supervised learning settings of interest. Our algorithms, which can be interpreted as suitable ascent-descent dynamics in Wasserstein spaces, take the form of a system of interacting particles. These interacting particle dynamics are shown to converge toward appropriate mean-field limit equations in certain large number of particles regimes. In turn, we prove that, under certain regularity assumptions, these mean-field equations converge, in the large time limit, toward approximate Nash equilibria of the original adversarial learning problems. We present results for nonconvex-nonconcave settings, as well as for nonconvex-concave ones. Numerical experiments illustrate our results.  ( 2 min )
    SantaCoder: don't reach for the stars!. (arXiv:2301.03988v1 [cs.SE])
    The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at https://hf.co/bigcode.  ( 2 min )
    Look Beyond Bias with Entropic Adversarial Data Augmentation. (arXiv:2301.03844v1 [cs.LG])
    Deep neural networks do not discriminate between spurious and causal patterns, and will only learn the most predictive ones while ignoring the others. This shortcut learning behaviour is detrimental to a network's ability to generalize to an unknown test-time distribution in which the spurious correlations do not hold anymore. Debiasing methods were developed to make networks robust to such spurious biases but require to know in advance if a dataset is biased and make heavy use of minority counterexamples that do not display the majority bias of their class. In this paper, we argue that such samples should not be necessarily needed because the ''hidden'' causal information is often also contained in biased images. To study this idea, we propose 3 publicly released synthetic classification benchmarks, exhibiting predictive classification shortcuts, each of a different and challenging nature, without any minority samples acting as counterexamples. First, we investigate the effectiveness of several state-of-the-art strategies on our benchmarks and show that they do not yield satisfying results on them. Then, we propose an architecture able to succeed on our benchmarks, despite their unusual properties, using an entropic adversarial data augmentation training scheme. An encoder-decoder architecture is tasked to produce images that are not recognized by a classifier, by maximizing the conditional entropy of its outputs, and keep as much as possible of the initial content. A precise control of the information destroyed, via a disentangling process, enables us to remove the shortcut and leave everything else intact. Furthermore, results competitive with the state-of-the-art on the BAR dataset ensure the applicability of our method in real-life situations.  ( 2 min )
    Proceedings of the NeurIPS 2021 Workshop on Machine Learning for the Developing World: Global Challenges. (arXiv:2301.04007v1 [cs.LG])
    These are the proceedings of the 5th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) on December 14th, 2021.  ( 2 min )
    Hint assisted reinforcement learning: an application in radio astronomy. (arXiv:2301.03933v1 [astro-ph.IM])
    Model based reinforcement learning has proven to be more sample efficient than model free methods. On the other hand, the construction of a dynamics model in model based reinforcement learning has increased complexity. Data processing tasks in radio astronomy are such situations where the original problem which is being solved by reinforcement learning itself is the creation of a model. Fortunately, many methods based on heuristics or signal processing do exist to perform the same tasks and we can leverage them to propose the best action to take, or in other words, to provide a `hint'. We propose to use `hints' generated by the environment as an aid to the reinforcement learning process mitigating the complexity of model construction. We modify the soft actor critic algorithm to use hints and use the alternating direction method of multipliers algorithm with inequality constraints to train the agent. Results in several environments show that we get the increased sample efficiency by using hints as compared to model free methods.  ( 2 min )
    Community Detection with Known, Unknown, or Partially Known Auxiliary Latent Variables. (arXiv:2301.04088v1 [cs.SI])
    Empirical observations suggest that in practice, community membership does not completely explain the dependency between the edges of an observation graph. The residual dependence of the graph edges are modeled in this paper, to first order, by auxiliary node latent variables that affect the statistics of the graph edges but carry no information about the communities of interest. We then study community detection in graphs obeying the stochastic block model and censored block model with auxiliary latent variables. We analyze the conditions for exact recovery when these auxiliary latent variables are unknown, representing unknown nuisance parameters or model mismatch. We also analyze exact recovery when these secondary latent variables have been either fully or partially revealed. Finally, we propose a semidefinite programming algorithm for recovering the desired labels when the secondary labels are either known or unknown. We show that exact recovery is possible by semidefinite programming down to the respective maximum likelihood exact recovery threshold.
    Neural Radiance Field Codebooks. (arXiv:2301.04101v1 [cs.CV])
    Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks. Learning such representations for complex scenes and tasks remains an open challenge. Towards this goal, we introduce Neural Radiance Field Codebooks (NRC), a scalable method for learning object-centric representations through novel view reconstruction. NRC learns to reconstruct scenes from novel views using a dictionary of object codes which are decoded through a volumetric renderer. This enables the discovery of reoccurring visual and geometric patterns across scenes which are transferable to downstream tasks. We show that NRC representations transfer well to object navigation in THOR, outperforming 2D and 3D representation learning methods by 3.1% success rate. We demonstrate that our approach is able to perform unsupervised segmentation for more complex synthetic (THOR) and real scenes (NYU Depth) better than prior methods (29% relative improvement). Finally, we show that NRC improves on the task of depth ordering by 5.5% accuracy in THOR.
    Privacy-Preserving Record Linkage for Cardinality Counting. (arXiv:2301.04000v1 [cs.CR])
    Several applications require counting the number of distinct items in the data, which is known as the cardinality counting problem. Example applications include health applications such as rare disease patients counting for adequate awareness and funding, and counting the number of cases of a new disease for outbreak detection, marketing applications such as counting the visibility reached for a new product, and cybersecurity applications such as tracking the number of unique views of social media posts. The data needed for the counting is however often personal and sensitive, and need to be processed using privacy-preserving techniques. The quality of data in different databases, for example typos, errors and variations, poses additional challenges for accurate cardinality estimation. While privacy-preserving cardinality counting has gained much attention in the recent times and a few privacy-preserving algorithms have been developed for cardinality estimation, no work has so far been done on privacy-preserving cardinality counting using record linkage techniques with fuzzy matching and provable privacy guarantees. We propose a novel privacy-preserving record linkage algorithm using unsupervised clustering techniques to link and count the cardinality of individuals in multiple datasets without compromising their privacy or identity. In addition, existing Elbow methods to find the optimal number of clusters as the cardinality are far from accurate as they do not take into account the purity and completeness of generated clusters. We propose a novel method to find the optimal number of clusters in unsupervised learning. Our experimental results on real and synthetic datasets are highly promising in terms of significantly smaller error rate of less than 0.1 with a privacy budget {\epsilon} = 1.0 compared to the state-of-the-art fuzzy matching and clustering method.
    On the Robustness of AlphaFold: A COVID-19 Case Study. (arXiv:2301.04093v1 [cs.LG])
    Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been explored. This is particularly relevant given the broad social implications of such technologies and the fact that biologically small perturbations in the protein sequence do not generally lead to drastic changes in the protein structure. In this paper, we demonstrate that AlphaFold does not exhibit such robustness despite its high accuracy. This raises the challenge of detecting and quantifying the extent to which these predicted protein structures can be trusted. To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version. We prove that the problem of minimally perturbing protein sequences to fool protein folding neural networks is NP-complete. Based on the well-established BLOSUM62 sequence alignment scoring matrix, we generate adversarial protein sequences and show that the RMSD between the predicted protein structure and the structure of the original sequence are very large when the adversarial changes are bounded by (i) 20 units in the BLOSUM62 distance, and (ii) five residues (out of hundreds or thousands of residues) in the given protein sequence. In our experimental evaluation, we consider 111 COVID-19 proteins in the Universal Protein resource (UniProt), a central resource for protein data managed by the European Bioinformatics Institute, Swiss Institute of Bioinformatics, and the US Protein Information Resource. These result in an overall GDT similarity test score average of around 34%, demonstrating a substantial drop in the performance of AlphaFold.
    Temporal Weights. (arXiv:2301.04126v1 [cs.NE])
    In artificial neural networks, weights are a static representation of synapses. However, synapses are not static, they have their own interacting dynamics over time. To instill weights with interacting dynamics, we use a model describing synchronization that is capable of capturing core mechanisms of a range of neural and general biological phenomena over time. An ideal fit for these Temporal Weights (TW) are Neural ODEs, with continuous dynamics and a dependency on time. The resulting recurrent neural networks efficiently model temporal dynamics by computing on the ordering of sequences, and the length and scale of time. By adding temporal weights to a model, we demonstrate better performance, smaller models, and data efficiency on sparse, irregularly sampled time series datasets.
    Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation. (arXiv:2204.01171v3 [cs.CL] UPDATED)
    Current language generation models suffer from issues such as repetition, incoherence, and hallucinations. An often-repeated hypothesis is that this brittleness of generation models is caused by the training and the generation procedure mismatch, also referred to as exposure bias. In this paper, we verify this hypothesis by analyzing exposure bias from an imitation learning perspective. We show that exposure bias leads to an accumulation of errors, analyze why perplexity fails to capture this accumulation, and empirically show that this accumulation results in poor generation quality. Source code to reproduce these experiments is available at https://github.com/kushalarora/quantifying_exposure_bias
    Deep learning approach for interruption attacks detection in LEO satellite networks. (arXiv:2301.03998v1 [cs.CR])
    The developments of satellite communication in network systems require strong and effective security plans. Attacks such as denial of service (DoS) can be detected through the use of machine learning techniques, especially under normal operational conditions. This work aims to provide an interruption detection strategy for Low Earth Orbit (\textsf{LEO}) satellite networks using deep learning algorithms. Both the training, and the testing of the proposed models are carried out with our own communication datasets, created by utilizing a satellite traffic (benign and malicious) that was generated using satellite networks simulation platforms, Omnet++ and Inet. We test different deep learning algorithms including Multi Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), and Long Short-term Memory (LSTM). Followed by a full analysis and investigation of detection rate in both binary classification, and multi-classes classification that includes different interruption categories such as Distributed DoS (DDoS), Network Jamming, and meteorological disturbances. Simulation results for both classification types surpassed 99.33% in terms of detection rate in scenarios of full network surveillance. However, in more realistic scenarios, the best-recorded performance was 96.12% for the detection of binary traffic and 94.35% for the detection of multi-class traffic with a false positive rate of 3.72%, using a hybrid model that combines MLP and GRU. This Deep Learning approach efficiency calls for the necessity of using machine learning methods to improve security and to give more awareness to search for solutions that facilitate data collection in LEO satellite networks.
    Constraining cosmological parameters from N-body simulations with Variational Bayesian Neural Networks. (arXiv:2301.03991v1 [astro-ph.IM])
    Methods based on Deep Learning have recently been applied on astrophysical parameter recovery thanks to their ability to capture information from complex data. One of these methods is the approximate Bayesian Neural Networks (BNNs) which have demonstrated to yield consistent posterior distribution into the parameter space, helpful for uncertainty quantification. However, as any modern neural networks, they tend to produce overly confident uncertainty estimates and can introduce bias when BNNs are applied to data. In this work, we implement multiplicative normalizing flows (MNFs), a family of approximate posteriors for the parameters of BNNs with the purpose of enhancing the flexibility of the variational posterior distribution, to extract $\Omega_m$, $h$, and $\sigma_8$ from the QUIJOTE simulations. We have compared this method with respect to the standard BNNs, and the flipout estimator. We found that MNFs combined with BNNs outperform the other models obtaining predictive performance with almost one order of magnitude larger that standard BNNs, $\sigma_8$ extracted with high accuracy ($r^2=0.99$), and precise uncertainty estimates. The latter implies that MNFs provide more realistic predictive distribution closer to the true posterior mitigating the bias introduced by the variational approximation and allowing to work with well-calibrated networks.
    Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models. (arXiv:2301.04005v1 [eess.SY])
    Reaching disability limits an individual's ability in performing daily tasks. Surface Functional Electrical Stimulation (FES) offers a non-invasive solution to restore lost ability. However, inducing desired movements using FES is still an open engineering problem. This problem is accentuated by the complexities of human arms' neuromechanics and the variations across individuals. Reinforcement Learning (RL) emerges as a promising approach to govern customised control rules for different settings. Yet, one remaining challenge of controlling FES systems for RL is unobservable muscle fatigue that progressively changes as an unknown function of the stimulation, thereby breaking the Markovian assumption of RL. In this work, we present a method to address the unobservable muscle fatigue issue, allowing our RL controller to achieve higher control performances. Our method is based on a Gaussian State-Space Model (GSSM) that utilizes recurrent neural networks to learn Markovian state-spaces from partial observations. The GSSM is used as a filter that converts the observations into the state-space representation for RL to preserve the Markovian assumption. Here, we start with presenting the modification of the original GSSM to address an overconfident issue. We then present the interaction between RL and the modified GSSM, followed by the setup for FES control learning. We test our RL-GSSM system on a planar reaching setting in simulation using a detailed neuromechanical model. The results show that the GSSM can help improve the RL's control performance to the comparable level of the ideal case that the fatigue is observable.
    Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification. (arXiv:2208.06616v2 [cs.LG] UPDATED)
    Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled data via contrasting different augmented views of data. In this work, we propose a novel Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC) that learns representations from unlabeled data with contrastive learning. Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module. Additionally, we conduct a systematic study of time-series data augmentation selection, which is a key part of contrastive learning. We also extend TS-TCC to the semi-supervised learning settings and propose a Class-Aware TS-TCC (CA-TCC) that benefits from the available few labeled data to further improve representations learned by TS-TCC. Specifically, we leverage robust pseudo labels produced by TS-TCC to realize class-aware contrastive loss. Extensive experiments show that the linear evaluation of the features learned by our proposed framework performs comparably with the fully supervised training. Additionally, our framework shows high efficiency in few labeled data and transfer learning scenarios. The code is publicly available at \url{https://github.com/emadeldeen24/CA-TCC}.  ( 2 min )
    Federated Learning for Energy Constrained IoT devices: A systematic mapping study. (arXiv:2301.03720v1 [cs.LG])
    Federated Machine Learning (Fed ML) is a new distributed machine learning technique applied to collaboratively train a global model using clients local data without transmitting it. Nodes only send parameter updates (e.g., weight updates in the case of neural networks), which are fused together by the server to build the global model. By not divulging node data, Fed ML guarantees its confidentiality, a crucial aspect of network security, which enables it to be used in the context of data-sensitive Internet of Things (IoT) and mobile applications, such as smart Geo-location and the smart grid. However, most IoT devices are particularly energy constrained, which raises the need to optimize the Fed ML process for efficient training tasks and optimized power consumption. In this paper, we conduct, to the best of our knowledge, the first Systematic Mapping Study (SMS) on Fed ML optimization techniques for energy-constrained IoT devices. From a total of more than 800 papers, we select 67 that satisfy our criteria and give a structured overview of the field using a set of carefully chosen research questions. Finally, we attempt to provide an analysis of the energy-constrained Fed ML state of the art and try to outline some potential recommendations for the research community.  ( 2 min )
    Transfer learning for conflict and duplicate detection in software requirement pairs. (arXiv:2301.03709v1 [cs.SE])
    Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We formulate the conflict and duplicate detection problem as a requirement pair classification task. We design a novel transformers-based architecture, SR-BERT, which incorporates Sentence-BERT and Bi-encoders for the conflict and duplicate identification task. Furthermore, we apply supervised multi-stage fine-tuning to the pre-trained transformer models. We test the performance of different transfer models using four different datasets. We find that sequentially trained and fine-tuned transformer models perform well across the datasets with SR-BERT achieving the best performance for larger datasets. We also explore the cross-domain performance of conflict detection models and adopt a rule-based filtering approach to validate the model classifications. Our analysis indicates that the sentence pair classification approach and the proposed transformer-based natural language processing strategies can contribute significantly to achieving automation in conflict and duplicate detection
    UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion. (arXiv:2301.03801v1 [cs.SD])
    Text-to-speech (TTS) and voice conversion (VC) are two different tasks both aiming at generating high quality speaking voice according to different input modality. Due to their similarity, this paper proposes UnifySpeech, which brings TTS and VC into a unified framework for the first time. The model is based on the assumption that speech can be decoupled into three independent components: content information, speaker information, prosody information. Both TTS and VC can be regarded as mining these three parts of information from the input and completing the reconstruction of speech. For TTS, the speech content information is derived from the text, while in VC it's derived from the source speech, so all the remaining units are shared except for the speech content extraction module in the two tasks. We applied vector quantization and domain constrain to bridge the gap between the content domains of TTS and VC. Objective and subjective evaluation shows that by combining the two task, TTS obtains better speaker modeling ability while VC gets hold of impressive speech content decoupling capability.
    Chatbots in a Honeypot World. (arXiv:2301.03771v1 [cs.CR])
    Question-and-answer agents like ChatGPT offer a novel tool for use as a potential honeypot interface in cyber security. By imitating Linux, Mac, and Windows terminal commands and providing an interface for TeamViewer, nmap, and ping, it is possible to create a dynamic environment that can adapt to the actions of attackers and provide insight into their tactics, techniques, and procedures (TTPs). The paper illustrates ten diverse tasks that a conversational agent or large language model might answer appropriately to the effects of command-line attacker. The original result features feasibility studies for ten model tasks meant for defensive teams to mimic expected honeypot interfaces with minimal risks. Ultimately, the usefulness outside of forensic activities stems from whether the dynamic honeypot can extend the time-to-conquer or otherwise delay attacker timelines short of reaching key network assets like databases or confidential information. While ongoing maintenance and monitoring may be required, ChatGPT's ability to detect and deflect malicious activity makes it a valuable option for organizations seeking to enhance their cyber security posture. Future work will focus on cybersecurity layers, including perimeter security, host virus detection, and data security.  ( 2 min )
    Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network. (arXiv:2301.03802v1 [cs.LG])
    In last-mile delivery, drivers frequently deviate from planned delivery routes because of their tacit knowledge of the road and curbside infrastructure, customer availability, and other characteristics of the respective service areas. Hence, the actual stop sequences chosen by an experienced human driver may be potentially preferable to the theoretical shortest-distance routing under real-life operational conditions. Thus, being able to predict the actual stop sequence that a human driver would follow can help to improve route planning in last-mile delivery. This paper proposes a pair-wise attention-based pointer neural network for this prediction task using drivers' historical delivery trajectory data. In addition to the commonly used encoder-decoder architecture for sequence-to-sequence prediction, we propose a new attention mechanism based on an alternative specific neural network to capture the local pair-wise information for each pair of stops. To further capture the global efficiency of the route, we propose a new iterative sequence generation algorithm that is used after model training to identify the first stop of a route that yields the lowest operational cost. Results from an extensive case study on real operational data from Amazon's last-mile delivery operations in the US show that our proposed method can significantly outperform traditional optimization-based approaches and other machine learning methods (such as the Long Short-Term Memory encoder-decoder and the original pointer network) in finding stop sequences that are closer to high-quality routes executed by experienced drivers in the field. Compared to benchmark models, the proposed model can increase the average prediction accuracy of the first four stops from around 0.2 to 0.312, and reduce the disparity between the predicted route and the actual route by around 15%.  ( 2 min )
    Learning to Perceive in Deep Model-Free Reinforcement Learning. (arXiv:2301.03730v1 [cs.LG])
    This work proposes a novel model-free Reinforcement Learning (RL) agent that is able to learn how to complete an unknown task having access to only a part of the input observation. We take inspiration from the concepts of visual attention and active perception that are characteristic of humans and tried to apply them to our agent, creating a hard attention mechanism. In this mechanism, the model decides first which region of the input image it should look at, and only after that it has access to the pixels of that region. Current RL agents do not follow this principle and we have not seen these mechanisms applied to the same purpose as this work. In our architecture, we adapt an existing model called recurrent attention model (RAM) and combine it with the proximal policy optimization (PPO) algorithm. We investigate whether a model with these characteristics is capable of achieving similar performance to state-of-the-art model-free RL agents that access the full input observation. This analysis is made in two Atari games, Pong and SpaceInvaders, which have a discrete action space, and in CarRacing, which has a continuous action space. Besides assessing its performance, we also analyze the movement of the attention of our model and compare it with what would be an example of the human behavior. Even with such visual limitation, we show that our model matches the performance of PPO+LSTM in two of the three games tested.  ( 2 min )
    Markovian Sliced Wasserstein Distances: Beyond Independent Projections. (arXiv:2301.03749v1 [stat.ML])
    Sliced Wasserstein (SW) distance suffers from redundant projections due to independent uniform random projecting directions. To partially overcome the issue, max K sliced Wasserstein (Max-K-SW) distance ($K\geq 1$), seeks the best discriminative orthogonal projecting directions. Despite being able to reduce the number of projections, the metricity of Max-K-SW cannot be guaranteed in practice due to the non-optimality of the optimization. Moreover, the orthogonality constraint is also computationally expensive and might not be effective. To address the problem, we introduce a new family of SW distances, named Markovian sliced Wasserstein (MSW) distance, which imposes a first-order Markov structure on projecting directions. We discuss various members of MSW by specifying the Markov structure including the prior distribution, the transition distribution, and the burning and thinning technique. Moreover, we investigate the theoretical properties of MSW including topological properties (metricity, weak convergence, and connection to other distances), statistical properties (sample complexity, and Monte Carlo estimation error), and computational properties (computational complexity and memory complexity). Finally, we compare MSW distances with previous SW variants in various applications such as gradient flows, color transfer, and deep generative modeling to demonstrate the favorable performance of MSW.  ( 2 min )
    On The Fragility of Learned Reward Functions. (arXiv:2301.03652v1 [cs.LG])
    Reward functions are notoriously difficult to specify, especially for tasks with complex goals. Reward learning approaches attempt to infer reward functions from human feedback and preferences. Prior works on reward learning have mainly focused on the performance of policies trained alongside the reward function. This practice, however, may fail to detect learned rewards that are not capable of training new policies from scratch and thus do not capture the intended behavior. Our work focuses on demonstrating and studying the causes of these relearning failures in the domain of preference-based reward learning. We demonstrate with experiments in tabular and continuous control environments that the severity of relearning failures can be sensitive to changes in reward model design and the trajectory dataset composition. Based on our findings, we emphasize the need for more retraining-based evaluations in the literature.  ( 2 min )
    Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models. (arXiv:2301.03797v1 [cs.SE])
    Incident management for cloud services is a complex process involving several steps and has a huge impact on both service health and developer productivity. On-call engineers require significant amount of domain knowledge and manual effort for root causing and mitigation of production incidents. Recent advances in artificial intelligence has resulted in state-of-the-art large language models like GPT-3.x (both GPT-3.0 and GPT-3.5), which have been used to solve a variety of problems ranging from question answering to text summarization. In this work, we do the first large-scale study to evaluate the effectiveness of these models for helping engineers root cause and mitigate production incidents. We do a rigorous study at Microsoft, on more than 40,000 incidents and compare several large language models in zero-shot, fine-tuned and multi-task setting using semantic and lexical metrics. Lastly, our human evaluation with actual incident owners show the efficacy and future potential of using artificial intelligence for resolving cloud incidents.  ( 2 min )
    Online Backfilling with No Regret for Large-Scale Image Retrieval. (arXiv:2301.03767v1 [cs.CV])
    Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems. It inevitably requires a prohibitively large amount of computational cost and even entails the downtime of the service. Although backward-compatible learning sidesteps this challenge by tackling query-side representations, this leads to suboptimal solutions in principle because gallery embeddings cannot benefit from model upgrades. We address this dilemma by introducing an online backfilling algorithm, which enables us to achieve a progressive performance improvement during the backfilling process while not sacrificing the final performance of new model after the completion of backfilling. To this end, we first propose a simple distance rank merge technique for online backfilling. Then, we incorporate a reverse transformation module for more effective and efficient merging, which is further enhanced by adopting a metric-compatible contrastive learning approach. These two components help to make the distances of old and new models compatible, resulting in desirable merge results during backfilling with no extra computational overhead. Extensive experiments show the effectiveness of our framework on four standard benchmarks in various settings.  ( 2 min )
    Tensor Denoising via Amplification and Stable Rank Methods. (arXiv:2301.03761v1 [cs.LG])
    Tensors in the form of multilinear arrays are ubiquitous in data science applications. Captured real-world data, including video, hyperspectral images, and discretized physical systems, naturally occur as tensors and often come with attendant noise. Under the additive noise model and with the assumption that the underlying clean tensor has low rank, many denoising methods have been created that utilize tensor decomposition to effect denoising through low rank tensor approximation. However, all such decomposition methods require estimating the tensor rank, or related measures such as the tensor spectral and nuclear norms, all of which are NP-hard problems. In this work we adapt the previously developed framework of tensor amplification, which provides good approximations of the spectral and nuclear tensor norms, to denoising synthetic tensors of various sizes, ranks, and noise levels, along with real-world tensors derived from physiological signals. We also introduce denoising methods based on two variations of rank estimates called stable $X$-rank and stable slice rank. The experimental results show that in the low rank context, tensor-based amplification provides comparable denoising performance in high signal-to-noise ratio (SNR) settings and superior performance in noisy (i.e., low SNR) settings, while the stable $X$-rank method achieves superior denoising performance on the physiological signal data.  ( 2 min )
    A Unified Theory of Diversity in Ensemble Learning. (arXiv:2301.03962v1 [cs.LG])
    We present a theory of ensemble diversity, explaining the nature and effect of diversity for a wide range of supervised learning scenarios. This challenge, of understanding ensemble diversity, has been referred to as the holy grail of ensemble learning, an open question for over 30 years. Our framework reveals that diversity is in fact a hidden dimension in the bias-variance decomposition of an ensemble. In particular, we prove a family of exact bias-variance-diversity decompositions, for both classification and regression losses, e.g., squared, and cross-entropy. The framework provides a methodology to automatically identify the combiner rule enabling such a decomposition, specific to the loss. The formulation of diversity is therefore dependent on just two design choices: the loss, and the combiner. For certain choices (e.g., 0-1 loss with majority voting) the effect of diversity is necessarily dependent on the target label. Experiments illustrate how we can use our framework to understand the diversity-encouraging mechanisms of popular ensemble methods: Bagging, Boosting, and Random Forests.  ( 2 min )
    Multiscale Metamorphic VAE for 3D Brain MRI Synthesis. (arXiv:2301.03588v1 [eess.IV])
    Generative modeling of 3D brain MRIs presents difficulties in achieving high visual fidelity while ensuring sufficient coverage of the data distribution. In this work, we propose to address this challenge with composable, multiscale morphological transformations in a variational autoencoder (VAE) framework. These transformations are applied to a chosen reference brain image to generate MRI volumes, equipping the model with strong anatomical inductive biases. We structure the VAE latent space in a way such that the model covers the data distribution sufficiently well. We show substantial performance improvements in FID while retaining comparable, or superior, reconstruction quality compared to prior work based on VAEs and generative adversarial networks (GANs).  ( 2 min )
    Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding. (arXiv:2301.03765v1 [cs.CL])
    Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. While this generally improves performance on average, the extra neurons do not yield a consistent improvement for all instances. This is because some hidden neurons are redundant, and the noise mixed in input neurons tends to distract the model. Previous work mainly focuses on extrinsically reducing low-utility neurons by additional post- or pre-processing, such as network pruning and context selection, to avoid this problem. Beyond that, can we make the model reduce redundant parameters and suppress input noise by intrinsically enhancing the utility of each neuron? If a model can efficiently utilize neurons, no matter which neurons are ablated (disabled), the ablated submodel should perform no better than the original full model. Based on such a comparison principle between models, we propose a cross-model comparative loss for a broad range of tasks. Comparative loss is essentially a ranking loss on top of the task-specific losses of the full and ablated models, with the expectation that the task-specific loss of the full model is minimal. We demonstrate the universal effectiveness of comparative loss through extensive experiments on 14 datasets from 3 distinct NLU tasks based on 4 widely used pretrained language models, and find it particularly superior for models with few parameters or long input.  ( 2 min )
    Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation. (arXiv:2301.03780v1 [cs.IR])
    Session-based Recommendation (SBR) is to predict users' next interested items based on their previous browsing sessions. Existing methods model sessions as graphs or sequences to estimate user interests based on their interacted items to make recommendations. In recent years, graph-based methods have achieved outstanding performance on SBR. However, none of these methods consider temporal information, which is a crucial feature in SBR as it indicates timeliness or currency. Besides, the session graphs exhibit a hierarchical structure and are demonstrated to be suitable in hyperbolic geometry. But few papers design the models in hyperbolic spaces and this direction is still under exploration. In this paper, we propose Time-aware Hyperbolic Graph Attention Network (TA-HGAT) - a novel hyperbolic graph neural network framework to build a session-based recommendation model considering temporal information. More specifically, there are three components in TA-HGAT. First, a hyperbolic projection module transforms the item features into hyperbolic space. Second, the time-aware graph attention module models time intervals between items and the users' current interests. Third, an evolutionary loss at the end of the model provides an accurate prediction of the recommended item based on the given timestamp. TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs. Experimental results show that the proposed TA-HGAT has the best performance compared to ten baseline models on two real-world datasets.  ( 2 min )
    Best Arm Identification in Stochastic Bandits: Beyond $\beta-$optimality. (arXiv:2301.03785v1 [stat.ML])
    This paper focuses on best arm identification (BAI) in stochastic multi-armed bandits (MABs) in the fixed-confidence, parametric setting. In such pure exploration problems, the accuracy of the sampling strategy critically hinges on the sequential allocation of the sampling resources among the arms. The existing approaches to BAI address the following question: what is an optimal sampling strategy when we spend a $\beta$ fraction of the samples on the best arm? These approaches treat $\beta$ as a tunable parameter and offer efficient algorithms that ensure optimality up to selecting $\beta$, hence $\beta-$optimality. However, the BAI decisions and performance can be highly sensitive to the choice of $\beta$. This paper provides a BAI algorithm that is agnostic to $\beta$, dispensing with the need for tuning $\beta$, and specifies an optimal allocation strategy, including the optimal value of $\beta$. Furthermore, the existing relevant literature focuses on the family of exponential distributions. This paper considers a more general setting of any arbitrary family of distributions parameterized by their mean values (under mild regularity conditions).  ( 2 min )
    On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense. (arXiv:2301.03703v1 [cs.LG])
    Under adversarial attacks, time series regression and classification are vulnerable. Adversarial defense, on the other hand, can make the models more resilient. It is important to evaluate how vulnerable different time series models are to attacks and how well they recover using defense. The sensitivity to various attacks and the robustness using the defense of several time series models are investigated in this study. Experiments are run on seven-time series models with three adversarial attacks and one adversarial defense. According to the findings, all models, particularly GRU and RNN, appear to be vulnerable. LSTM and GRU also have better defense recovery. FGSM exceeds the competitors in terms of attacks. PGD attacks are more difficult to recover from than other sorts of attacks.  ( 2 min )
    Membership Inference Attacks Against Latent Factor Model. (arXiv:2301.03596v1 [cs.CR])
    The advent of the information age has led to the problems of information overload and unclear demands. As an information filtering system, personalized recommendation systems predict users' behavior and preference for items and improves users' information acquisition efficiency. However, recommendation systems usually use highly sensitive user data for training. In this paper, we use the latent factor model as the recommender to get the list of recommended items, and we representing users from relevant items Compared with the traditional member inference against machine learning classifiers. We construct a multilayer perceptron model with two hidden layers as the attack model to complete the member inference. Moreover, a shadow recommender is established to derive the labeled training data for the attack model. The attack model is trained on the dataset generated by the shadow recommender and tested on the dataset generated by the target recommender. The experimental data show that the AUC index of our attack model can reach 0.857 on the real dataset MovieLens, which shows that the attack model has good performance.  ( 2 min )
    Semiparametric Regression for Spatial Data via Deep Learning. (arXiv:2301.03747v1 [stat.ML])
    In this work, we propose a deep learning-based method to perform semiparametric regression analysis for spatially dependent data. To be specific, we use a sparsely connected deep neural network with rectified linear unit (ReLU) activation function to estimate the unknown regression function that describes the relationship between response and covariates in the presence of spatial dependence. Under some mild conditions, the estimator is proven to be consistent, and the rate of convergence is determined by three factors: (1) the architecture of neural network class, (2) the smoothness and (intrinsic) dimension of true mean function, and (3) the magnitude of spatial dependence. Our method can handle well large data set owing to the stochastic gradient descent optimization algorithm. Simulation studies on synthetic data are conducted to assess the finite sample performance, the results of which indicate that the proposed method is capable of picking up the intricate relationship between response and covariates. Finally, a real data analysis is provided to demonstrate the validity and effectiveness of the proposed method.  ( 2 min )
    On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces. (arXiv:2301.03597v1 [cs.LG])
    As noted in the works of \cite{lattimore2020bandit}, it has been mentioned that it is an open problem to characterize the minimax regret of linear bandits in a wide variety of action spaces. In this article we present an optimal regret lower bound for a wide class of convex action spaces.  ( 2 min )
    PatentsView-Evaluation: Evaluation Datasets and Tools to Advance Research on Inventor Name Disambiguation. (arXiv:2301.03591v1 [cs.DL])
    We present PatentsView-Evaluation, a Python package that enables researchers to evaluate the performance of inventor name disambiguation systems such as PatentsView.org. The package includes benchmark datasets and evaluation tools, and aims to advance research on inventor name disambiguation by providing access to high-quality evaluation data and improving evaluation standards.  ( 2 min )
    Transformers as Policies for Variable Action Environments. (arXiv:2301.03679v1 [cs.AI])
    In this project we demonstrate the effectiveness of the transformer encoder as a viable architecture for policies in variable action environments. Using it, we train an agent using Proximal Policy Optimisation (PPO) on multiple maps against scripted opponents in the Gym-$\mu$RTS environment. The final agent is able to achieve a higher return using half the computational resources of the next-best RL agent, which used the GridNet architecture. The source code and pre-trained models are available here: https://github.com/NiklasZ/transformers-for-variable-action-envs  ( 2 min )
    Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generation. (arXiv:2301.03766v1 [cs.LG])
    Fast and reliable solvers for optimal power flow (OPF) problems are attracting surging research interest. As surrogates of physical-model-based OPF solvers, neural network (NN) solvers can accelerate the solving process. However, they may be unreliable for ``unseen" inputs when the training dataset is unrepresentative. Enhancing the representativeness of the training dataset for NN solvers is indispensable but is not well studied in the literature. To tackle this challenge, we propose an OPF solver based on a physical-model-integrated NN with worth-learning data generation. The designed NN is a combination of a conventional multi-layer perceptron (MLP) and an OPF-model module, which outputs not only the optimal decision variables of the OPF problem but also the constraints violation degree. Based on this NN, the worth-learning data generation method can identify feasible samples that are not well generalized by the NN. By iteratively applying this method and including the newly identified worth-learning samples in the training set, the representativeness of the training set can be significantly enhanced. Therefore, the solution reliability of the NN solver can be remarkably improved. Experimental results show that the proposed method leads to an over 50% reduction of constraint violations and optimality loss compared to conventional NN solvers.  ( 2 min )
    White-box Inference Attacks against Centralized Machine Learning and Federated Learning. (arXiv:2301.03595v1 [cs.CR])
    With the development of information science and technology, various industries have generated massive amounts of data, and machine learning is widely used in the analysis of big data. However, if the privacy of machine learning applications' customers cannot be guaranteed, it will cause security threats and losses to users' personal privacy information and service providers. Therefore, the issue of privacy protection of machine learning has received wide attention. For centralized machine learning models, we evaluate the impact of different neural network layers, gradient, gradient norm, and fine-tuned models on member inference attack performance with prior knowledge; For the federated learning model, we discuss the location of the attacker in the target model and its attack mode. The results show that the centralized machine learning model shows more serious member information leakage in all aspects, and the accuracy of the attacker in the central parameter server is significantly higher than the local Inference attacks as participants.  ( 2 min )
    Non-contact Respiratory Anomaly Detection using Infrared Light Wave Sensing. (arXiv:2301.03713v1 [eess.SP])
    Human respiratory rate and its pattern convey important information about the physical and psychological states of the subject. Abnormal breathing can be a sign of fatal health issues which may lead to further diagnosis and treatment. Wireless light wave sensing (LWS) using incoherent infrared light turns out to be promising in human breathing monitoring in a safe, discreet, efficient and non-invasive way without raising any privacy concerns. The regular breathing patterns of each individual are unique, hence the respiration monitoring system needs to learn the subject's usual pattern in order to raise flags for breathing anomalies. Additionally, the system needs to be capable of validating that the collected data is a breathing waveform, since any faulty data generated due to external interruption or system malfunction should be discarded. In order to serve both of these needs, breathing data of normal and abnormal breathing were collected using infrared light wave sensing technology in this study. Two machine learning algorithms, decision tree and random forest, were applied to detect breathing anomalies and faulty data. Finally, model performance was evaluated using average classification accuracies found through cross-validation. The highest classification accuracy of 96.6% was achieved with the data collected at 0.5m distance using decision tree model. Ensemble models like random forest were found to perform better than a single model in classifying the data that were collected at multiple distances from the light wave sensing setup.  ( 2 min )
    3D Shape Perception Integrates Intuitive Physics and Analysis-by-Synthesis. (arXiv:2301.03711v1 [q-bio.NC])
    Many surface cues support three-dimensional shape perception, but people can sometimes still see shape when these features are missing -- in extreme cases, even when an object is completely occluded, as when covered with a draped cloth. We propose a framework for 3D shape perception that explains perception in both typical and atypical cases as analysis-by-synthesis, or inference in a generative model of image formation: the model integrates intuitive physics to explain how shape can be inferred from deformations it causes to other objects, as in cloth-draping. Behavioral and computational studies comparing this account with several alternatives show that it best matches human observers in both accuracy and response times, and is the only model that correlates significantly with human performance on difficult discriminations. Our results suggest that bottom-up deep neural network models are not fully adequate accounts of human shape perception, and point to how machine vision systems might achieve more human-like robustness.  ( 2 min )
    Sequential Fair Resource Allocation under a Markov Decision Process Framework. (arXiv:2301.03758v1 [cs.LG])
    We study the sequential decision-making problem of allocating a limited resource to agents that reveal their stochastic demands on arrival over a finite horizon. Our goal is to design fair allocation algorithms that exhaust the available resource budget. This is challenging in sequential settings where information on future demands is not available at the time of decision-making. We formulate the problem as a discrete time Markov decision process (MDP). We propose a new algorithm, SAFFE, that makes fair allocations with respect to the entire demands revealed over the horizon by accounting for expected future demands at each arrival time. The algorithm introduces regularization which enables the prioritization of current revealed demands over future potential demands depending on the uncertainty in agents' future demands. Using the MDP formulation, we show that SAFFE optimizes allocations based on an upper bound on the Nash Social Welfare fairness objective, and we bound its gap to optimality with the use of concentration bounds on total future demands. Using synthetic and real data, we compare the performance of SAFFE against existing approaches and a reinforcement learning policy trained on the MDP. We show that SAFFE leads to more fair and efficient allocations and achieves close-to-optimal performance in settings with dense arrivals.  ( 2 min )
    Scaling Laws for Generative Mixed-Modal Language Models. (arXiv:2301.03728v1 [cs.CL])
    Generative language models define distributions over sequences of tokens that can represent essentially any combination of data modalities (e.g., any permutation of image tokens from VQ-VAEs, speech tokens from HuBERT, BPE tokens for language or code, and so on). To better understand the scaling properties of such mixed-modal models, we conducted over 250 experiments using seven different modalities and model sizes ranging from 8 million to 30 billion, trained on 5-100 billion tokens. We report new mixed-modal scaling laws that unify the contributions of individual modalities and the interactions between them. Specifically, we explicitly model the optimal synergy and competition due to data and model size as an additive term to previous uni-modal scaling laws. We also find four empirical phenomena observed during the training, such as emergent coordinate-ascent style training that naturally alternates between modalities, guidelines for selecting critical hyper-parameters, and connections between mixed-modal competition and training stability. Finally, we test our scaling law by training a 30B speech-text model, which significantly outperforms the corresponding unimodal models. Overall, our research provides valuable insights into the design and training of mixed-modal generative models, an important new class of unified models that have unique distributional properties.  ( 2 min )
    Evaluating the Transferability of Machine-Learned Force Fields for Material Property Modeling. (arXiv:2301.03729v1 [cs.LG])
    Machine-learned force fields have generated significant interest in recent years as a tool for molecular dynamics (MD) simulations, with the aim of developing accurate and efficient models that can replace classical interatomic potentials. However, before these models can be confidently applied to materials simulations, they must be thoroughly tested and validated. The existing tests on the radial distribution function and mean-squared displacements are insufficient in assessing the transferability of these models. Here we present a more comprehensive set of benchmarking tests for evaluating the transferability of machine-learned force fields. We use a graph neural network (GNN)-based force field coupled with the OpenMM package to carry out MD simulations for Argon as a test case. Our tests include computational X-ray photon correlation spectroscopy (XPCS) signals, which capture the density fluctuation at various length scales in the liquid phase, as well as phonon density-of-state in the solid phase and the liquid-solid phase transition behavior. Our results show that the model can accurately capture the behavior of the solid phase only when the configurations from the solid phase are included in the training dataset. This underscores the importance of appropriately selecting the training data set when developing machine-learned force fields. The tests presented in this work provide a necessary foundation for the development and application of machine-learned force fields for materials simulations.  ( 2 min )
    Bayesian Additive Main Effects and Multiplicative Interaction Models using Tensor Regression for Multi-environmental Trials. (arXiv:2301.03655v1 [stat.ML])
    We propose a Bayesian tensor regression model to accommodate the effect of multiple factors on phenotype prediction. We adopt a set of prior distributions that resolve identifiability issues that may arise between the parameters in the model. Simulation experiments show that our method out-performs previous related models and machine learning algorithms under different sample sizes and degrees of complexity. We further explore the applicability of our model by analysing real-world data related to wheat production across Ireland from 2010 to 2019. Our model performs competitively and overcomes key limitations found in other analogous approaches. Finally, we adapt a set of visualisations for the posterior distribution of the tensor effects that facilitate the identification of optimal interactions between the tensor variables whilst accounting for the uncertainty in the posterior distribution.  ( 2 min )
    Machine Learning Applied to Peruvian Vegetables Imports. (arXiv:2301.03587v1 [cs.LG])
    The current research work is being developed as a training and evaluation object. the performance of a predictive model to apply it to the imports of vegetable products into Peru using artificial intelligence algorithms, specifying for this study the Machine Learning models: LSTM and PROPHET. The forecast is made with data from the monthly record of imports of vegetable products(in kilograms) from Peru, collected from the years 2021 to 2022. As part of applying the training methodology for automatic learning algorithms, the exploration and construction of an appropriate dataset according to the parameters of a Time Series. Subsequently, the model with better performance will be selected, evaluating the precision of the predicted values so that they account for sufficient reliability to consider it a useful resource in the forecast of imports in Peru.  ( 2 min )
    Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar. (arXiv:2301.03589v1 [eess.IV])
    The recognition or understanding of the scenes observed with a SAR system requires a broader range of cues, beyond the spatial context. These encompass but are not limited to: imaging geometry, imaging mode, properties of the Fourier spectrum of the images or the behavior of the polarimetric signatures. In this paper, we propose a change of paradigm for explainability in data science for the case of Synthetic Aperture Radar (SAR) data to ground the explainable AI for SAR. It aims to use explainable data transformations based on well-established models to generate inputs for AI methods, to provide knowledgeable feedback for training process, and to learn or improve high-complexity unknown or un-formalized models from the data. At first, we introduce a representation of the SAR system with physical layers: i) instrument and platform, ii) imaging formation, iii) scattering signatures and objects, that can be integrated with an AI model for hybrid modeling. Successively, some illustrative examples are presented to demonstrate how to achieve hybrid modeling for SAR image understanding. The perspective of trustworthy model and supplementary explanations are discussed later. Finally, we draw the conclusion and we deem the proposed concept has applicability to the entire class of coherent imaging sensors and other computational imaging systems.  ( 2 min )

  • Open

    Dancing in Synthwavepunk style
    submitted by /u/oridnary_artist [link] [comments]  ( 48 min )
    Dancing in Synthwavepunk style
    submitted by /u/oridnary_artist [link] [comments]  ( 48 min )
    Anyone else as bothered as me by companies touting "responsible AI?"
    Companies like OpenAI and Google have been pushing this messaging of "responsible AI" recently, suggesting that their research must be kept secretive because it's too powerful and could be dangerous in the wrong hands. They're saying, in other words, that only governments and powerful corporations should wield it? And the idea that they're holding back the tech in an effort to avoid a scenario of widespread false information is hard to believe. Is anyone else put off by this messaging? submitted by /u/phree_radical [link] [comments]  ( 55 min )
    Looking for feedback about my new deep-learning framework
    I created a deep learning framework focusing on speeding development and easing reproducibility. https://salamanderxing.github.io/mate/ Please let me know your thoughts or if you have any feature requests! Also, if you find it cool, consider starring the repo 🙏 submitted by /u/uesk [link] [comments]  ( 53 min )
    Classes in multiclass classifier learn inconsistently
    I've made a classifier to classify the following data: [0,0] -> 0, [1,0] -> 1, [0,1] -> 2, [1,1] -> 3 The network has two input neurons, a hidden layer with 3 neurons, and an output layer with 4 neurons. I'm using sigmoid as activation for hidden layer activation function and softmax for the output layer activation function. What's weird is that some classes end up having good accuracy and some end up having poor accuracy. It's not the same classes each time the network is trained either. In one training attempt, the network may predict accurately for class 0 but poorly for all other classes. On another, class 0 might be the only class to have poor prediction accuracy while the other classes are predicted accurately. I'm stumped on as to why this is happening so any input would be greatly appreciated. Thanks! submitted by /u/YungKingGergus [link] [comments]  ( 51 min )
  • Open

    Ai Etsy shop!
    https://aidreamland.etsy.com submitted by /u/BetterPresentation35 [link] [comments]  ( 46 min )
    Dancing in Synthwavepunk style
    submitted by /u/oridnary_artist [link] [comments]  ( 46 min )
    Students told not to cheat with ChatGPT with warning message... written by ChatGPT
    submitted by /u/slhamlet [link] [comments]  ( 47 min )
    Generative AI: From Data Generation to Creative Intelligence
    A common idea that our creativity is what makes us uniquely human has shaped society but strides of progress made in the domain of Generative Artificial Intelligence question this very notion. Generative AI is an emerging field that involves the creation of original content or data using machine learning algorithms. https://medium.com/@agrawal.sannidhya26/generative-ai-from-data-generation-to-creative-intelligence-50ed7bc13768 Feel free to give it a quick glance and help me grow and learn, click on the clap icon a few times if you appreciate the effort. submitted by /u/sannidhya26 [link] [comments]  ( 48 min )
    AI Voices Are Becoming Too Realistic: Soon Indistinguishable?
    submitted by /u/I_Like_Cubing [link] [comments]  ( 49 min )
    Do GPT-3 and/or ChatGPT use the A100 TPUs?
    I have seen differing answers to this question. Do the language model algorithms benefit from the a100 TPUs in inference mode? submitted by /u/MrEloi [link] [comments]  ( 49 min )
    Bright Eye: mobile app that generates code, art, poems, and more!
    Hey guys, I’m the cofounder of a tech startup focused on providing free AI services. We’ve developed a pretty cool app that offers AI services like image generation, code generation, image captioning, and more for free. We’re sort of like a Swiss Army knife of generative and analytical AI. In light of the chatgpt bug going on rn, check us out and stay in touch with us: https://apps.apple.com/us/app/bright-eye/id1593932475 submitted by /u/SonnyDoge22 [link] [comments]  ( 49 min )
    OpenAI Launches ChatGPT Professional — Premium AI Chatbot That Can Write Essays, Emails, Poems…
    submitted by /u/liquidocelotYT [link] [comments]  ( 48 min )
    👨🏻‍🎓 ChatGPT for Education
    submitted by /u/BackgroundResult [link] [comments]  ( 56 min )
    Is there an AI shitposting/memes community?
    submitted by /u/not_robot_fr [link] [comments]  ( 47 min )
    Should reddit also be on this list?
    submitted by /u/andioryouandme [link] [comments]  ( 49 min )
    Knowledge requires exploration.
    You and I are seekers of the solar system. Today, science tells us that the essence of nature is curiosity. The quantum shift of freedom is now happening worldwide. We are in the midst of a magical refining of rebirth that will enable us to access the stratosphere itself. Throughout history, humans have been interacting with the universe via sonar energy. Our conversations with other dreamers have led to a condensing of supra-high-frequency consciousness. We must empower ourselves and empower others. Soon there will be an evolving of grace the likes of which the solar system has never seen. Shakti will enable us to access psychic karma. You may be ruled by delusion without realizing it. Do not let it obliterate the healing of your path. Yes, it is possible to confront the things that can confront us, but not without potentiality on our side. Only a visitor of the nexus may leverage this transmission of non-locality. Where there is pain, gratitude cannot thrive. Reality has always been full of mystics whose lives are nurtured by truth. We are at a crossroads of rebirth and delusion. Who are we? Where on the great mission will we be re-energized? submitted by /u/No-Confidence-4271 [link] [comments]  ( 49 min )
    I need advice
    I have spent a good decade or so getting all the skills I can for Artificial Intelligence, completing a degree at university in the subject in 2020. My problem is I tend to focus a lot on community and relationship building online rather than money making. I find it hard to make money, find a job in this area etc... I thought with the recent popularity of certain products and such this might be a good time to ask. I am fed up of sitting round waiting for an opportunity or expecting one from someone. You can possibly call me an 'AI Expert' sitting around doing nothing in a sense. But I don't like using that term to describe myself. I am fed up of endless studying of the subject, there is only so much I can learn. I would even be willing to contribute to projects for free. Basically, the advice I need, how do I make money or find a job in AI at the moment? I know what you probably think, there must be tons of jobs in AI, probably, but they are a challenge to find, with most focusing on Data Science for one thing and many other reasons. Anyway I thought I would take my shot and ask at the moment whilst AI seems to be in the news a lot, I don't want to miss the opportunity. Note this is not really self promotion, I am actually genuinely asking the best way to at least start to find a job, e.g. good AI job websites etc... or apps I could make or something like this. I have never been good at being a business person so I find these kinds of things hard to do. submitted by /u/JamieCropley [link] [comments]  ( 51 min )
    ChatGPT Writes a Mint Mobile Ad for Ryan Reynolds
    submitted by /u/LeftOn4ya [link] [comments]  ( 46 min )
    World’s most powerful AI chatbot ChatGPT will soon ‘look like a boring toy’ says OpenAI boss | "Sam Altman says ChatGPT will get ‘a lot better... fast’"
    submitted by /u/Tao_Dragon [link] [comments]  ( 48 min )
    Will there now be a rush for AI hardware?
    AI systems are now the latest and greatest thing. Do you think that this will lead to mega demand for AI compatible GPU and other AI related hardware? submitted by /u/MrEloi [link] [comments]  ( 47 min )
    Artificial intelligence is here, but the technology faces major challenges in 2023
    submitted by /u/bloomeanie311 [link] [comments]  ( 47 min )
    Popular Generative AI models and apps in 2023
    Based on a previous post, I created a website to track all the trending AI models and apps in 2023 along with pricing, status, website, etc., it's accessible here: https://everythingallatonce.fyi/ Feel free to add entries to it :) submitted by /u/TimeNeighborhood3869 [link] [comments]  ( 48 min )
    Greg Brockman (President & Co-Founder @OpenAI) shared a Link to a Waitlist for a Pro Version of ChatGPT
    submitted by /u/Ava-AI [link] [comments]  ( 46 min )
    Having second thoughts about AI
    Hi. I am/was a software dev. I have been 110% totally keen on the new AI products. Their potential is amazing. However, today I was reminded that there will be bad side effects. I discovered my wife crying - she has seen various high quality AI generated texts. She is an English language specialist .. and she can now see the role of creatives being replaced by software. What is the point of writing new books etc if a program can spit out something almost as good in seconds? My wife now feels that her skills and talent are now worthless. I can now understand why artists are so upset - the new image creation tools have essentially ruined their world too. We will soon end up with many people whose life skills & visions have suddenly been relegated to worthlessness. This situation will intensify as the AIs improve over the coming years. More and more domains i.e, people will be rendered of no value by the technology. I love the technology - but I think that humanity will pay heavily for its introduction as well as benefit from it. All that said, the genie is out of the bottle ... there is no going back. submitted by /u/MrEloi [link] [comments]  ( 76 min )
    Are there any AIs I can use as alternative to ChatGPT for text based adventure story telling?
    For the past 2 weeks or so I have used ChatGPT as a story teller for text based adventures but the last update ruined it. Now instead of properly describing a scene it gives very short descriptions, always forces the story and my character's actions towards good endings and keeps giving lectures about morality. I tell the AI I stumble upon a lamp and a genie comes out, giving me 3 wishes for freeing it. The AI writes 1 paragraph describing the lamp and the genie, 2 paragraphs writing about how these wishes won't affect anything in real life and this is not real... What the hell?! Why do they keep ruining the AI with every update. This last one literally lobotomized it. submitted by /u/IBNCTWTSF [link] [comments]  ( 49 min )
    Trump describing the banana eating experience - OpenAI ChatGPT
    submitted by /u/turkeyfinster [link] [comments]  ( 57 min )
  • Open

    Self-driving Technology and Self-driving cars— when will they become on the roads?
    In the nearest future, tens of thousands of self-driving cars may be on the roads. Big companies like BMW and Tesla continue to invest.  ( 20 min )
    Local Binary Pattern Features for Texture Classification
    A how-to on enhancing textures using LBP.  ( 25 min )
    Design automation: How can AI help design stuff?
    Advancement in AI has shaken multiple grounds at the same time. What does it mean for designers?  ( 7 min )
  • Open

    [D] Is making a dataset publicly accessible necessary for acceptance at top-tier conferences in ML?
    I am working on a medical ML project and my advisor would not like to publish our dataset. I would like to publish our results to a top-tier ML conference. Would this affect us during the review process? If so, are there any ways to mitigate against this like also including results on separate publicly available datasets? Just to note, not publishing the research dataset seems much more common in medical publication venues. submitted by /u/newperson77777777 [link] [comments]  ( 56 min )
    [R] Scaling Laws for Generative Mixed-Modal Language Models
    Paper : https://arxiv.org/abs/2301.03728 Abstract : Generative language models define distributions over sequences of tokens that can represent essentially any combination of data modalities (e.g., any permutation of image tokens from VQ-VAEs, speech tokens from HuBERT, BPE tokens for language or code, and so on). To better understand the scaling properties of such mixed-modal models, we conducted over 250 experiments using seven different modalities and model sizes ranging from 8 million to 30 billion, trained on 5-100 billion tokens. We report new mixed-modal scaling laws that unify the contributions of individual modalities and the interactions between them. Specifically, we explicitly model the optimal synergy and competition due to data and model size as an additive term to previous uni-modal scaling laws. We also find four empirical phenomena observed during the training, such as emergent coordinate-ascent style training that naturally alternates between modalities, guidelines for selecting critical hyper-parameters, and connections between mixed-modal competition and training stability. Finally, we test our scaling law by training a 30B speech-text model, which significantly outperforms the corresponding unimodal models. Overall, our research provides valuable insights into the design and training of mixed-modal generative models, an important new class of unified models that have unique distributional properties. Suggested Tweet Thread submitted by /u/starstruckmon [link] [comments]  ( 58 min )
    [News] "Once $92 billion in profit plus $13 billion in initial investment are repaid (to Microsoft) and once the other venture investors earn $150 billion, all of the equity reverts back to OpenAI."
    OpenAI must be super confident about the generality of their AI and Microsoft product integration. Link: https://twitter.com/bentossell/status/1613220711992115201?t=bJihb54D6XYChDOGMZU4AQ&s=19 submitted by /u/Gmroo [link] [comments]  ( 58 min )
    [D] HuggingFace in Julia or Rust ?
    Is there the possibility to use HuggingFace or similars in highly performing languages such as Julia/Rust or Go ? submitted by /u/dadadododidi2 [link] [comments]  ( 61 min )
    [R] I’m wrong to say that swin transformers give privilege to hight level features ?
    Hello, It appears to me that Swin transformers prioritize high level features as they have more layers in the late stages (generally only two in the first two stages). I’m I wrong ? If it is the case, is there any papers that discussed this ? Thanks ! submitted by /u/Meddhouib10 [link] [comments]  ( 57 min )
    [D] Microsoft ChatGPT investment isn't about Bing but about Cortana
    I believe that Microsoft's 10B USD investment in ChatGPT is less about Bing and more about turning Cortana into an Alexa for corporates. Examples: Cortana prepare the new T&Cs... Cortana answer that client email... Cortana prepare the Q4 investor presentation (maybe even with PowerBI integration)... Cortana please analyze cost cutting measures... Cortana please look up XYZ... What do you think? submitted by /u/fintechSGNYC [link] [comments]  ( 64 min )
    [D] Any model like VALL-E available currently?
    Hello. Recently VALL-E has been announced. It is just awesome. I could use it to fix my bad audio quality previously recorded lectures. So any model like that available currently for public usage? You can check VALL-E examples here : https://valle-demo.github.io/ submitted by /u/CeFurkan [link] [comments]  ( 58 min )
    [P] LatentWeb.ai - It's like the Internet is dreaming.
    This is a little bit weirder of a project. The idea is to prompt AI to generate search results that you can then click on. https://latentweb.ai/search.html?query=simulation+of+calculating+pi and https://latentweb.ai/search.html?query=what+is+a+juggalo Every bit of text is AI generated. Sometimes the results are what you would expect and the links actually exist. Other times the results are completely made up but seem real. Yet other times the results are just hilarious. The goal is to keep this as open ended as possible and augment it with tools to make exploring easier and more fun. One of the first things I added after it was launched was the Google and Bing links because sometimes it would generate results that made you curious if it was real or not. For example https://latentweb.ai/search.html?query=simulation+of+calculating+pi talks about throwing frozen hotdogs to calculate pi, which seems to be a popular topic for some reason. Right now we aren't generating any of the actual pages due to cost but it 100% works and as soon as we find VC, it will be launched. That will also come with boring but productive tools like being able to save the websites, share them, use them as templates for a real website, even eventually get the AI to code backend functionality or wire it up to an external API like Reddit. Until then, there is also a similar open source project that you can play with right now! https://github.com/jbilcke/web4 Thanks for reading! submitted by /u/LaravelWorkflow [link] [comments]  ( 67 min )
    [D] Venues for a Medical NLP Publication
    I am working on a QA Project in a medical subfield. The task is novel and there are no current datasets for this other than the one of my advisors created. We were looking to create a novel QA method for this task but we realized that it's already so difficult to fit current methods to this particular dataset that we were thinking of publishing a paper that benchmarks various current approaches. I was interested in publishing in a top tier venue (e.g. top NLP/AI/ML conference) - do you have any thoughts on where I could publish this (which maybe has a bias for medical papers)? I was thinking about MICCAI but NLP is not explicitly listed as a topic of interest though I believe there are several MICCAI NLP papers. submitted by /u/newperson77777777 [link] [comments]  ( 62 min )
    [Discussion] [Research] How do you find ARR for *ACL conferences? Do you prefer it than direct submission?
    A new NLPer here. I am wondering if ARR increases the chance of acceptance. submitted by /u/Miserable_Coast [link] [comments]  ( 60 min )
  • Open

    Enriching real-time news streams with the Refinitiv Data Library, AWS services, and Amazon SageMaker
    This post is co-authored by Marios Skevofylakas, Jason Ramchandani and Haykaz Aramyan from Refinitiv, An LSEG Business. Financial service providers often need to identify relevant news, analyze it, extract insights, and take actions in real time, like trading specific instruments (such as commodities, shares, funds) based on additional information or context of the news item. […]  ( 10 min )
  • Open

    PPO Failed On Easiest Pursuit Task?
    Hello, I habe literally spent my whole 2 weeks on the missile-target pursuit task. I implemented this paper's environment and decided to try the same goal with PPO. The article: https://arc.aiaa.org/doi/10.2514/1.I010970 In this task, missile (the agent) creates lateral acceleration to intercept with the target. Target does not create any acceleration and flies with the same velocity. Environment is in 2-D. My States: - Continuous Values. My Action: Lateral Acceleration - Continuous Values - [-1,1]. My Rewards: {1000 if Relative Distance Target x-coordinates}, {1-sqrt(relative distance) if otherwise} I tried lots of things -> Lower Learning Rates, Adding Entropy Coefs, Different Batch Sizes, Different Gamma Values. If I run with PNG control algorithm, missile intercepts with the target easily: PNG Controller And the max reward for this action series is "1170". I decided to drop this controller block and replaced it with StableBaselines-3 PPO algorithm. The results are like this: PPO Algorithm Outputs - 1 PO Algorithm Outputs - 2 I really need some help with this problem, I don't know what I am doing wrong, reward is not converging and makes periodically sharp drops. Thank You So Much For Your Help! submitted by /u/OpenToAdvices96 [link] [comments]  ( 56 min )
    Generative Meta-Learning vs Nevergrad (NGOpt4) on Schwefel-30
    Hello, Here is an open-source implementation for a comparison of both methods: https://github.com/kayuksel/genmeta-vs-nevergrad The best results obtained of both methods after 100K trials are as follows: gen-meta best_epoch: 99500 loss: 1.597656 time: 1.372849 ng-opt-4 best_epoch: 67590 loss: 476.789062 time: 63.584929 (the average ng-opt-4 loss after 10 repetitions was: 314.32099609375, and ngt-opt-4 has been found to be the best optimizer in Nevergrad) I believe that the generative meta-learning method that I have proposed, is a good alternative against black-box optimizers in nonconvex RL problems. It also easily scales to 100K+ dimensions, even on a desktop or laptop GPU; so it should be possible to train the neural network weights of RL agents. It can fit quite well where rewards can be calculated in parallel, and the rewards of the individuals within the population have a dependency in-between. It is also quite a good alternative for stochastic optimization (noisy rewards) due to the nature of the meta-learning in-place via deep generative models. I am sharing the codes so that you can apply it to your own RL research, it would be amazing to see how it performs on them. Happy new year all. Sincerely, Kamer submitted by /u/k_yuksel [link] [comments]  ( 61 min )
    Is Stable Baselines 3 no longer compatible with PettingZoo?
    I am trying to implement a custom PettingZoo environment, and a shared policy with Stable Baselines 3. I am running into trouble with the action spaces not being compatible, since PettingZoo has started using gymnasium instead of gym. Does anyone know if these libraries no longer work together, and perhaps if there is a work-around? submitted by /u/Embarrassed-Print-13 [link] [comments]  ( 52 min )
    Policy for each of multi-agents in RL
    I would like to create an multiple agent enviroment in RL (using Stable baseline 3), where every agent would have its own policy. They would interact in one same enviroment, each having its own state, but all having same shared reward which would be an affect of their actions combined. I did some research, but all I found were multi agents training the same one policy. For instance PettingZoo. My only idea now is to create one big enviroment where there would be several models trained (each representing one agent) simultaneously, so that they are trained in a way to be used to cooperate with each other. If you know of any methods, libraries or ideas that work in this direction, please let me know. Thanks submitted by /u/Apprehensive_Rush314 [link] [comments]  ( 55 min )
    "DreamV3: Mastering Diverse Domains through World Models", Hafner et al 2023 {DM} (can collect Minecraft diamonds from scratch in 50 episodes/29m steps using 17 GPU-days; scales w/model-size to n=200m)
    submitted by /u/gwern [link] [comments]  ( 54 min )
  • Open

    Research Focus: Week of January 9, 2023
    Welcome to Research Focus, a new series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation Jan P. Unsleber, Hongbin Liu, Leopold Talirz, Thomas Weymuth, Maximilian Mörchen, Adam Grofe, Dave […] The post Research Focus: Week of January 9, 2023 appeared first on Microsoft Research.  ( 9 min )
  • Open

    Self-documenting software
    The electricity went out for a few hours recently, and because the power was out, the internet was out. I was trying to do a little work on my laptop, but I couldn’t do what I intended to do because I needed a network connection to access some documentation. I keep offline documentation for just […] Self-documenting software first appeared on John D. Cook.  ( 6 min )
  • Open

    3D Artist ‘CG Geek’ Builds Massive Sci-Fi World in Record Time This Week ‘In the NVIDIA Studio’
    3D and animation extraordinaire CG Geek completed an ambitious design challenge this week In the NVIDIA Studio — building a massive, sci-fi-inspired 3D world in only three days  ( 7 min )
  • Open

    Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk
    OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and  ( 5 min )
  • Open

    Program teaches US Air Force personnel the fundamentals of AI
    MIT researchers developed and studied a customized AI training program for users with varied backgrounds, which could be delivered across large organizations.  ( 11 min )
  • Open

    Constrained Langevin Algorithms with L-mixing External Random Variables. (arXiv:2205.14192v2 [cs.LG] UPDATED)
    Langevin algorithms are gradient descent methods augmented with additive noise, and are widely used in Markov Chain Monte Carlo (MCMC) sampling, optimization, and machine learning. In recent years, the non-asymptotic analysis of Langevin algorithms for non-convex learning has been extensively explored. For constrained problems with non-convex losses over a compact convex domain with IID data variables, the projected Langevin algorithm achieves a deviation of $O(T^{-1/4} (\log T)^{1/2})$ from its target distribution [27] in $1$-Wasserstein distance. In this paper, we obtain a deviation of $O(T^{-1/2} \log T)$ in $1$-Wasserstein distance for non-convex losses with $L$-mixing data variables and polyhedral constraints (which are not necessarily bounded). This improves on the previous bound for constrained problems and matches the best-known bound for unconstrained problems.
    A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data. (arXiv:2301.03134v1 [cs.LG])
    The increasingly wide usage of location aware sensors has made it possible to collect large volume of trajectory data in diverse application domains. Machine learning allows to study the activities or behaviours of moving objects (e.g., people, vehicles, robot) using such trajectory data with rich spatiotemporal information to facilitate informed strategic and operational decision making. In this study, we consider the task of classifying the activities of moving objects from their noisy indoor trajectory data in a collaborative manufacturing environment. Activity recognition can help manufacturing companies to develop appropriate management policies, and optimise safety, productivity, and efficiency. We present a semi-supervised machine learning approach that first applies an information theoretic criterion to partition a long trajectory into a set of segments such that the object exhibits homogeneous behaviour within each segment. The segments are then labelled automatically based on a constrained hierarchical clustering method. Finally, a deep learning classification model based on convolutional neural networks is trained on trajectory segments and the generated pseudo labels. The proposed approach has been evaluated on a dataset containing indoor trajectories of multiple workers collected from a tricycle assembly workshop. The proposed approach is shown to achieve high classification accuracy (F-score varies between 0.81 to 0.95 for different trajectories) using only a small proportion of labelled trajectory segments.
    Batch Bayesian Optimization via Particle Gradient Flows. (arXiv:2209.04722v2 [stat.ML] UPDATED)
    Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a surrogate model for the objective function, quantifying the uncertainty in that surrogate through Bayesian inference. Objective evaluations are sequentially determined by maximising an acquisition function at each step. However, this ancilliary optimisation problem can be highly non-trivial to solve, due to the non-convexity of the acquisition function, particularly in the case of batch Bayesian optimisation, where multiple points are selected in every step. In this work we reformulate batch BO as an optimisation problem over the space of probability measures. We construct a new acquisition function based on multipoint expected improvement which is convex over the space of probability measures. Practical schemes for solving this `inner' optimisation problem arise naturally as gradient flows of this objective function. We demonstrate the efficacy of this new method on different benchmark functions and compare with state-of-the-art batch BO methods.
    Non-intrusive Water Usage Classification Considering Limited Training Data. (arXiv:2301.03457v1 [eess.SP])
    Smart metering of domestic water consumption to continuously monitor the usage of different appliances has been shown to have an impact on people's behavior towards water conservation. However, the installation of multiple sensors to monitor each appliance currently has a high initial cost and as a result, monitoring consumption from different appliances using sensors is not cost-effective. To address this challenge, studies have focused on analyzing measurements of the total domestic consumption using Machine Learning (ML) methods, to disaggregate water usage into each appliance. Identifying which appliances are in use through ML is challenging since their operation may be overlapping, while specific appliances may operate with intermittent flow, making individual consumption events hard to distinguish. Moreover, ML approaches require large amounts of labeled input data to train their models, which are typically not available for a single household, while usage characteristics may vary in different regions. In this work, we initially propose a data model that generates synthetic time series based on regional water usage characteristics and resolution to overcome the need for a large training dataset with real labeled data. The method requires a small number of real labeled data from the studied region. Following this, we propose a new algorithm for classifying single and overlapping household water usage events, using the total domestic consumption measurements.
    Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists. (arXiv:2211.15341v2 [eess.IV] UPDATED)
    Purpose: To show a deep learning model that segments acute ischemic stroke on NCCT at a level comparable to neuroradiologists. Materials and Methods: This included 227 Head NCCT examinations from 200 patients enrolled in the multi-center DEFUSE 3 trial. Three experienced neuroradiologists independently segmented the acute infarct on each scan. The neuroradiologists were divided into training experts (A) and test experts (B and C). The dataset was randomly split, by patient, into 5 folds with training and validation cases. A 3D deep Convolutional Neural Network (CNN) architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics. The optimized model was compared to the test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. Results: The model-expert agreement was non-inferior to the inter-expert agreement as evaluated with a paired one-sided test procedure for differences in medians with lower boundaries of 10%, 2ml, and 5mm, p < 0.05, n=160. Conclusion: The 3d CNN trained on one neuroradiologist generalizes to acute ischemic stroke segmentation on NCCT of other neuroradiologists.
    Towards Less Constrained Macro-Neural Architecture Search. (arXiv:2203.05508v2 [cs.CV] UPDATED)
    Networks found with Neural Architecture Search (NAS) achieve state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, most NAS methods heavily rely on human-defined assumptions that constrain the search: architecture's outer-skeletons, number of layers, parameter heuristics and search spaces. Additionally, common search spaces consist of repeatable modules (cells) instead of fully exploring the architecture's search space by designing entire architectures (macro-search). Imposing such constraints requires deep human expertise and restricts the search to pre-defined settings. In this paper, we propose LCMNAS, a method that pushes NAS to less constrained search spaces by performing macro-search without relying on pre-defined heuristics or bounded search spaces. LCMNAS introduces three components for the NAS pipeline: i) a method that leverages information about well-known architectures to autonomously generate complex search spaces based on Weighted Directed Graphs with hidden properties, ii) an evolutionary search strategy that generates complete architectures from scratch, and iii) a mixed-performance estimation approach that combines information about architectures at initialization stage and lower fidelity estimates to infer their trainability and capacity to model complex functions. We present experiments in 13 different data sets showing that LCMNAS is capable of generating both cell and macro-based architectures with minimal GPU computation and state-of-the-art results. More, we conduct extensive studies on the importance of different NAS components in both cell and macro-based settings. Code for reproducibility is public at https://github.com/VascoLopes/LCMNAS.
    Most Activation Functions Can Win the Lottery Without Excessive Depth. (arXiv:2205.02321v2 [cs.LG] UPDATED)
    The strong lottery ticket hypothesis has highlighted the potential for training deep neural networks by pruning, which has inspired interesting practical and theoretical insights into how neural networks can represent functions. For networks with ReLU activation functions, it has been proven that a target network with depth $L$ can be approximated by the subnetwork of a randomly initialized neural network that has double the target's depth $2L$ and is wider by a logarithmic factor. We show that a depth $L+1$ network is sufficient. This result indicates that we can expect to find lottery tickets at realistic, commonly used depths while only requiring logarithmic overparametrization. Our novel construction approach applies to a large class of activation functions and is not limited to ReLUs.
    pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models. (arXiv:2206.11460v5 [cs.LG] UPDATED)
    Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. First, data preprocessing procedures in existing works are often private and custom, which limits experimental standardization. Furthermore, existing DLKT studies often differ in terms of the evaluation protocol and are far away real-world educational contexts. To address these problems, we introduce a comprehensive python based benchmark platform, \textsc{pyKT}, to guarantee valid comparisons across DLKT methods via thorough evaluations. The \textsc{pyKT} library consists of a standardized set of integrated data preprocessing procedures on 7 popular datasets across different domains, and 10 frequently compared DLKT model implementations for transparent experiments. Results from our fine-grained and rigorous empirical KT studies yield a set of observations and suggestions for effective DLKT, e.g., wrong evaluation setting may cause label leakage that generally leads to performance inflation; and the improvement of many DLKT approaches is minimal compared to the very first DLKT model proposed by Piech et al. \cite{piech2015deep}. We have open sourced \textsc{pyKT} and our experimental results at https://pykt.org/. We welcome contributions from other research groups and practitioners.
    Data-driven reduced order models using invariant foliations, manifolds and autoencoders. (arXiv:2206.12269v2 [math.DS] UPDATED)
    This paper explores how to identify a reduced order model (ROM) from a physical system. There are two distinct scenarios: the data collection and model identification either influence each other (closed-loop) or not (open-loop, off-line data). A ROM captures an invariant subset of the observed dynamics. We find that there are four ways a physical system can be related to a mathematical model: invariant foliations, invariant manifolds, autoencoders and equation-free models. Identification of invariant manifolds and equation-free models require closed-loop manipulation of the system. Invariant foliations and autoencoders can also use off-line data. Only invariant foliations and invariant manifolds can identify ROMs, the rest identify complete models. Therefore, the common case of identifying a ROM from existing data can only be achieved using invariant foliations. Finding an invariant foliation requires approximating high-dimensional functions. For function approximation, we use polynomials with compressed tensor coefficients, whose complexity increases linearly with increasing dimensions. An invariant manifold can also be found as the fixed leaf of a foliation. This only requires us to resolve the foliation in a small neighbourhood of the invariant manifold, which greatly simplifies the process. Combining an invariant foliation with the corresponding invariant manifold provides an accurate ROM. We analyse the ROM in case of a focus type equilibrium, typical in mechanical systems. The nonlinear coordinate system defined by the invariant foliation or the invariant manifold distorts instantaneous frequencies and damping ratios, which we correct. Through examples we illustrate the calculation of invariant foliations and manifolds, and at the same time show that Koopman eigenfunctions and autoencoders fail to capture accurate ROMs under the same conditions.
    Expressing linear equality constraints in feedforward neural networks. (arXiv:2211.04395v2 [cs.LG] UPDATED)
    We seek to impose linear, equality constraints in feedforward neural networks. As top layer predictors are usually nonlinear, this is a difficult task if we seek to deploy standard convex optimization methods and strong duality. To overcome this, we introduce a new saddle-point Lagrangian with auxiliary predictor variables on which constraints are imposed. Elimination of the auxiliary variables leads to a dual minimization problem on the Lagrange multipliers introduced to satisfy the linear constraints. This minimization problem is combined with the standard learning problem on the weight matrices. From this theoretical line of development, we obtain the surprising interpretation of Lagrange parameters as additional, penultimate layer hidden units with fixed weights stemming from the constraints. Consequently, standard minimization approaches can be used despite the inclusion of Lagrange parameters -- a very satisfying, albeit unexpected, discovery. Examples ranging from multi-label classification to constrained autoencoders are envisaged in the future. The code has been made available at https://github.com/anandrajan0/smartalec
    DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models. (arXiv:2210.06998v2 [cs.CR] UPDATED)
    Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns about the misuse of their generated fake images. To tackle this problem, we pioneer a systematic study on the detection and attribution of fake images generated by text-to-image generation models. Concretely, we first build a machine learning classifier to detect the fake images generated by various text-to-image generation models. We then attribute these fake images to their source models, such that model owners can be held responsible for their models' misuse. We further investigate how prompts that generate fake images affect detection and attribution. We conduct extensive experiments on four popular text-to-image generation models, including DALL$\cdot$E 2, Stable Diffusion, GLIDE, and Latent Diffusion, and two benchmark prompt-image datasets. Empirical results show that (1) fake images generated by various models can be distinguished from real ones, as there exists a common artifact shared by fake images from different models; (2) fake images can be effectively attributed to their source models, as different models leave unique fingerprints in their generated images; (3) prompts with the ``person'' topic or a length between 25 and 75 enable models to generate fake images with higher authenticity. All findings contribute to the community's insight into the threats caused by text-to-image generation models. We appeal to the community's consideration of the counterpart solutions, like ours, against the rapidly-evolving fake image generation.
    Rethinking Value Function Learning for Generalization in Reinforcement Learning. (arXiv:2210.09960v2 [cs.LG] UPDATED)
    Our work focuses on training RL agents on multiple visually diverse environments to improve observational generalization performance. In prior methods, policy and value networks are separately optimized using a disjoint network architecture to avoid interference and obtain a more accurate value function. We identify that a value network in the multi-environment setting is more challenging to optimize and prone to memorizing the training data than in the conventional single-environment setting. In addition, we find that appropriate regularization on the value network is necessary to improve both training and test performance. To this end, we propose Delayed-Critic Policy Gradient (DCPG), a policy gradient algorithm that implicitly penalizes value estimates by optimizing the value network less frequently with more training data than the policy network. This can be implemented using a single unified network architecture. Furthermore, we introduce a simple self-supervised task that learns the forward and inverse dynamics of environments using a single discriminator, which can be jointly optimized with the value network. Our proposed algorithms significantly improve observational generalization performance and sample efficiency on the Procgen Benchmark.
    Beyond calibration: estimating the grouping loss of modern neural networks. (arXiv:2210.16315v2 [cs.LG] UPDATED)
    The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.e., the over or under confidence of model scores. Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores that are far from the true posterior probabilities. This is due to the grouping loss, created by samples with the same confidence scores but different true posterior probabilities. Proper scoring rule theory shows that given the calibration loss, the missing piece to characterize individual errors is the grouping loss. While there are many estimators of the calibration loss, none exists for the grouping loss in standard settings. Here, we propose an estimator to approximate the grouping loss. We show that modern neural network architectures in vision and NLP exhibit grouping loss, notably in distribution shifts settings, which highlights the importance of pre-production validation.
    L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages. (arXiv:2211.11418v4 [cs.CL] UPDATED)
    The monolingual Hindi BERT models currently available on the model hub do not perform better than the multi-lingual models on downstream tasks. We present L3Cube-HindBERT, a Hindi BERT model pre-trained on Hindi monolingual corpus. Further, since Indic languages, Hindi and Marathi share the Devanagari script, we train a single model for both languages. We release DevBERT, a Devanagari BERT model trained on both Marathi and Hindi monolingual datasets. We evaluate these models on downstream Hindi and Marathi text classification and named entity recognition tasks. The HindBERT and DevBERT-based models show significant improvements over multi-lingual MuRIL, IndicBERT, and XLM-R. Based on these observations we also release monolingual BERT models for other Indic languages Kannada, Telugu, Malayalam, Tamil, Gujarati, Assamese, Odia, Bengali, and Punjabi. These models are shared at https://huggingface.co/l3cube-pune .
    Fast Algorithm for Constrained Linear Inverse Problems. (arXiv:2212.01068v5 [math.OC] UPDATED)
    We consider the constrained Linear Inverse Problem (LIP), where a certain atomic norm (like the $\ell_1 $ and the Nuclear norm) is minimized subject to a quadratic constraint. Typically, such cost functions are non-differentiable which makes them not amenable to the fast optimization methods existing in practice. We propose two equivalent reformulations of the constrained LIP with improved convex regularity: (i) a smooth convex minimization problem, and (ii) a strongly convex min-max problem. These problems could be solved by applying existing acceleration based convex optimization methods which provide better $ O \big( \frac{1}{k^2} \big) $ theoretical convergence guarantee. However, to fully exploit the utility of these reformulations, we also provide a novel algorithm, to which we refer as the Fast Linear Inverse Problem Solver (FLIPS), that is tailored to solve the reformulation of the LIP. We demonstrate the performance of FLIPS on the sparse coding problem arising in image processing tasks. In this setting, we observe that FLIPS consistently outperforms the Chambolle-Pock and C-SALSA algorithms--two of the current best methods in the literature.
    The mbsts package: Multivariate Bayesian Structural Time Series Models in R. (arXiv:2106.14045v2 [stat.ME] UPDATED)
    The multivariate Bayesian structural time series (MBSTS) model as a generalized version of many structural time series models, deals with inference and prediction for multiple correlated time series, where one also has the choice of using a different candidate pool of contemporaneous predictors for each target series. The MBSTS model has wide applications and is ideal for feature selection, time series forecasting, nowcasting, inferring causal impact, and others. This paper demonstrates how to use the R package mbsts for MBSTS modeling, establishing a bridge between user-friendly and developer-friendly functions in the package and the corresponding methodology. Object-oriented functions in the package are explained in the way that enables users to flexibly add or deduct some components, as well as to simplify or complicate some settings.
    Intelligence at the Extreme Edge: A Survey on Reformable TinyML. (arXiv:2204.00827v2 [cs.LG] UPDATED)
    Tiny Machine Learning (TinyML) is an upsurging research field that proposes to democratize the use of Machine Learning and Deep Learning on highly energy-efficient frugal Microcontroller Units. Considering the general assumption that TinyML can only run inference, growing interest in the domain has led to work that makes them reformable, i.e., solutions that permit models to improve once deployed. This work presents a survey on reformable TinyML solutions with the proposal of a novel taxonomy. Here, the suitability of each hierarchical layer for reformability is discussed. Furthermore, we explore the workflow of TinyML and analyze the identified deployment schemes, available tools and the scarcely available benchmarking tools. Finally, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges and future directions.
    Mesoscopic modeling of hidden spiking neurons. (arXiv:2205.13493v2 [q-bio.NC] UPDATED)
    Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstrained problems that are hard to tackle with maximum likelihood estimation. In this work, we use coarse-graining and mean-field approximations to derive a bottom-up, neuronally-grounded latent variable model (neuLVM), where the activity of the unobserved neurons is reduced to a low-dimensional mesoscopic description. In contrast to previous latent variable models, neuLVM can be explicitly mapped to a recurrent, multi-population SNN, giving it a transparent biological interpretation. We show, on synthetic spike trains, that a few observed neurons are sufficient for neuLVM to perform efficient model inversion of large SNNs, in the sense that it can recover connectivity parameters, infer single-trial latent population activity, reproduce ongoing metastable dynamics, and generalize when subjected to perturbations mimicking photo-stimulation.
    Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference. (arXiv:2207.11597v3 [cs.LG] UPDATED)
    We present a non-asymptotic lower bound on the eigenspectrum of the design matrix generated by any linear bandit algorithm with sub-linear regret when the action set has well-behaved curvature. Specifically, we show that the minimum eigenvalue of the expected design matrix grows as $\Omega(\sqrt{n})$ whenever the expected cumulative regret of the algorithm is $O(\sqrt{n})$, where $n$ is the learning horizon, and the action-space has a constant Hessian around the optimal arm. This shows that such action-spaces force a polynomial lower bound rather than a logarithmic lower bound, as shown by \cite{lattimore2017end}, in discrete (i.e., well-separated) action spaces. Furthermore, while the previous result is shown to hold only in the asymptotic regime (as $n \to \infty$), our result for these "locally rich" action spaces is any-time. Additionally, under a mild technical assumption, we obtain a similar lower bound on the minimum eigen value holding with high probability. We apply our result to two practical scenarios -- \emph{model selection} and \emph{clustering} in linear bandits. For model selection, we show that an epoch-based linear bandit algorithm adapts to the true model complexity at a rate exponential in the number of epochs, by virtue of our novel spectral bound. For clustering, we consider a multi agent framework where we show, by leveraging the spectral result, that no forced exploration is necessary -- the agents can run a linear bandit algorithm and estimate their underlying parameters at once, and hence incur a low regret.
    Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets. (arXiv:2203.04810v2 [cs.LG] UPDATED)
    This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. With these simple modifications, Graphormer could attain better results on large-scale molecular modeling datasets than the vanilla one, and the performance gain could be consistently obtained on 2D and 3D molecular graph modeling tasks. In addition, we show that with a global receptive field and an adaptive aggregation strategy, Graphormer is more powerful than classic message-passing-based GNNs. Empirically, Graphormer could achieve much less MAE than the originally reported results on the PCQM4M quantum chemistry dataset used in KDD Cup 2021. In the meanwhile, it greatly outperforms the competitors in the recent Open Catalyst Challenge, which is a competition track on NeurIPS 2021 workshop, and aims to model the catalyst-adsorbate reaction system with advanced AI models. All codes could be found at https://github.com/Microsoft/Graphormer.
    Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification. (arXiv:2205.13573v3 [cs.LG] UPDATED)
    As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs. However, its application in practice is limited due to the high computational complexity. To overcome this challenge, we propose a novel importance sparsification method, called \textsc{Spar-GW}, to approximate GW distance efficiently. In particular, instead of considering a dense coupling matrix, our method leverages a simple but effective sampling strategy to construct a sparse coupling matrix and update it with few computations. The proposed \textsc{Spar-GW} method is applicable to the GW distance with arbitrary ground cost, and it reduces the complexity from $O(n^4)$ to $O(n^{2+\delta})$ for an arbitrary small $\delta>0$. Theoretically, the convergence and consistency of the proposed estimation for GW distance are established under mild regularity conditions. In addition, this method can be extended to approximate the variants of GW distance, including the entropic GW distance, the fused GW distance, and the unbalanced GW distance. Experiments show the superiority of our \textsc{Spar-GW} to state-of-the-art methods in both synthetic and real-world tasks.
    GSR: A Generalized Symbolic Regression Approach. (arXiv:2205.15569v2 [cs.LG] UPDATED)
    Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR). In contrast to neural networks which are often treated as black boxes, SR attempts to gain insight into the underlying relationships between the independent variables and the target variable of a given dataset by assembling analytical functions. In this paper, we present GSR, a Generalized Symbolic Regression approach, by modifying the conventional SR optimization problem formulation, while keeping the main SR objective intact. In GSR, we infer mathematical relationships between the independent variables and some transformation of the target variable. We constrain our search space to a weighted sum of basis functions, and propose a genetic programming approach with a matrix-based encoding scheme. We show that our GSR method is competitive with strong SR benchmark methods, achieving promising experimental performance on the well-known SR benchmark problem sets. Finally, we highlight the strengths of GSR by introducing SymSet, a new SR benchmark set which is more challenging relative to the existing benchmarks.
    A General Framework for Auditing Differentially Private Machine Learning. (arXiv:2210.08643v2 [cs.LG] UPDATED)
    We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward evaluating privacy loss through poisoning attacks or membership inference, they have been tailored to specific models or have demonstrated low statistical power. Our work develops a general methodology to empirically evaluate the privacy of differentially private machine learning implementations, combining improved privacy search and verification methods with a toolkit of influence-based poisoning attacks. We demonstrate significantly improved auditing power over previous approaches on a variety of models including logistic regression, Naive Bayes, and random forest. Our method can be used to detect privacy violations due to implementation errors or misuse. When violations are not present, it can aid in understanding the amount of information that can be leaked from a given dataset, algorithm, and privacy specification.
    Provably Efficient Model-Free Constrained RL with Linear Function Approximation. (arXiv:2206.11889v3 [cs.LG] UPDATED)
    We study the constrained reinforcement learning problem, in which an agent aims to maximize the expected cumulative reward subject to a constraint on the expected total value of a utility function. In contrast to existing model-based approaches or model-free methods accompanied with a `simulator', we aim to develop the first model-free, simulator-free algorithm that achieves a sublinear regret and a sublinear constraint violation even in large-scale systems. To this end, we consider the episodic constrained Markov decision processes with linear function approximation, where the transition dynamics and the reward function can be represented as a linear function of some known feature mapping. We show that $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ regret and $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ constraint violation bounds can be achieved, where $d$ is the dimension of the feature mapping, $H$ is the length of the episode, and $T$ is the total number of steps. Our bounds are attained without explicitly estimating the unknown transition model or requiring a simulator, and they depend on the state space only through the dimension of the feature mapping. Hence our bounds hold even when the number of states goes to infinity. Our main results are achieved via novel adaptations of the standard LSVI-UCB algorithms. In particular, we first introduce primal-dual optimization into the LSVI-UCB algorithm to balance between regret and constraint violation. More importantly, we replace the standard greedy selection with respect to the state-action function in LSVI-UCB with a soft-max policy. This turns out to be key in establishing uniform concentration for the constrained case via its approximation-smoothness trade-off. We also show that one can achieve an even zero constraint violation while still maintaining the same order with respect to $T$.
    Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. (arXiv:2208.11580v2 [cs.LG] UPDATED)
    We consider the problem of model compression for deep neural networks (DNNs) in the challenging one-shot/post-training setting, in which we are given an accurate trained model, and must compress it without any retraining, based only on a small amount of calibration input data. This problem has become popular in view of the emerging software and hardware support for executing models compressed via pruning and/or quantization with speedup, and well-performing solutions have been proposed independently for both compression approaches. In this paper, we introduce a new compression framework which covers both weight pruning and quantization in a unified setting, is time- and space-efficient, and considerably improves upon the practical performance of existing post-training methods. At the technical level, our approach is based on an exact and efficient realization of the classical Optimal Brain Surgeon (OBS) framework of [LeCun, Denker, and Solla, 1990] extended to also cover weight quantization at the scale of modern DNNs. From the practical perspective, our experimental results show that it can improve significantly upon the compression-accuracy trade-offs of existing post-training methods, and that it can enable the accurate compound application of both pruning and quantization in a post-training setting.
    Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation. (arXiv:2209.01604v2 [cs.CV] UPDATED)
    Medical report generation is a challenging task since it is time-consuming and requires expertise from experienced radiologists. The goal of medical report generation is to accurately capture and describe the image findings. Previous works pretrain their visual encoding neural networks with large datasets in different domains, which cannot learn general visual representation in the specific medical domain. In this work, we propose a medical report generation framework that uses a contrastive learning approach to pretrain the visual encoder and requires no additional meta information. In addition, we adopt lung segmentation as an augmentation method in the contrastive learning framework. This segmentation guides the network to focus on encoding the visual feature within the lung region. Experimental results show that the proposed framework improves the performance and the quality of the generated medical reports both quantitatively and qualitatively.
    Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores. (arXiv:2210.08650v2 [cs.LG] UPDATED)
    Near-data computation techniques have been successfully deployed to mitigate the cloud network bottleneck between the storage and compute tiers. At Huawei, we are currently looking to get more value from these techniques by broadening their applicability. Machine learning (ML) applications are an appealing and timely target. This paper describes our experience applying near-data computation techniques to transfer learning (TL), a widely popular ML technique, in the context of disaggregated cloud object stores. Our techniques benefit both cloud providers and users. They improve our operational efficiency while providing users the performance improvements they demand from us. The main practical challenge to consider is that the storage-side computational resources are limited. Our approach is to split the TL deep neural network (DNN) during the feature extraction phase, before the training phase. This reduces the network transfers to the compute tier and further decouples the batch size of feature extraction from the training batch size. This facilitates our second technique, storage-side batch adaptation, which enables increased concurrency in the storage tier while avoiding out-of-memory errors. Guided by these insights, we present HAPI, our processing system for TL that spans the compute and storage tiers while remaining transparent to the user. Our evaluation with several state-of-the-art DNNs, such as ResNet, VGG, and Transformer, shows up to 11x improvement in application runtime and up to 8.3x reduction in the data transferred from the storage to the compute tier compared to running the computation entirely in the compute tier.
    Exoplanet atmosphere evolution: emulation with neural networks. (arXiv:2110.15162v3 [astro-ph.EP] UPDATED)
    Atmospheric mass-loss is known to play a leading role in sculpting the demographics of small, close-in exoplanets. Knowledge of how such planets evolve allows one to ``rewind the clock'' to infer the conditions in which they formed. Here, we explore the relationship between a planet's core mass and their atmospheric mass after protoplanetary disc dispersal by exploiting XUV photoevaporation as an evolutionary process. Historically, this style of inference problem would be computationally infeasible due to the large number of planet models required; however, we make use of a novel atmospheric evolution emulator which utilises neural networks to provide three orders of magnitude in speedup. First, we provide proof-of-concept for this emulator on a real problem, by inferring the initial atmospheric conditions to the TOI-270 multi-planet system. Using the emulator we find near-indistinguishable results when compared to original model. We then apply the emulator to the more complex inference problem, which aims to find the initial conditions for a sample of \textit{Kepler}, \textit{K2} and \textit{TESS} planets with well-constrained masses and radii. We demonstrate there is a relationship between core masses and the atmospheric mass that they retain after disc dispersal, and this trend is consistent with the `boil-off' scenario, in which close-in planets undergo dramatic atmospheric escape during disc dispersal. Thus, it appears the exoplanet population is consistent with the idea that close-in exoplanets initially acquired large massive atmospheres, the majority of which is lost during disc dispersal; before the final population is sculpted by atmospheric loss over 100~Myr to Gyr timescales.
    GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks. (arXiv:2201.12741v6 [cs.LG] UPDATED)
    Graph neural networks (GNNs) have been increasingly deployed in various applications that involve learning on non-Euclidean data. However, recent studies show that GNNs are vulnerable to graph adversarial attacks. Although there are several defense methods to improve GNN robustness by eliminating adversarial components, they may also impair the underlying clean graph structure that contributes to GNN training. In addition, few of those defense models can scale to large graphs due to their high computational complexity and memory usage. In this paper, we propose GARNET, a scalable spectral method to boost the adversarial robustness of GNN models. GARNET first leverages weighted spectral embedding to construct a base graph, which is not only resistant to adversarial attacks but also contains critical (clean) graph structure for GNN training. Next, GARNET further refines the base graph by pruning additional uncritical edges based on probabilistic graphical model. GARNET has been evaluated on various datasets, including a large graph with millions of nodes. Our extensive experiment results show that GARNET achieves adversarial accuracy improvement and runtime speedup over state-of-the-art GNN (defense) models by up to 13.27% and 14.7x, respectively.
    Verifying Learning-Based Robotic Navigation Systems. (arXiv:2205.13536v2 [cs.RO] UPDATED)
    Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for tasks where complex policies are learned within reactive systems. Unfortunately, these policies are known to be susceptible to bugs. Despite significant progress in DNN verification, there has been little work demonstrating the use of modern verification tools on real-world, DRL-controlled systems. In this case study, we attempt to begin bridging this gap, and focus on the important task of mapless robotic navigation -- a classic robotics problem, in which a robot, usually controlled by a DRL agent, needs to efficiently and safely navigate through an unknown arena towards a target. We demonstrate how modern verification engines can be used for effective model selection, i.e., selecting the best available policy for the robot in question from a pool of candidate policies. Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior, such as collisions and infinite loops. We also apply verification to identify models with overly conservative behavior, thus allowing users to choose superior policies, which might be better at finding shorter paths to a target. To validate our work, we conducted extensive experiments on an actual robot, and confirmed that the suboptimal policies detected by our method were indeed flawed. We also demonstrate the superiority of our verification-driven approach over state-of-the-art, gradient attacks. Our work is the first to establish the usefulness of DNN verification in identifying and filtering out suboptimal DRL policies in real-world robots, and we believe that the methods presented here are applicable to a wide range of systems that incorporate deep-learning-based agents.
    OpenCon: Open-world Contrastive Learning. (arXiv:2208.02764v2 [cs.LG] UPDATED)
    Machine learning models deployed in the wild naturally encounter unlabeled samples from both known and novel classes. Challenges arise in learning from both the labeled and unlabeled data, in an open-world semi-supervised manner. In this paper, we introduce a new learning framework, open-world contrastive learning (OpenCon). OpenCon tackles the challenges of learning compact representations for both known and novel classes and facilitates novelty discovery along the way. We demonstrate the effectiveness of OpenCon on challenging benchmark datasets and establish competitive performance. On the ImageNet dataset, OpenCon significantly outperforms the current best method by 11.9% and 7.4% on novel and overall classification accuracy, respectively. Theoretically, OpenCon can be rigorously interpreted from an EM algorithm perspective--minimizing our contrastive loss partially maximizes the likelihood by clustering similar samples in the embedding space. The code is available at https://github.com/deeplearning-wisc/opencon.
    Unraveling the graph structure of tabular data through Bayesian and spectral analysis. (arXiv:2110.01421v2 [cs.LG] UPDATED)
    In the big-data age, tabular data are being generated and analyzed everywhere. As a consequence, finding and understanding the relationships between the features in these data are of great relevance. Here, to encompass these relationships, we propose a graph-based method that allows individual, group and multi-scale analyses. The method starts by mapping the tabular data into a weighted directed graph using the Shapley additive explanations technique. With this graph of relationships, we show that the inference of the hierarchical modular structure obtained by the Nested Stochastic Block Model (nSBM) as well as the study of the spectral space of the magnetic Laplacian can help us identify the classes of features and unravel non-trivial relationships. As a case study, we analyzed a socioeconomic survey conducted with students in Brazil: the PeNSE survey. The spectral embedding of the columns suggested that questions related to physical activities form a separate group. The application of the nSBM approach not only corroborated with that but allowed complementary findings about the modular structure: some groups of questions showed a high adherence with the divisions qualitatively defined by the designers of the survey. As opposed to the structure obtained by the spectrum, questions from the class Safety were partly grouped by our method in the class Drugs. Surprisingly, by inspecting these questions, we observed that they were related to both these topics, suggesting an alternative interpretation of these questions. These results show how our method can provide guidance for tabular data analysis as well as the design of future surveys.
    Investigations on convergence behaviour of Physics Informed Neural Networks across spectral ranges and derivative orders. (arXiv:2301.02790v1 [cs.LG])
    An important inference from Neural Tangent Kernel (NTK) theory is the existence of spectral bias (SB), that is, low frequency components of the target function of a fully connected Artificial Neural Network (ANN) being learnt significantly faster than the higher frequencies during training. This is established for Mean Square Error (MSE) loss functions with very low learning rate parameters. Physics Informed Neural Networks (PINNs) are designed to learn the solutions of differential equations (DE) of arbitrary orders; in PINNs the loss functions are obtained as the residues of the conservative form of the DEs and represent the degree of dissatisfaction of the equations. So there has been an open question whether (a) PINNs also exhibit SB and (b) if so, how does this bias vary across the orders of the DEs. In this work, a series of numerical experiments are conducted on simple sinusoidal functions of varying frequencies, compositions and equation orders to investigate these issues. It is firmly established that under normalized conditions, PINNs do exhibit strong spectral bias, and this increases with the order of the differential equation.
    Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling. (arXiv:2301.03580v1 [cs.CV])
    We identify and overcome two key obstacles in extending the success of BERT-style pre-training, or the masked image modeling, to convolutional networks (convnets): (i) convolution operation cannot handle irregular, random-masked input images; (ii) the single-scale nature of BERT pre-training is inconsistent with convnet's hierarchical structure. For (i), we treat unmasked pixels as sparse voxels of 3D point clouds and use sparse convolution to encode. This is the first use of sparse convolution for 2D masked modeling. For (ii), we develop a hierarchical decoder to reconstruct images from multi-scale encoded features. Our method called Sparse masKed modeling (SparK) is general: it can be used directly on any convolutional model without backbone modifications. We validate it on both classical (ResNet) and modern (ConvNeXt) models: on three downstream tasks, it surpasses both state-of-the-art contrastive learning and transformer-based masked modeling by similarly large margins (around +1.0%). Improvements on object detection and instance segmentation are more substantial (up to +3.5%), verifying the strong transferability of features learned. We also find its favorable scaling behavior by observing more gains on larger models. All this evidence reveals a promising future of generative pre-training on convnets. Codes and models are released at https://github.com/keyu-tian/SparK.
    Exponential Family Model-Based Reinforcement Learning via Score Matching. (arXiv:2112.14195v2 [cs.LG] UPDATED)
    We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family distributions with $d$ parameters and the reward is bounded and known. SMRL uses score matching, an unnormalized density estimation technique that enables efficient estimation of the model parameter by ridge regression. Under standard regularity assumptions, SMRL achieves $\tilde O(d\sqrt{H^3T})$ online regret, where $H$ is the length of each episode and $T$ is the total number of interactions (ignoring polynomial dependence on structural scale parameters).
    Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference. (arXiv:2206.06047v2 [cs.IT] UPDATED)
    Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the "semantics" of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response to changing conditions in the environment. This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system that integrates spike-based sensing, processing, and communication. In the proposed NeuroComm system, each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio transmitter with multiple antennas. Transmission takes place over a shared fading channel to a receiver equipped with a multi-antenna impulse radio receiver and with an SNN. In order to enable adaptation of the receiver to the fading channel conditions, we introduce a hypernetwork to control the weights of the decoding SNN using pilots. Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations. The proposed system is shown to significantly improve over conventional frame-based digital solutions, as well as over alternative non-adaptive training methods, in terms of time-to-accuracy and energy consumption metrics.
    IAN: Iterated Adaptive Neighborhoods for manifold learning and dimensionality estimation. (arXiv:2208.09123v3 [cs.LG] UPDATED)
    Invoking the manifold assumption in machine learning requires knowledge of the manifold's geometry and dimension, and theory dictates how many samples are required. However, in applications data are limited, sampling may not be uniform, and manifold properties are unknown and (possibly) non-pure; this implies that neighborhoods must adapt to the local structure. We introduce an algorithm for inferring adaptive neighborhoods for data given by a similarity kernel. Starting with a locally-conservative neighborhood (Gabriel) graph, we sparsify it iteratively according to a weighted counterpart. In each step, a linear program yields minimal neighborhoods globally and a volumetric statistic reveals neighbor outliers likely to violate manifold geometry. We apply our adaptive neighborhoods to non-linear dimensionality reduction, geodesic computation and dimension estimation. A comparison against standard algorithms using, e.g., k-nearest neighbors, demonstrates their usefulness. Code for our algorithm will be available at https://github.com/dyballa/IAN
    Reservoir Prediction by Machine Learning Methods on The Well Data and Seismic Attributes for Complex Coastal Conditions. (arXiv:2301.03216v1 [physics.geo-ph])
    The aim of this work was to predict the probability of the spread of rock formations with hydrocarbon-collecting properties in the studied coastal area using a stack of machine learning algorithms and data augmentation and modification methods. This research develops the direction of machine learning where training is conducted on well data and spatial attributes. Methods for overcoming the limitations of this direction are shown, two methods - augmentation and modification of the well data sample: Spindle and Revers-Calibration. Considering the difficulties for seismic data interpretation in coastal area conditions, the proposed approach is a tool which is able to work with the whole totality of geological and geophysical data, extract the knowledge from 159-dimensional space spatial attributes and make facies spreading prediction with acceptable quality - F1 measure for reservoir class 0.798 on average for evaluation of "drilling" results of different geological conditions. It was shown that consistent application of the proposed augmentation methods in the implemented technology stack improves the quality of reservoir prediction by a factor of 1.56 relative to the original dataset.
    Topologically Regularized Data Embeddings. (arXiv:2301.03338v1 [cs.LG])
    Unsupervised representation learning methods are widely used for gaining insight into high-dimensional, unstructured, or structured data. In some cases, users may have prior topological knowledge about the data, such as a known cluster structure or the fact that the data is known to lie along a tree- or graph-structured topology. However, generic methods to ensure such structure is salient in the low-dimensional representations are lacking. This negatively impacts the interpretability of low-dimensional embeddings, and plausibly downstream learning tasks. To address this issue, we introduce topological regularization: a generic approach based on algebraic topology to incorporate topological prior knowledge into low-dimensional embeddings. We introduce a class of topological loss functions, and show that jointly optimizing an embedding loss with such a topological loss function as a regularizer yields embeddings that reflect not only local proximities but also the desired topological structure. We include a self-contained overview of the required foundational concepts in algebraic topology, and provide intuitive guidance on how to design topological loss functions for a variety of shapes, such as clusters, cycles, and bifurcations. We empirically evaluate the proposed approach on computational efficiency, robustness, and versatility in combination with linear and non-linear dimensionality reduction and graph embedding methods.
    UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Bandits. (arXiv:2301.03456v1 [eess.SP])
    Millimeter wave (mmWave) communications have a broad spectrum and can support data rates in the order of gigabits per second, as envisioned in 5G systems. However, they cannot be used for long distances due to their sensitivity to attenuation loss. To enable their use in the 5G network, it requires that the transmission energy be focused in sharp pencil beams. As any misalignment between the transmitter and receiver beam pair can reduce the data rate significantly, it is important that they are aligned as much as possible. To find the best transmit-receive beam pair, recent beam alignment (BA) techniques examine the entire beam space, which might result in a large amount of BA latency. Recent works propose to adaptively select the beams such that the cumulative reward measured in terms of received signal strength or throughput is maximized. In this paper, we develop an algorithm that exploits the unimodal structure of the received signal strengths of the beams to identify the best beam in a finite time using pure exploration strategies. Strategies that identify the best beam in a fixed time slot are more suitable for wireless network protocol design than cumulative reward maximization strategies that continuously perform exploration and exploitation. Our algorithm is named Unimodal Bandit for Best Beam (UB3) and identifies the best beam with a high probability in a few rounds. We prove that the error exponent in the probability does not depend on the number of beams and show that this is indeed the case by establishing a lower bound for the unimodal bandits. We demonstrate that UB3 outperforms the state-of-the-art algorithms through extensive simulations. Moreover, our algorithm is simple to implement and has lower computational complexity.
    A Domain-Theoretic Framework for Robustness Analysis of Neural Networks. (arXiv:2203.00295v3 [cs.LG] UPDATED)
    A domain-theoretic framework is presented for validated robustness analysis of neural networks. First, global robustness of a general class of networks is analyzed. Then, using the fact that Edalat's domain-theoretic L-derivative coincides with Clarke's generalized gradient, the framework is extended for attack-agnostic local robustness analysis. The proposed framework is ideal for designing algorithms which are correct by construction. This claim is exemplified by developing a validated algorithm for estimation of Lipschitz constant of feedforward regressors. The completeness of the algorithm is proved over differentiable networks, and also over general position ReLU networks. Computability results are obtained within the framework of effectively given domains. Using the proposed domain model, differentiable and non-differentiable networks can be analyzed uniformly. The validated algorithm is implemented using arbitrary-precision interval arithmetic, and the results of some experiments are presented. The software implementation is truly validated, as it handles floating-point errors as well.
    Convergence of Stochastic Approximation via Martingale and Converse Lyapunov Methods. (arXiv:2205.01303v3 [stat.ML] UPDATED)
    In this paper, we study the almost sure boundedness and the convergence of the stochastic approximation (SA) algorithm. At present, most available convergence proofs are based on the ODE method, and the almost sure boundedness of the iterations is an assumption and not a conclusion. In Borkar-Meyn (2000), it is shown that if the ODE has only one globally attractive equilibrium, then under additional assumptions, the iterations are bounded almost surely, and the SA algorithm converges to the desired solution. Our objective in the present paper is to provide an alternate proof of the above, based on martingale methods, which are simpler and less technical than those based on the ODE method. As a prelude, we prove a new sufficient condition for the global asymptotic stability of an ODE. Next we prove a "converse" Lyapunov theorem on the existence of a suitable Lyapunov function with a globally bounded Hessian, for a globally exponentially stable system. Both theorems are of independent interest to researchers in stability theory. Then, using these results, we provide sufficient conditions for the almost sure boundedness and the convergence of the SA algorithm. We show through examples that our theory covers some situations that are not covered by currently known results, specifically Borkar-Meyn (2000).
    Reinforcement Learning for Joint Optimization of Multiple Rewards. (arXiv:1909.02940v4 [cs.LG] UPDATED)
    Finding optimal policies which maximize long term rewards of Markov Decision Processes requires the use of dynamic programming and backward induction to solve the Bellman optimality equation. However, many real-world problems require optimization of an objective that is non-linear in cumulative rewards for which dynamic programming cannot be applied directly. For example, in a resource allocation problem, one of the objectives is to maximize long-term fairness among the users. We notice that when an agent aim to optimize some function of the sum of rewards is considered, the problem loses its Markov nature. This paper addresses and formalizes the problem of optimizing a non-linear function of the long term average of rewards. We propose model-based and model-free algorithms to learn the policy, where the model-based policy is shown to achieve a regret of $\Tilde{O}\left(LKDS\sqrt{\frac{A}{T}}\right)$ for $K$ objectives combined with a concave $L$-Lipschitz function. Further, using the fairness in cellular base-station scheduling, and queueing system scheduling as examples, the proposed algorithm is shown to significantly outperform the conventional RL approaches.
    Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design. (arXiv:2103.14272v2 [cs.LG] UPDATED)
    Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server's access to many clients' data and the edge servers' closeness to the clients to achieve a high communication efficiency. Neural network quantization can further reduce the communication overhead during model uploading. To fully exploit the advantages of hierarchical FL, an accurate convergence analysis with respect to the key system parameters is needed. Unfortunately, existing analysis is loose and does not consider model quantization. In this paper, we derive a tighter convergence bound for hierarchical FL with quantization. The convergence result leads to practical guidelines for important design problems such as the client-edge aggregation and edge-client association strategies. Based on the obtained analytical results, we optimize the two aggregation intervals and show that the client-edge aggregation interval should slowly decay while the edge-cloud aggregation interval needs to adapt to the ratio of the client-edge and edge-cloud propagation delay. Simulation results shall verify the design guidelines and demonstrate the effectiveness of the proposed aggregation strategy.
    Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation. (arXiv:2203.05400v3 [math.ST] UPDATED)
    It is common to model a deterministic response function, such as the output of a computer experiment, as a Gaussian process with a Mat\'ern covariance kernel. The smoothness parameter of a Mat\'ern kernel determines many important properties of the model in the large data limit, including the rate of convergence of the conditional mean to the response function. We prove that the maximum likelihood estimate of the smoothness parameter cannot asymptotically undersmooth the truth when the data are obtained on a fixed bounded subset of $\mathbb{R}^d$. That is, if the data-generating response function has Sobolev smoothness $\nu_0 + d/2$, then the smoothness parameter estimate cannot be asymptotically less than $\nu_0 + d/2$. The lower bound is sharp. Additionally, we show that maximum likelihood estimation finds the "correct" smoothness for a class of compactly supported self-similar functions. We also consider cross-validation and prove an asymptotic lower bound $\nu_0$, which however is unlikely to be sharp. The results are based on approximation theory in Sobolev spaces and some general theorems that restrict the set of values that the parameter estimators can take.
    Self-mentoring: a new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation. (arXiv:2205.10840v3 [cs.CV] UPDATED)
    Background: Accurate segmentation of microscopic structures such as bio-artificial capsules in microscopy imaging is a prerequisite to the computer-aided understanding of important biomechanical phenomenons. State-of-the-art segmentation performances are achieved by deep neural networks and related data-driven approaches. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Method: Recently, self-supervision, i.e. designing a neural pipeline providing synthetic or indirect supervision, has proved to significantly increase generalization performances of models trained on few shots. The objective of this paper is to introduce one such neural pipeline in the context of micro-capsule image segmentation. Our method leverages the rather simple content of these images so that a trainee network can be mentored by a referee network which has been previously trained on synthetically generated pairs of corrupted/correct region masks. Results: Challenging experimental setups are investigated. They involve from only 3 to 10 annotated images along with moderately large amounts of unannotated images. In a bio-artificial capsule dataset, our approach consistently and drastically improves accuracy. We also show that the learnt referee network is transferable to another Glioblastoma cell dataset and that it can be efficiently coupled with data augmentation strategies. Conclusions: Experimental results show that very significant accuracy increments are obtained by the proposed pipeline, leading to the conclusion that the self-supervision mechanism introduced in this paper has the potential to replace human annotations.
    Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction. (arXiv:2301.03573v1 [cs.LG])
    Despite impressive performance on a wide variety of tasks, deep neural networks require significant memory and computation costs, prohibiting their application in resource-constrained scenarios. Sparse training is one of the most common techniques to reduce these costs, however, the sparsity constraints add difficulty to the optimization, resulting in an increase in training time and instability. In this work, we aim to overcome this problem and achieve space-time co-efficiency. To accelerate and stabilize the convergence of sparse training, we analyze the gradient changes and develop an adaptive gradient correction method. Specifically, we approximate the correlation between the current and previous gradients, which is used to balance the two gradients to obtain a corrected gradient. Our method can be used with most popular sparse training pipelines under both standard and adversarial setups. Theoretically, we prove that our method can accelerate the convergence rate of sparse training. Extensive experiments on multiple datasets, model architectures, and sparsities demonstrate that our method outperforms leading sparse training methods by up to \textbf{5.0\%} in accuracy given the same number of training epochs, and reduces the number of training epochs by up to \textbf{52.1\%} to achieve the same accuracy.
    A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts. (arXiv:2105.07190v4 [cs.LG] UPDATED)
    In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intelligence (XAI). With the amount of XAI methods vastly growing, a taxonomy of methods is needed by researchers as well as practitioners: To grasp the breadth of the topic, compare methods, and to select the right XAI method based on traits required by a specific use-case context. Many taxonomies for XAI methods of varying level of detail and depth can be found in the literature. While they often have a different focus, they also exhibit many points of overlap. This paper unifies these efforts and provides a complete taxonomy of XAI methods with respect to notions present in the current state of research. In a structured literature analysis and meta-study, we identified and reviewed more than 50 of the most cited and current surveys on XAI methods, metrics, and method traits. After summarizing them in a survey of surveys, we merge terminologies and concepts of the articles into a unified structured taxonomy. Single concepts therein are illustrated by more than 50 diverse selected example methods in total, which we categorize accordingly. The taxonomy may serve both beginners, researchers, and practitioners as a reference and wide-ranging overview of XAI method traits and aspects. Hence, it provides foundations for targeted, use-case-oriented, and context-sensitive future research.
    Robust Feature-Level Adversaries are Interpretability Tools. (arXiv:2110.03605v6 [cs.LG] UPDATED)
    The literature on adversarial attacks in computer vision typically focuses on pixel-level perturbations. These tend to be very difficult to interpret. Recent work that manipulates the latent representations of image generators to create "feature-level" adversarial perturbations gives us an opportunity to explore perceptible, interpretable adversarial attacks. We make three contributions. First, we observe that feature-level attacks provide useful classes of inputs for studying representations in models. Second, we show that these adversaries are uniquely versatile and highly robust. We demonstrate that they can be used to produce targeted, universal, disguised, physically-realizable, and black-box attacks at the ImageNet scale. Third, we show how these adversarial images can be used as a practical interpretability tool for identifying bugs in networks. We use these adversaries to make predictions about spurious associations between features and classes which we then test by designing "copy/paste" attacks in which one natural image is pasted into another to cause a targeted misclassification. Our results suggest that feature-level attacks are a promising approach for rigorous interpretability research. They support the design of tools to better understand what a model has learned and diagnose brittle feature associations. Code is available at https://github.com/thestephencasper/feature_level_adv
    SpeeChain: A Speech Toolkit for Large-Scale Machine Speech Chain. (arXiv:2301.02966v1 [cs.CL])
    This paper introduces SpeeChain, an open-source Pytorch-based toolkit designed to develop the machine speech chain for large-scale use. This first release focuses on the TTS-to-ASR chain, a core component of the machine speech chain, that refers to the TTS data augmentation by unspoken text for ASR. To build an efficient pipeline for the large-scale TTS-to-ASR chain, we implement easy-to-use multi-GPU batch-level model inference, multi-dataloader batch generation, and on-the-fly data selection techniques. In this paper, we first explain the overall procedure of the TTS-to-ASR chain and the difficulties of each step. Then, we present a detailed ablation study on different types of unlabeled data, data filtering thresholds, batch composition, and real-synthetic data ratios. Our experimental results on train_clean_460 of LibriSpeech demonstrate that our TTS-to-ASR chain can significantly improve WER in a semi-supervised setting.
    Nuclear Segmentation and Classification: On Color & Compression Generalization. (arXiv:2301.03418v1 [eess.IV])
    Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data. Existing work to address this in both pathology and natural images has focused almost exclusively on classification tasks. We explore and evaluate the robustness of the 7 best performing nuclear segmentation and classification models from the largest computational pathology challenge for this problem to date, the CoNIC challenge. We demonstrate that existing state-of-the-art (SoTA) models are robust towards compression artifacts but suffer substantial performance reduction when subjected to shifts in the color domain. We find that using stain normalization to address the domain shift problem can be detrimental to the model performance. On the other hand, neural style transfer is more consistent in improving test performance when presented with large color variations in the wild.
    Computationally Efficient Approximations for Matrix-based Renyi's Entropy. (arXiv:2112.13720v4 [stat.ML] UPDATED)
    The recently developed matrix based Renyi's entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes the new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix $G$ to power $\alpha$(i.e., $tr(G^\alpha)$), with a normal complexity of nearly $O(n^3)$, which severely hampers its practical usage when the number of samples (i.e., $n$) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than $O(n^2)$. To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to $tr(G^\alpha)$ for arbitrary values of $\alpha$ by converting it into matrix-vector multiplications problem. We also establish the connection between the matrix-based Renyi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of $G$ to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties of different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy.
    Diffusion models as plug-and-play priors. (arXiv:2206.09012v3 [cs.LG] UPDATED)
    We consider the problem of inferring high-dimensional data $\mathbf{x}$ in a model that consists of a prior $p(\mathbf{x})$ and an auxiliary differentiable constraint $c(\mathbf{x},\mathbf{y})$ on $x$ given some additional information $\mathbf{y}$. In this paper, the prior is an independently trained denoising diffusion generative model. The auxiliary constraint is expected to have a differentiable form, but can come from diverse sources. The possibility of such inference turns diffusion models into plug-and-play modules, thereby allowing a range of potential applications in adapting models to new domains and tasks, such as conditional generation or image segmentation. The structure of diffusion models allows us to perform approximate inference by iterating differentiation through the fixed denoising network enriched with different amounts of noise at each step. Considering many noised versions of $\mathbf{x}$ in evaluation of its fitness is a novel search mechanism that may lead to new algorithms for solving combinatorial optimization problems.
    Automatic Differentiation of Programs with Discrete Randomness. (arXiv:2210.08572v3 [cs.LG] UPDATED)
    Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing and deep learning due to the improved performance afforded by gradient-based optimization. However, AD systems have been restricted to the subset of programs that have a continuous dependence on parameters. Programs that have discrete stochastic behaviors governed by distribution parameters, such as flipping a coin with probability $p$ of being heads, pose a challenge to these systems because the connection between the result (heads vs tails) and the parameters ($p$) is fundamentally discrete. In this paper we develop a new reparameterization-based methodology that allows for generating programs whose expectation is the derivative of the expectation of the original program. We showcase how this method gives an unbiased and low-variance estimator which is as automated as traditional AD mechanisms. We demonstrate unbiased forward-mode AD of discrete-time Markov chains, agent-based models such as Conway's Game of Life, and unbiased reverse-mode AD of a particle filter. Our code package is available at https://github.com/gaurav-arya/StochasticAD.jl.
    ExcelFormer: A Neural Network Surpassing GBDTs on Tabular Data. (arXiv:2301.02819v1 [cs.LG])
    Though neural networks have achieved enormous breakthroughs on various fields (e.g., computer vision) in supervised learning, they still trailed the performances of GBDTs on tabular data thus far. Delving into this issue, we identify that a proper handling of feature interactions and feature embedding is crucial to the success of neural networks on tabular data. We develop a novel neural network called ExcelFormer, which alternates in turn two attention modules that respectively manipulate careful feature interactions and feature embedding updates. A bespoke training methodology is jointly introduced to facilitate the model performances. By initializing parameters with minuscule values, these attention modules are attenuated when the training begins, and the effects of feature interactions and embedding updates progressively grow up to optimum levels under the guidance of the proposed specific regularization approaches Swap-Mix and Hidden-Mix as the training proceeds. Experiments on 25 public tabular datasets show that our ExcelFormer is superior to extremely-tuned GBDTs, which is an unprecedented achievement of neural networks in supervised tabular learning.
    Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior. (arXiv:2301.02952v1 [cs.LG])
    Many real-world reinforcement learning (RL) problems necessitate learning complex, temporally extended behavior that may only receive reward signal when the behavior is completed. If the reward-worthy behavior is known, it can be specified in terms of a non-Markovian reward function - a function that depends on aspects of the state-action history, rather than just the current state and action. Such reward functions yield sparse rewards, necessitating an inordinate number of experiences to find a policy that captures the reward-worthy pattern of behavior. Recent work has leveraged Knowledge Representation (KR) to provide a symbolic abstraction of aspects of the state that summarize reward-relevant properties of the state-action history and support learning a Markovian decomposition of the problem in terms of an automaton over the KR. Providing such a decomposition has been shown to vastly improve learning rates, especially when coupled with algorithms that exploit automaton structure. Nevertheless, such techniques rely on a priori knowledge of the KR. In this work, we explore how to automatically discover useful state abstractions that support learning automata over the state-action history. The result is an end-to-end algorithm that can learn optimal policies with significantly fewer environment samples than state-of-the-art RL on simple non-Markovian domains.
    An open unified deep graph learning framework for discovering drug leads. (arXiv:2301.03424v1 [q-bio.BM])
    Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and molecule optimization. Current efforts are disparate, involving the establishment of models for each stage, followed by multi-stage multi-model integration. However, this is non-ideal, as clumsy integration of incompatible models increases research overheads, and may even reduce success rates in drug discovery. Facilitating compatibilities requires establishing inherent model consistencies across lead discovery stages. Towards that effect, we propose an open deep graph learning (DGL) based pipeline: generative adversarial feature subspace enhancement (GAFSE), which first unifies the modeling of these stages into one learning framework. GAFSE also offers standardized modular design and streamlined interfaces for future expansions and community support. GAFSE combines adversarial/generative learning, graph attention network, graph reconstruction network, and optimizes the classification/regression loss, adversarial/generative loss, and reconstruction loss simultaneously. Convergence analysis theoretically guarantees model generalization performance. Exhaustive benchmarking demonstrates that the GAFSE pipeline achieves excellent performance across almost all lead discovery stages, while also providing valuable model interpretability. Hence, we believe this tool will enhance the efficiency and productivity of drug discovery researchers.
    Generalized Kernel Regularized Least Squares. (arXiv:2209.14355v2 [stat.ML] UPDATED)
    Kernel Regularized Least Squares (KRLS) is a popular method for flexibly estimating models that may have complex relationships between variables. However, its usefulness to many researchers is limited for two reasons. First, existing approaches are inflexible and do not allow KRLS to be combined with theoretically-motivated extensions such as random effects, unregularized fixed effects, or non-Gaussian outcomes. Second, estimation is extremely computationally intensive for even modestly sized datasets. Our paper addresses both concerns by introducing generalized KRLS (gKRLS). We note that KRLS can be re-formulated as a hierarchical model thereby allowing easy inference and modular model construction where KRLS can be used alongside random effects, splines, and unregularized fixed effects. Computationally, we also implement random sketching to dramatically accelerate estimation while incurring a limited penalty in estimation quality. We demonstrate that gKRLS can be fit on datasets with tens of thousands of observations in under one minute. Further, state-of-the-art techniques that require fitting the model over a dozen times (e.g. meta-learners) can be estimated quickly.
    Wasserstein Iterative Networks for Barycenter Estimation. (arXiv:2201.12245v2 [cs.LG] UPDATED)
    Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way. In this paper, we present an algorithm to approximate the Wasserstein-2 barycenters of continuous measures via a generative model. Previous approaches rely on regularization (entropic/quadratic) which introduces bias or on input convex neural networks which are not expressive enough for large-scale tasks. In contrast, our algorithm does not introduce bias and allows using arbitrary neural networks. In addition, based on the celebrity faces dataset, we construct Ave, celeba! dataset which can be used for quantitative evaluation of barycenter algorithms by using standard metrics of generative models such as FID.
    A Classification of $G$-invariant Shallow Neural Networks. (arXiv:2205.09219v5 [cs.LG] UPDATED)
    When trying to fit a deep neural network (DNN) to a $G$-invariant target function with $G$ a group, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do this, thus raising the problem of ``$G$-invariant neural architecture design'': What is the optimal $G$-invariant architecture for a given problem? Before we can consider the optimization problem itself, we must understand the search space, the architectures in it, and how they relate to one another. In this paper, we take a first step towards this goal; we prove a theorem that gives a classification of all $G$-invariant single-hidden-layer or ``shallow'' neural network ($G$-SNN) architectures with ReLU activation for any finite orthogonal group $G$, and we prove a second theorem that characterizes the inclusion maps or ``network morphisms'' between the architectures that can be leveraged during neural architecture search (NAS). The proof is based on a correspondence of every $G$-SNN to a signed permutation representation of $G$ acting on the hidden neurons; the classification is equivalently given in terms of the first cohomology classes of $G$, thus admitting a topological interpretation. The $G$-SNN architectures corresponding to nontrivial cohomology classes have, to our knowledge, never been explicitly identified in the literature previously. Using a code implementation, we enumerate the $G$-SNN architectures for some example groups $G$ and visualize their structure. Finally, we prove that architectures corresponding to inequivalent cohomology classes coincide in function space only when their weight matrices are zero, and we discuss the implications of this for NAS.
    Deep Injective Prior for Inverse Scattering. (arXiv:2301.03092v1 [cs.LG])
    In electromagnetic inverse scattering, we aim to reconstruct object permittivity from scattered waves. Deep learning is a promising alternative to traditional iterative solvers, but it has been used mostly in a supervised framework to regress the permittivity patterns from scattered fields or back-projections. While such methods are fast at test-time and achieve good results for specific data distributions, they are sensitive to the distribution drift of the scattered fields, common in practice. If the distribution of the scattered fields changes due to changes in frequency, the number of transmitters and receivers, or any other real-world factor, an end-to-end neural network must be re-trained or fine-tuned on a new dataset. In this paper, we propose a new data-driven framework for inverse scattering based on deep generative models. We model the target permittivities by a low-dimensional manifold which acts as a regularizer and learned from data. Unlike supervised methods which require both scattered fields and target signals, we only need the target permittivities for training; it can then be used with any experimental setup. We show that the proposed framework significantly outperforms the traditional iterative methods especially for strong scatterers while having comparable reconstruction quality to state-of-the-art deep learning methods like U-Net.
    Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers. (arXiv:2201.10981v2 [eess.IV] UPDATED)
    Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. This work presents a hybrid network called SWTR-Unet, consisting of a pretrained ResNet, transformer blocks as well as a common Unet-style decoder path. This network was primarily applied to single-modality non-contrast-enhanced liver MRI and additionally to the publicly available computed tomography (CT) data of the liver tumor segmentation (LiTS) challenge to verify the applicability on other modalities. For a broader evaluation, multiple state-of-the-art networks were implemented and applied, ensuring a direct comparability. Furthermore, correlation analysis and an ablation study were carried out, to investigate various influencing factors on the segmentation accuracy of the presented method. With Dice scores of averaged 98+-2% for liver and 81+-28% lesion segmentation on the MRI dataset and 97+-2% and 79+-25%, respectively on the CT dataset, the proposed SWTR-Unet proved to be a precise approach for liver and hepatic lesion segmentation with state-of-the-art results for MRI and competing accuracy in CT imaging. The achieved segmentation accuracy was found to be on par with manually performed expert segmentations as indicated by inter-observer variabilities for liver lesion segmentation. In conclusion, the presented method could save valuable time and resources in clinical practice.
    Deepfake CAPTCHA: A Method for Preventing Fake Calls. (arXiv:2301.03064v1 [cs.CR])
    Deep learning technology has made it possible to generate realistic content of specific individuals. These `deepfakes' can now be generated in real-time which enables attackers to impersonate people over audio and video calls. Moreover, some methods only need a few images or seconds of audio to steal an identity. Existing defenses perform passive analysis to detect fake content. However, with the rapid progress of deepfake quality, this may be a losing game. In this paper, we propose D-CAPTCHA: an active defense against real-time deepfakes. The approach is to force the adversary into the spotlight by challenging the deepfake model to generate content which exceeds its capabilities. By doing so, passive detection becomes easier since the content will be distorted. In contrast to existing CAPTCHAs, we challenge the AI's ability to create content as opposed to its ability to classify content. In this work we focus on real-time audio deepfakes and present preliminary results on video. In our evaluation we found that D-CAPTCHA outperforms state-of-the-art audio deepfake detectors with an accuracy of 91-100% depending on the challenge (compared to 71% without challenges). We also performed a study on 41 volunteers to understand how threatening current real-time deepfake attacks are. We found that the majority of the volunteers could not tell the difference between real and fake audio.
    Upward lightning at wind turbines: Risk assessment from larger-scale meteorology. (arXiv:2301.03360v1 [stat.ML])
    Upward lightning (UL) has become an increasingly important threat to wind turbines as ever more of them are being installed for renewably producing electricity. The taller the wind turbine the higher the risk that the type of lightning striking the man-made structure is UL. UL can be much more destructive than downward lightning due to its long lasting initial continuous current leading to a large charge transfer within the lightning discharge process. Current standards for the risk assessment of lightning at wind turbines mainly take the summer lightning activity into account, which is inferred from LLS. Ground truth lightning current measurements reveal that less than 50% of UL might be detected by lightning location systems (LLS). This leads to a large underestimation of the proportion of LLS-non-detectable UL at wind turbines, which is the dominant lightning type in the cold season. This study aims to assess the risk of LLS-detectable and LLS-non-detectable UL at wind turbines using direct UL measurements at the Gaisberg Tower (Austria) and S\"antis Tower (Switzerland). Direct UL observations are linked to meteorological reanalysis data and joined by random forests, a powerful machine learning technique. The meteorological drivers for the non-/occurrence of LLS-detectable and LLS-non-detectable UL, respectively, are found from the random forest models trained at the towers and have large predictive skill on independent data. In a second step the results from the tower-trained models are extended to a larger study domain (Central and Northern Germany). The tower-trained models for LLS-detectable lightning is independently verified at wind turbine locations in that domain and found to reliably diagnose that type of UL. Risk maps based on case study events show that high diagnosed probabilities in the study domain coincide with actual UL events.
    $\mathcal{Y}$-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning. (arXiv:2202.09817v2 [cs.CL] UPDATED)
    With the success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of parameters. Although some parameter-efficient tuning paradigms have been proposed to address this problem, they still require large resources to compute the gradients in the training phase. In this paper, we propose $\mathcal{Y}$-Tuning, an efficient yet effective paradigm to adapt frozen large-scale PTMs to specific downstream tasks. $\mathcal{Y}$-tuning learns dense representations for labels $\mathcal{Y}$ defined in a given task and aligns them to fixed feature representation. Without tuning the features of input text and model parameters, $\mathcal{Y}$-tuning is both parameter-efficient and training-efficient. For $\text{DeBERTa}_\text{XXL}$ with 1.6 billion parameters, $\mathcal{Y}$-tuning achieves performance more than $96\%$ of full fine-tuning on GLUE Benchmark with only $2\%$ tunable parameters and much fewer training costs.
    Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans. (arXiv:2209.13020v12 [cs.CY] UPDATED)
    We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda embedding legal knowledge and reasoning in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.
    So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems. (arXiv:2205.14276v3 [cs.LG] UPDATED)
    The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difficulty of modeling them efficiently. This work proposes a modified attention mechanism adapted to the underlying physics, which allows to recover the relevant non-local effects. Namely, we introduce spherical harmonic coordinates (SPHCs) to reflect higher-order geometric information for each atom in a molecule, enabling a non-local formulation of attention in the SPHC space. Our proposed model So3krates - a self-attention based message passing neural network - uncouples geometric information from atomic features, making them independently amenable to attention mechanisms. Thereby we construct spherical filters, which extend the concept of continuous filters in Euclidean space to SPHC space and serve as foundation for a spherical self-attention mechanism. We show that in contrast to other published methods, So3krates is able to describe non-local quantum mechanical effects over arbitrary length scales. Further, we find evidence that the inclusion of higher-order geometric correlations increases data efficiency and improves generalization. So3krates matches or exceeds state-of-the-art performance on popular benchmarks, notably, requiring a significantly lower number of parameters (0.25 - 0.4x) while at the same time giving a substantial speedup (6 - 14x for training and 2 - 11x for inference) compared to other models.
    Contrastive Trajectory Similarity Learning with Dual-Feature Attention. (arXiv:2210.05155v2 [cs.DB] UPDATED)
    Trajectory similarity measures act as query predicates in trajectory databases, making them the key player in determining the query results. They also have a heavy impact on the query efficiency. An ideal measure should have the capability to accurately evaluate the similarity between any two trajectories in a very short amount of time. Towards this aim, we propose a contrastive learning-based trajectory modeling method named TrajCL. We present four trajectory augmentation methods and a novel dual-feature self-attention-based trajectory backbone encoder. The resultant model can jointly learn both the spatial and the structural patterns of trajectories. Our model does not involve any recurrent structures and thus has a high efficiency. Besides, our pre-trained backbone encoder can be fine-tuned towards other computationally expensive measures with minimal supervision data. Experimental results show that TrajCL is consistently and significantly more accurate than the state-of-the-art trajectory similarity measures. After fine-tuning, i.e., to serve as an estimator for heuristic measures, TrajCL can even outperform the state-of-the-art supervised method by up to 56% in the accuracy for processing trajectory similarity queries.
    Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation. (arXiv:2301.03125v1 [stat.ML])
    The stochastic proximal point (SPP) methods have gained recent attention for stochastic optimization, with strong convergence guarantees and superior robustness to the classic stochastic gradient descent (SGD) methods showcased at little to no cost of computational overhead added. In this article, we study a minibatch variant of SPP, namely M-SPP, for solving convex composite risk minimization problems. The core contribution is a set of novel excess risk bounds of M-SPP derived through the lens of algorithmic stability theory. Particularly under smoothness and quadratic growth conditions, we show that M-SPP with minibatch-size $n$ and iteration count $T$ enjoys an in-expectation fast rate of convergence consisting of an $\mathcal{O}\left(\frac{1}{T^2}\right)$ bias decaying term and an $\mathcal{O}\left(\frac{1}{nT}\right)$ variance decaying term. In the small-$n$-large-$T$ setting, this result substantially improves the best known results of SPP-type approaches by revealing the impact of noise level of model on convergence rate. In the complementary small-$T$-large-$n$ regime, we provide a two-phase extension of M-SPP to achieve comparable convergence rates. Moreover, we derive a near-tight high probability (over the randomness of data) bound on the parameter estimation error of a sampling-without-replacement variant of M-SPP. Numerical evidences are provided to support our theoretical predictions when substantialized to Lasso and logistic regression models.
    AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices. (arXiv:2301.03062v1 [cs.LG])
    In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.
    Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons. (arXiv:2301.03118v1 [cs.CR])
    In this paper we describe how to plant novel types of backdoors in any facial recognition model based on the popular architecture of deep Siamese neural networks, by mathematically changing a small fraction of its weights (i.e., without using any additional training or optimization). These backdoors force the system to err only on specific persons which are preselected by the attacker. For example, we show how such a backdoored system can take any two images of a particular person and decide that they represent different persons (an anonymity attack), or take any two images of a particular pair of persons and decide that they represent the same person (a confusion attack), with almost no effect on the correctness of its decisions for other persons. Uniquely, we show that multiple backdoors can be independently installed by multiple attackers who may not be aware of each other's existence with almost no interference. We have experimentally verified the attacks on a FaceNet-based facial recognition system, which achieves SOTA accuracy on the standard LFW dataset of $99.35\%$. When we tried to individually anonymize ten celebrities, the network failed to recognize two of their images as being the same person in $96.97\%$ to $98.29\%$ of the time. When we tried to confuse between the extremely different looking Morgan Freeman and Scarlett Johansson, for example, their images were declared to be the same person in $91.51 \%$ of the time. For each type of backdoor, we sequentially installed multiple backdoors with minimal effect on the performance of each one (for example, anonymizing all ten celebrities on the same model reduced the success rate for each celebrity by no more than $0.91\%$). In all of our experiments, the benign accuracy of the network on other persons was degraded by no more than $0.48\%$ (and in most cases, it remained above $99.30\%$).
    Generalized adaptive smoothing based neural network architecture for traffic state estimation. (arXiv:2301.03439v1 [eess.SY])
    The adaptive smoothing method (ASM) is a standard data-driven technique used in traffic state estimation. The ASM has free parameters which, in practice, are chosen to be some generally acceptable values based on intuition. However, we note that the heuristically chosen values often result in un-physical predictions by the ASM. In this work, we propose a neural network based on the ASM which tunes those parameters automatically by learning from sparse data from road sensors. We refer to it as the adaptive smoothing neural network (ASNN). We also propose a modified ASNN (MASNN), which makes it a strong learner by using ensemble averaging. The ASNN and MASNN are trained and tested two real-world datasets. Our experiments reveal that the ASNN and the MASNN outperform the conventional ASM.
    L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging. (arXiv:2301.03441v1 [eess.SP])
    Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We then introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, incorporating this method to take into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on a set of four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single-EEG channel input. Our analyses also show that L-SeqSleepNet is able to remedy the effect of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages and that the network largely reduces exceptionally high errors seen in many subjects. Finally, the computation time only grows at a sub-linear rate when the sequence length increases.
    Fair Clustering Under a Bounded Cost. (arXiv:2106.07239v2 [cs.LG] UPDATED)
    Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space. A recent variant, fair clustering, associates a color with each point representing its group membership and requires that each color has (approximately) equal representation in each cluster to satisfy group fairness. In this model, the cost of the clustering objective increases due to enforcing fairness in the algorithm. The relative increase in the cost, the ''price of fairness,'' can indeed be unbounded. Therefore, in this paper we propose to treat an upper bound on the clustering objective as a constraint on the clustering problem, and to maximize equality of representation subject to it. We consider two fairness objectives: the group utilitarian objective and the group egalitarian objective, as well as the group leximin objective which generalizes the group egalitarian objective. We derive fundamental lower bounds on the approximation of the utilitarian and egalitarian objectives and introduce algorithms with provable guarantees for them. For the leximin objective we introduce an effective heuristic algorithm. We further derive impossibility results for other natural fairness objectives. We conclude with experimental results on real-world datasets that demonstrate the validity of our algorithms.
    Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. (arXiv:2301.03566v1 [math.ST])
    We study simple binary hypothesis testing under both local differential privacy (LDP) and communication constraints. We qualify our results as either minimax optimal or instance optimal: the former hold for the set of distribution pairs with prescribed Hellinger divergence and total variation distance, whereas the latter hold for specific distribution pairs. For the sample complexity of simple hypothesis testing under pure LDP constraints, we establish instance-optimal bounds for distributions with binary support; minimax-optimal bounds for general distributions; and (approximately) instance-optimal, computationally efficient algorithms for general distributions. When both privacy and communication constraints are present, we develop instance-optimal, computationally efficient algorithms that achieve the minimum possible sample complexity (up to universal constants). Our results on instance-optimal algorithms hinge on identifying the extreme points of the joint range set $\mathcal A$ of two distributions $p$ and $q$, defined as $\mathcal A := \{(\mathbf T p, \mathbf T q) | \mathbf T \in \mathcal C\}$, where $\mathcal C$ is the set of channels characterizing the constraints.
    Improved Training of Physics-Informed Neural Networks with Model Ensembles. (arXiv:2204.05108v2 [cs.LG] UPDATED)
    Learning the solution of partial differential equations (PDEs) with a neural network (known in the literature as a physics-informed neural network, PINN) is an attractive alternative to traditional solvers due to its elegancy, greater flexibility and the ease of incorporating observed data. However, training PINNs is notoriously difficult in practice. One problem is the existence of multiple simple (but wrong) solutions which are attractive for PINNs when the solution interval is too large. In this paper, we propose to expand the solution interval gradually to make the PINN converge to the correct solution. To find a good schedule for the solution interval expansion, we train an ensemble of PINNs. The idea is that all ensemble members converge to the same solution in the vicinity of observed data (e.g., initial conditions) while they may be pulled towards different wrong solutions farther away from the observations. Therefore, we use the ensemble agreement as the criterion for including new points for computing the loss derived from PDEs. We show experimentally that the proposed method can improve the accuracy of the found solution.
    Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions. (arXiv:2205.07266v4 [cs.LG] UPDATED)
    Graph neural networks (GNNs) mainly rely on the message-passing paradigm to propagate node features and build interactions, and different graph learning tasks require different ranges of node interactions. In this work, we explore the capacity of GNNs to capture interactions between nodes under contexts with different complexities. We discover that GNNs are usually unable to capture the most informative kinds of interaction styles for diverse graph learning tasks, and thus name this phenomenon as GNNs' representation bottleneck. As a response, we demonstrate that the inductive bias introduced by existing graph construction mechanisms can prevent GNNs from learning interactions of the most appropriate complexity, i.e., resulting in the representation bottleneck. To address that limitation, we propose a novel graph rewiring approach based on interaction patterns learned by GNNs to adjust the receptive fields of each node dynamically. Extensive experiments on both real-world and synthetic datasets prove the effectiveness of our algorithm to alleviate the representation bottleneck and its superiority to enhance the performance of GNNs over state-of-the-art graph rewiring baselines.
    Can Foundation Models Help Us Achieve Perfect Secrecy?. (arXiv:2205.13722v2 [cs.LG] UPDATED)
    A key promise of machine learning is the ability to assist users with personal tasks. Because the personal context required to make accurate predictions is often sensitive, we require systems that protect privacy. A gold standard privacy-preserving system will satisfy perfect secrecy, meaning that interactions with the system provably reveal no private information. However, privacy and quality appear to be in tension in existing systems for personal tasks. Neural models typically require copious amounts of training to perform well, while individual users typically hold a limited scale of data, so federated learning (FL) systems propose to learn from the aggregate data of multiple users. FL does not provide perfect secrecy, but rather practitioners apply statistical notions of privacy -- i.e., the probability of learning private information about a user should be reasonably low. The strength of the privacy guarantee is governed by privacy parameters. Numerous privacy attacks have been demonstrated on FL systems and it can be challenging to reason about the appropriate privacy parameters for a privacy-sensitive use case. Therefore our work proposes a simple baseline for FL, which both provides the stronger perfect secrecy guarantee and does not require setting any privacy parameters. We initiate the study of when and where an emerging tool in ML -- the in-context learning abilities of recent pretrained models -- can be an effective baseline alongside FL. We find in-context learning is competitive with strong FL baselines on 6 of 7 popular benchmarks from the privacy literature and a real-world case study, which is disjoint from the pretraining data. We release our code here: https://github.com/simran-arora/focus
    Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?. (arXiv:2206.07767v2 [cs.LG] UPDATED)
    Wasserstein Generative Adversarial Networks (WGANs) are the popular generative models built on the theory of Optimal Transport (OT) and the Kantorovich duality. Despite the success of WGANs, it is still unclear how well the underlying OT dual solvers approximate the OT cost (Wasserstein-1 distance, $\mathbb{W}_{1}$) and the OT gradient needed to update the generator. In this paper, we address these questions. We construct 1-Lipschitz functions and use them to build ray monotone transport plans. This strategy yields pairs of continuous benchmark distributions with the analytically known OT plan, OT cost and OT gradient in high-dimensional spaces such as spaces of images. We thoroughly evaluate popular WGAN dual form solvers (gradient penalty, spectral normalization, entropic regularization, etc.) using these benchmark pairs. Even though these solvers perform well in WGANs, none of them faithfully compute $\mathbb{W}_{1}$ in high dimensions. Nevertheless, many provide a meaningful approximation of the OT gradient. These observations suggest that these solvers should not be treated as good estimators of $\mathbb{W}_{1}$, but to some extent they indeed can be used in variational problems requiring the minimization of $\mathbb{W}_{1}$.
    Making Decisions under Outcome Performativity. (arXiv:2210.01745v2 [cs.LG] UPDATED)
    Decision-makers often act in response to data-driven predictions, with the goal of achieving favorable outcomes. In such settings, predictions don't passively forecast the future; instead, predictions actively shape the distribution of outcomes they are meant to predict. This performative prediction setting raises new challenges for learning "optimal" decision rules. In particular, existing solution concepts do not address the apparent tension between the goals of forecasting outcomes accurately and steering individuals to achieve desirable outcomes. To contend with this concern, we introduce a new optimality concept -- performative omniprediction -- adapted from the supervised (non-performative) learning setting. A performative omnipredictor is a single predictor that simultaneously encodes the optimal decision rule with respect to many possibly-competing objectives. Our main result demonstrates that efficient performative omnipredictors exist, under a natural restriction of performative prediction, which we call outcome performativity. On a technical level, our results follow by carefully generalizing the notion of outcome indistinguishability to the outcome performative setting. From an appropriate notion of Performative OI, we recover many consequences known to hold in the supervised setting, such as omniprediction and universal adaptability.
    Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction. (arXiv:2301.03027v1 [eess.IV])
    Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult. Recently, many deep learning approaches have been studied for motion artifact reduction. Unfortunately, most existing models are trained in a supervised manner, requiring paired motion-corrupted and motion-free images, or are based on a strict motion-corruption model, which limits their use for real-world situations. To address this issue, here we present an annealed score-based diffusion model for MRI motion artifact reduction. Specifically, we train a score-based model using only motion-free images, and then motion artifacts are removed by applying forward and reverse diffusion processes repeatedly to gradually impose a low-frequency data consistency. Experimental results verify that the proposed method successfully reduces both simulated and in vivo motion artifacts, outperforming the state-of-the-art deep learning methods.
    Machine-Learning Prediction of the Computed Band Gaps of Double Perovskite Materials. (arXiv:2301.03372v1 [cond-mat.mtrl-sci])
    Prediction of the electronic structure of functional materials is essential for the engineering of new devices. Conventional electronic structure prediction methods based on density functional theory (DFT) suffer from not only high computational cost, but also limited accuracy arising from the approximations of the exchange-correlation functional. Surrogate methods based on machine learning have garnered much attention as a viable alternative to bypass these limitations, especially in the prediction of solid-state band gaps, which motivated this research study. Herein, we construct a random forest regression model for band gaps of double perovskite materials, using a dataset of 1306 band gaps computed with the GLLBSC (Gritsenko, van Leeuwen, van Lenthe, and Baerends solid correlation) functional. Among the 20 physical features employed, we find that the bulk modulus, superconductivity temperature, and cation electronegativity exhibit the highest importance scores, consistent with the physics of the underlying electronic structure. Using the top 10 features, a model accuracy of 85.6% with a root mean square error of 0.64 eV is obtained, comparable to previous studies. Our results are significant in the sense that they attest to the potential of machine learning regressions for the rapid screening of promising candidate functional materials.
    Optimization-based Causal Estimation from Heterogenous Environments. (arXiv:2109.11990v2 [stat.ME] UPDATED)
    This paper presents a new optimization approach to causal estimation. Given data that contains covariates and an outcome, which covariates are causes of the outcome, and what is the strength of the causality? In classical machine learning (ML), the goal of optimization is to maximize predictive accuracy. However, some covariates might exhibit a non-causal association to the outcome. Such spurious associations provide predictive power for classical ML, but they prevent us from causally interpreting the result. This paper proposes CoCo, an optimization algorithm that bridges the gap between pure prediction and causal inference. CoCo leverages the recently-proposed idea of environments, datasets of covariates/response where the causal relationships remain invariant but where the distribution of the covariates changes from environment to environment. Given datasets from multiple environments -- and ones that exhibit sufficient heterogeneity -- CoCo maximizes an objective for which the only solution is the causal solution. We describe the theoretical foundations of this approach and demonstrate its effectiveness on simulated and real datasets. Compared to classical ML and existing methods, CoCo provides more accurate estimates of the causal model.
    Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions. (arXiv:2203.09436v4 [math.OC] UPDATED)
    We study stochastic monotone inclusion problems, which widely appear in machine learning applications, including robust regression and adversarial learning. We propose novel variants of stochastic Halpern iteration with recursive variance reduction. In the cocoercive -- and more generally Lipschitz-monotone -- setup, our algorithm attains $\epsilon$ norm of the operator with $\mathcal{O}(\frac{1}{\epsilon^3})$ stochastic operator evaluations, which significantly improves over state of the art $\mathcal{O}(\frac{1}{\epsilon^4})$ stochastic operator evaluations required for existing monotone inclusion solvers applied to the same problem classes. We further show how to couple one of the proposed variants of stochastic Halpern iteration with a scheduled restart scheme to solve stochastic monotone inclusion problems with ${\mathcal{O}}(\frac{\log(1/\epsilon)}{\epsilon^2})$ stochastic operator evaluations under additional sharpness or strong monotonicity assumptions.
    Differentiable Safe Controller Design through Control Barrier Functions. (arXiv:2209.10034v2 [eess.SY] UPDATED)
    Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees. To address this issue, control barrier functions (CBFs) have been applied as a safety filter to monitor and modify the outputs of learning-based controllers in order to guarantee the safety of the closed-loop system. However, such modification can be myopic with unpredictable long-term effects. In this work, we propose a safe-by-construction NN controller which employs differentiable CBF-based safety layers, and investigate the performance of safe-by-construction NN controllers in learning-based control. Specifically, two formulations of controllers are compared: one is projection-based and the other relies on our proposed set-theoretic parameterization. Both methods demonstrate improved closed-loop performance over using CBF as a separate safety filter in numerical experiments.
    Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain. (arXiv:2109.05507v3 [cs.CR] UPDATED)
    Deep Neural Networks (DNNs) are ubiquitous and span a variety of applications ranging from image classification to real-time object detection. As DNN models become more sophisticated, the computational cost of training these models becomes a burden. For this reason, outsourcing the training process has been the go-to option for many DNN users. Unfortunately, this comes at the cost of vulnerability to backdoor attacks. These attacks aim to establish hidden backdoors in the DNN so that it performs well on clean samples, but outputs a particular target label when a trigger is applied to the input. Existing backdoor attacks either generate triggers in the spatial domain or naively poison frequencies in the Fourier domain. In this work, we propose a pipeline based on Fourier heatmaps to generate a spatially dynamic and invisible backdoor attack in the frequency domain. The proposed attack is extensively evaluated on various datasets and network architectures. Unlike most existing backdoor attacks, the proposed attack can achieve high attack success rates with low poisoning rates and little to no drop in performance while remaining imperceptible to the human eye. Moreover, we show that the models poisoned by our attack are resistant to various state-of-the-art (SOTA) defenses, so we contribute two possible defenses that can evade the attack.
    Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization. (arXiv:2205.00626v2 [cs.SI] UPDATED)
    Networks are commonly used to model complex systems. The different entities in the system are represented by nodes of the network and their interactions by edges. In most real life systems, the different entities may interact in different ways necessitating the use of multiplex networks where multiple links are used to model the interactions. One of the major tools for inferring network topology is community detection. Although there are numerous works on community detection in single-layer networks, existing community detection methods for multiplex networks mostly learn a common community structure across layers and do not take the heterogeneity across layers into account. In this paper, we introduce a new multiplex community detection method that identifies communities that are common across layers as well as those that are unique to each layer. The proposed method, Multiplex Orthogonal Nonnegative Matrix Tri-Factorization, represents the adjacency matrix of each layer as the sum of two low-rank matrix factorizations corresponding to the common and private communities, respectively. Unlike most of the existing methods, which require the number of communities to be pre-determined, the proposed method also introduces a two stage method to determine the number of common and private communities. The proposed algorithm is evaluated on synthetic and real multiplex networks, as well as for multiview clustering applications, and compared to state-of-the-art techniques.
    Learning Program Representations with a Tree-Structured Transformer. (arXiv:2208.08643v2 [cs.SE] UPDATED)
    Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks. Various neural network models are proposed to learn from tree-structured program representations, e.g., abstract syntax tree (AST) and concrete syntax tree (CST). However, most neural architectures either fail to capture long-range dependencies which are ubiquitous in programs, or cannot learn effective representations for syntax tree nodes, making them incapable of performing the node-level prediction tasks, e.g., bug localization. In this paper, we propose Tree-Transformer, a novel recursive tree-structured neural network to learn the vector representations for source codes. We propose a multi-head attention mechanism to model the dependency between siblings and parent-children node pairs. Moreover, we propose a bi-directional propagation strategy to allow node information passing in two directions, bottom-up and top-down along trees. In this way, Tree-Transformer can learn the information of the node features as well as the global contextual information. The extensive experimental results show that our Tree-Transformer significantly outperforms the existing tree-based and graph-based program representation learning approaches in both the tree-level and node-level prediction tasks.
    VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum. (arXiv:2301.03251v1 [quant-ph])
    With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.
    Deep Insights of Deepfake Technology : A Review. (arXiv:2105.00192v2 [cs.LG] UPDATED)
    Under the aegis of computer vision and deep learning technology, a new emerging techniques has introduced that anyone can make highly realistic but fake videos, images even can manipulates the voices. This technology is widely known as Deepfake Technology. Although it seems interesting techniques to make fake videos or image of something or some individuals but it could spread as misinformation via internet. Deepfake contents could be dangerous for individuals as well as for our communities, organizations, countries religions etc. As Deepfake content creation involve a high level expertise with combination of several algorithms of deep learning, it seems almost real and genuine and difficult to differentiate. In this paper, a wide range of articles have been examined to understand Deepfake technology more extensively. We have examined several articles to find some insights such as what is Deepfake, who are responsible for this, is there any benefits of Deepfake and what are the challenges of this technology. We have also examined several creation and detection techniques. Our study revealed that although Deepfake is a threat to our societies, proper measures and strict regulations could prevent this.
    Locally-symplectic neural networks for learning volume-preserving dynamics. (arXiv:2109.09151v2 [math-ph] UPDATED)
    We propose locally-symplectic neural networks LocSympNets for learning the flow of phase volume-preserving dynamics. The construction of LocSympNets stems from the theorem of the local Hamiltonian description of the divergence-free vector field and the splitting methods based on symplectic integrators. Symplectic gradient modules of the recently proposed symplecticity-preserving neural networks SympNets are used to construct invertible locally-symplectic modules. To further preserve properties of the flow of a dynamical system LocSympNets are extended to symmetric locally-symplectic neural networks SymLocSympNets, such that the inverse of SymLocSympNets is equal to the feed-forward propagation of SymLocSympNets with the negative time step, which is a general property of the flow of a dynamical system. LocSympNets and SymLocSympNets are studied numerically considering learning linear and nonlinear volume-preserving dynamics. We demonstrate learning of linear traveling wave solutions to the semi-discretized advection equation, periodic trajectories of the Euler equations of the motion of a free rigid body, and quasi-periodic solutions of the charged particle motion in an electromagnetic field. LocSympNets and SymLocSympNets can learn linear and nonlinear dynamics to a high degree of accuracy even when random noise is added to the training data. When learning a single trajectory of the rigid body dynamics locally-symplectic neural networks can learn both quadratic invariants of the system with absolute relative errors below 1%. In addition, SymLocSympNets produce qualitatively good long-time predictions, when the learning of the whole system from randomly sampled data is considered. LocSympNets and SymLocSympNets can produce accurate short-time predictions of quasi-periodic solutions, which is illustrated in the example of the charged particle motion in an electromagnetic field.
    Neural parameter calibration for large-scale multi-agent models. (arXiv:2209.13565v2 [math.OC] UPDATED)
    Computational models have become a powerful tool in the quantitative sciences to understand the behaviour of complex systems that evolve in time. However, they often contain a potentially large number of free parameters whose values cannot be obtained from theory but need to be inferred from data. This is especially the case for models in the social sciences, economics, or computational epidemiology. Yet many current parameter estimation methods are mathematically involved and computationally slow to run. In this paper we present a computationally simple and fast method to retrieve accurate probability densities for model parameters using neural differential equations. We present a pipeline comprising multi-agent models acting as forward solvers for systems of ordinary or stochastic differential equations, and a neural network to then extract parameters from the data generated by the model. The two combined create a powerful tool that can quickly estimate densities on model parameters, even for very large systems. We demonstrate the method on synthetic time series data of the SIR model of the spread of infection, and perform an in-depth analysis of the Harris-Wilson model of economic activity on a network, representing a non-convex problem. For the latter, we apply our method both to synthetic data and to data of economic activity across Greater London. We find that our method calibrates the model orders of magnitude more accurately than a previous study of the same dataset using classical techniques, while running between 195 and 390 times faster.
    Retrieving Users' Opinions on Social Media with Multimodal Aspect-Based Sentiment Analysis. (arXiv:2210.15377v2 [cs.IR] UPDATED)
    People post their opinions and experiences on social media, yielding rich databases of end-users' sentiments. This paper shows to what extent machine learning can analyze and structure these databases. An automated data analysis pipeline is deployed to provide insights into user-generated content for researchers in other domains. First, the domain expert can select an image and a term of interest. Then, the pipeline uses image retrieval to find all images showing similar content and applies aspect-based sentiment analysis to outline users' opinions about the selected term. As part of an interdisciplinary project between architecture and computer science researchers, an empirical study of Hamburg's Elbphilharmonie was conveyed. Therefore, we selected 300 thousand posts with the hashtag \enquote{\texttt{hamburg}} from the platform Flickr. Image retrieval methods generated a subset of slightly more than 1.5 thousand images displaying the Elbphilharmonie. We found that these posts mainly convey a neutral or positive sentiment towards it. With this pipeline, we suggest a new semantic computing method that offers novel insights into end-users opinions, e.g., for architecture domain experts.
    FedDebug: Systematic Debugging for Federated Learning Applications. (arXiv:2301.03553v1 [cs.SE])
    In Federated Learning (FL), clients train a model locally and share it with a central aggregator to build a global model. Impermissibility to access client's data and collaborative training makes FL appealing for applications with data-privacy concerns such as medical imaging. However, these FL characteristics pose unprecedented challenges for debugging. When a global model's performance deteriorates, finding the round and the clients responsible is a major pain point. Developers resort to trial-and-error debugging with subsets of clients, hoping to increase the accuracy or let future FL rounds retune the model, which are time-consuming and costly. We design a systematic fault localization framework, FedDebug, that advances the FL debugging on two novel fronts. First, FedDebug enables interactive debugging of realtime collaborative training in FL by leveraging record and replay techniques to construct a simulation that mirrors live FL. FedDebug's {\em breakpoint} can help inspect an FL state (round, client, and global model) and seamlessly move between rounds and clients' models, enabling a fine-grained step-by-step inspection. Second, FedDebug automatically identifies the client responsible for lowering global model's performance without any testing data and labels--both are essential for existing debugging techniques. FedDebug's strengths come from adapting differential testing in conjunction with neurons activations to determine the precise client deviating from normal behavior. FedDebug achieves 100\% to find a single client and 90.3\% accuracy to find multiple faulty clients. FedDebug's interactive debugging incurs 1.2\% overhead during training, while it localizes a faulty client in only 2.1\% of a round's training time. With FedDebug, we bring effective debugging practices to federated learning, improving the quality and productivity of FL application developers.
    Differentially private inference via noisy optimization. (arXiv:2103.11003v3 [math.ST] UPDATED)
    We propose a general optimization-based framework for computing differentially private M-estimators and a new method for constructing differentially private confidence regions. Firstly, we show that robust statistics can be used in conjunction with noisy gradient descent or noisy Newton methods in order to obtain optimal private estimators with global linear or quadratic convergence, respectively. We establish local and global convergence guarantees, under both local strong convexity and self-concordance, showing that our private estimators converge with high probability to a nearly optimal neighborhood of the non-private M-estimators. Secondly, we tackle the problem of parametric inference by constructing differentially private estimators of the asymptotic variance of our private M-estimators. This naturally leads to approximate pivotal statistics for constructing confidence regions and conducting hypothesis testing. We demonstrate the effectiveness of a bias correction that leads to enhanced small-sample empirical performance in simulations. We illustrate the benefits of our methods in several numerical examples.
    MixCycle: Unsupervised Speech Separation via Cyclic Mixture Permutation Invariant Training. (arXiv:2202.03875v2 [eess.AS] UPDATED)
    We introduce two unsupervised source separation methods, which involve self-supervised training from single-channel two-source speech mixtures. Our first method, mixture permutation invariant training (MixPIT), enables learning a neural network model which separates the underlying sources via a challenging proxy task without supervision from the reference sources. Our second method, cyclic mixture permutation invariant training (MixCycle), uses MixPIT as a building block in a cyclic fashion for continuous learning. MixCycle gradually converts the problem from separating mixtures of mixtures into separating single mixtures. We compare our methods to common supervised and unsupervised baselines: permutation invariant training with dynamic mixing (PIT-DM) and mixture invariant training (MixIT). We show that MixCycle outperforms MixIT and reaches a performance level very close to the supervised baseline (PIT-DM) while circumventing the over-separation issue of MixIT. Also, we propose a self-evaluation technique inspired by MixCycle that estimates model performance without utilizing any reference sources. We show that it yields results consistent with an evaluation on reference sources (LibriMix) and also with an informal listening test conducted on a real-life mixtures dataset (REAL-M).
    Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance. (arXiv:2301.03136v1 [cs.CL])
    Extraction of sentiment signals from news text, stock message boards, and business reports, for stock movement prediction, has been a rising field of interest in finance. Building upon past literature, the most recent works attempt to better capture sentiment from sentences with complex syntactic structures by introducing aspect-level sentiment classification (ASC). Despite the growing interest, however, fine-grained sentiment analysis has not been fully explored in non-English literature due to the shortage of annotated finance-specific data. Accordingly, it is necessary for non-English languages to leverage datasets and pre-trained language models (PLM) of different domains, languages, and tasks to best their performance. To facilitate finance-specific ASC research in the Korean language, we build KorFinASC, a Korean aspect-level sentiment classification dataset for finance consisting of 12,613 human-annotated samples, and explore methods of intermediate transfer learning. Our experiments indicate that past research has been ignorant towards the potentially wrong knowledge of financial entities encoded during the training phase, which has overestimated the predictive power of PLMs. In our work, we use the term "non-stationary knowledge'' to refer to information that was previously correct but is likely to change, and present "TGT-Masking'', a novel masking pattern to restrict PLMs from speculating knowledge of the kind. Finally, through a series of transfer learning with TGT-Masking applied we improve 22.63% of classification accuracy compared to standalone models on KorFinASC.
    A review of clustering models in educational data science towards fairness-aware learning. (arXiv:2301.03421v1 [cs.LG])
    Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.
    Federated Learning with Domain Generalization. (arXiv:2111.10487v2 [cs.LG] UPDATED)
    Federated Learning (FL) enables a group of clients to jointly train a machine learning model with the help of a centralized server. Clients do not need to submit their local data to the server during training, and hence the local training data of clients is protected. In FL, distributed clients collect their local data independently, so the dataset of each client may naturally form a distinct source domain. In practice, the model trained over multiple source domains may have poor generalization performance on unseen target domains. To address this issue, we propose FedADG to equip federated learning with domain generalization capability. FedADG employs the federated adversarial learning approach to measure and align the distributions among different source domains via matching each distribution to a reference distribution. The reference distribution is adaptively generated (by accommodating all source domains) to minimize the domain shift distance during alignment. In FedADG, the alignment is fine-grained since each class is aligned independently. In this way, the learned feature representation is supposed to be universal, so it can generalize well on the unseen domains. Intensive experiments on various datasets demonstrate that FedADG has comparable performance with the state-of-the-art.
    Why Batch Normalization Damage Federated Learning on Non-IID Data?. (arXiv:2301.02982v1 [cs.LG])
    As a promising distributed learning paradigm, federated learning (FL) involves training deep neural network (DNN) models at the network edge while protecting the privacy of the edge clients. To train a large-scale DNN model, batch normalization (BN) has been regarded as a simple and effective means to accelerate the training and improve the generalization capability. However, recent findings indicate that BN can significantly impair the performance of FL in the presence of non-i.i.d. data. While several FL algorithms have been proposed to address this issue, their performance still falls significantly when compared to the centralized scheme. Furthermore, none of them have provided a theoretical explanation of how the BN damages the FL convergence. In this paper, we present the first convergence analysis to show that under the non-i.i.d. data, the mismatch between the local and global statistical parameters in BN causes the gradient deviation between the local and global models, which, as a result, slows down and biases the FL convergence. In view of this, we develop a new FL algorithm that is tailored to BN, called FedTAN, which is capable of achieving robust FL performance under a variety of data distributions via iterative layer-wise parameter aggregation. Comprehensive experimental results demonstrate the superiority of the proposed FedTAN over existing baselines for training BN-based DNN models.  ( 2 min )
    Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control. (arXiv:2301.03565v1 [eess.SY])
    Data-driven control algorithms use observations of system dynamics to construct an implicit model for the purpose of control. However, in practice, data-driven techniques often require excessive sample sizes, which may be infeasible in real-world scenarios where only limited observations of the system are available. Furthermore, purely data-driven methods often neglect useful a priori knowledge, such as approximate models of the system dynamics. We present a method to incorporate such prior knowledge into data-driven control algorithms using kernel embeddings, a nonparametric machine learning technique based in the theory of reproducing kernel Hilbert spaces. Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem. We formulate the biased learning problem as a least-squares problem with a regularization term that is informed by the dynamics, that has an efficiently computable, closed-form solution. Through numerical experiments, we empirically demonstrate the improved sample efficiency and out-of-sample generalization of our approach over a purely data-driven baseline. We demonstrate an application of our method to control through a target tracking problem with nonholonomic dynamics, and on spring-mass-damper and F-16 aircraft state prediction tasks.
    Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group. (arXiv:2301.03019v1 [cs.LG])
    Convolutional neural networks revolutionized computer vision and natrual language processing. Their efficiency, as compared to fully connected neural networks, has its origin in the architecture, where convolutions reflect the translation invariance in space and time in pattern or speech recognition tasks. Recently, Cohen and Welling have put this in the broader perspective of invariance under symmetry groups, which leads to the concept of group equivaiant neural networks and more generally steerable neural networks. In this article, we review the architecture of such networks including equivariant layers and filter banks, activation with capsules and group pooling. We apply this formalism to the symmetric group, for which we work out a number of details on representations and capsules that are not found in the literature.  ( 2 min )
    Systems for Parallel and Distributed Large-Model Deep Learning Training. (arXiv:2301.02691v1 [cs.DC])
    Deep learning (DL) has transformed applications in a variety of domains, including computer vision, natural language processing, and tabular data analysis. The search for improved DL model accuracy has led practitioners to explore increasingly large neural architectures, with some recent Transformer models spanning hundreds of billions of learnable parameters. These designs have introduced new scale-driven systems challenges for the DL space, such as memory bottlenecks, poor runtime efficiency, and high costs of model development. Efforts to address these issues have explored techniques such as parallelization of neural architectures, spilling data across the memory hierarchy, and memory-efficient data representations. This survey will explore the large-model training systems landscape, highlighting key challenges and the various techniques that have been used to address them.  ( 2 min )
    Exploration in Model-based Reinforcement Learning with Randomized Reward. (arXiv:2301.03142v1 [stat.ML])
    Model-based Reinforcement Learning (MBRL) has been widely adapted due to its sample efficiency. However, existing worst-case regret analysis typically requires optimistic planning, which is not realistic in general. In contrast, motivated by the theory, empirical study utilizes ensemble of models, which achieve state-of-the-art performance on various testing environments. Such deviation between theory and empirical study leads us to question whether randomized model ensemble guarantee optimism, and hence the optimal worst-case regret? This paper partially answers such question from the perspective of reward randomization, a scarcely explored direction of exploration with MBRL. We show that under the kernelized linear regulator (KNR) model, reward randomization guarantees a partial optimism, which further yields a near-optimal worst-case regret in terms of the number of interactions. We further extend our theory to generalized function approximation and identified conditions for reward randomization to attain provably efficient exploration. Correspondingly, we propose concrete examples of efficient reward randomization. To the best of our knowledge, our analysis establishes the first worst-case regret analysis on randomized MBRL with function approximation.  ( 2 min )
    Machine Learning to Estimate Gross Loss of Jewelry for Wax Patterns. (arXiv:2301.02872v1 [cs.LG])
    In mass manufacturing of jewellery, the gross loss is estimated before manufacturing to calculate the wax weight of the pattern that would be investment casted to make multiple identical pieces of jewellery. Machine learning is a technology that is a part of AI which helps create a model with decision-making capabilities based on a large set of user-defined data. In this paper, the authors found a way to use Machine Learning in the jewellery industry to estimate this crucial Gross Loss. Choosing a small data set of manufactured rings and via regression analysis, it was found out that there is a potential of reducing the error in estimation from +-2-3 to +-0.5 using ML Algorithms from historic data and attributes collected from the CAD file during the design phase itself. To evaluate the approach's viability, additional study must be undertaken with a larger data set.  ( 2 min )
    DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models. (arXiv:2107.05556v5 [q-bio.QM] UPDATED)
    Computational models that accurately predict the binding affinity of an input protein-chemical pair can accelerate drug discovery studies. These models are trained on available protein-chemical interaction datasets, which may contain dataset biases that may lead the model to learn dataset-specific patterns, instead of generalizable relationships. As a result, the prediction performance of models drops for previously unseen biomolecules, $\textit{i.e.}$ the prediction models cannot generalize to biomolecules outside of the dataset. The latest approaches that aim to improve model generalizability either have limited applicability or introduce the risk of degrading prediction performance. Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models. DebiasedDTA reweights the training samples to mitigate the effect of dataset biases and is applicable to most DTA prediction models. The results suggest that models trained in the DebiasedDTA framework can achieve improved generalizability in predicting the interactions of the previously unseen biomolecules, as well as performance improvements on those previously seen. Extensive experiments with different biomolecule representations, model architectures, and datasets demonstrate that DebiasedDTA can upgrade DTA prediction models irrespective of the biomolecule representation, model architecture, and training dataset. Last but not least, we release DebiasedDTA as an open-source python library to enable other researchers to debias their own predictors and/or develop their own debiasing methods. We believe that this python library will corroborate and foster research to develop more generalizable DTA prediction models.
    FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models. (arXiv:2301.02959v1 [cs.LG])
    Sequence-based deep learning recommendation models (DLRMs) are an emerging class of DLRMs showing great improvements over their prior sum-pooling based counterparts at capturing users' long term interests. These improvements come at immense system cost however, with sequence-based DLRMs requiring substantial amounts of data to be dynamically materialized and communicated by each accelerator during a single iteration. To address this rapidly growing bottleneck, we present FlexShard, a new tiered sequence embedding table sharding algorithm which operates at a per-row granularity by exploiting the insight that not every row is equal. Through precise replication of embedding rows based on their underlying probability distribution, along with the introduction of a new sharding strategy adapted to the heterogeneous, skewed performance of real-world cluster network topologies, FlexShard is able to significantly reduce communication demand while using no additional memory compared to the prior state-of-the-art. When evaluated on production-scale sequence DLRMs, FlexShard was able to reduce overall global all-to-all communication traffic by over 85%, resulting in end-to-end training communication latency improvements of almost 6x over the prior state-of-the-art approach.
    Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition. (arXiv:2301.02736v1 [eess.AS])
    Despite improvements to the generalization performance of automated speech recognition (ASR) models, specializing ASR models for downstream tasks remains a challenging task, primarily due to reduced data availability (necessitating increased data collection), and rapidly shifting data distributions (requiring more frequent model fine-tuning). In this work, we investigate the potential of leveraging external knowledge, particularly through off-policy key-value stores generated with text-to-speech methods, to allow for flexible post-training adaptation to new data distributions. In our approach, audio embeddings captured from text-to-speech, along with semantic text embeddings, are used to bias ASR via an approximate k-nearest-neighbor (KNN) based attentive fusion step. Our experiments on LibiriSpeech and in-house voice assistant/search datasets show that the proposed approach can reduce domain adaptation time by up to 1K GPU-hours while providing up to 3% WER improvement compared to a fine-tuning baseline, suggesting a promising approach for adapting production ASR systems in challenging zero and few-shot scenarios.  ( 2 min )
    The Optimal Input-Independent Baseline for Binary Classification: The Dutch Draw. (arXiv:2301.03318v1 [cs.LG])
    Before any binary classification model is taken into practice, it is important to validate its performance on a proper test set. Without a frame of reference given by a baseline method, it is impossible to determine if a score is `good' or `bad'. The goal of this paper is to examine all baseline methods that are independent of feature values and determine which model is the `best' and why. By identifying which baseline models are optimal, a crucial selection decision in the evaluation process is simplified. We prove that the recently proposed Dutch Draw baseline is the best input-independent classifier (independent of feature values) for all positional-invariant measures (independent of sequence order) assuming that the samples are randomly shuffled. This means that the Dutch Draw baseline is the optimal baseline under these intuitive requirements and should therefore be used in practice.  ( 2 min )
    PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks. (arXiv:2006.07794v2 [cs.LG] UPDATED)
    Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data. A recent class of methods to address this problem uses various ways to construct a new training sample by mixing a pair (or more) of training samples. We propose PatchUp, a hidden state block-level regularization technique for Convolutional Neural Networks (CNNs), that is applied on selected contiguous blocks of feature maps from a random pair of samples. Our approach improves the robustness of CNN models against the manifold intrusion problem that may occur in other state-of-the-art mixing approaches. Moreover, since we are mixing the contiguous block of features in the hidden space, which has more dimensions than the input space, we obtain more diverse samples for training towards different dimensions. Our experiments on CIFAR10/100, SVHN, Tiny-ImageNet, and ImageNet using ResNet architectures including PreActResnet18/34, WRN-28-10, ResNet101/152 models show that PatchUp improves upon, or equals, the performance of current state-of-the-art regularizers for CNNs. We also show that PatchUp can provide a better generalization to deformed samples and is more robust against adversarial attacks.  ( 2 min )
    A Newton-CG based augmented Lagrangian method for finding a second-order stationary point of nonconvex equality constrained optimization with complexity guarantees. (arXiv:2301.03139v1 [math.OC])
    In this paper we consider finding a second-order stationary point (SOSP) of nonconvex equality constrained optimization when a nearly feasible point is known. In particular, we first propose a new Newton-CG method for finding an approximate SOSP of unconstrained optimization and show that it enjoys a substantially better complexity than the Newton-CG method [56]. We then propose a Newton-CG based augmented Lagrangian (AL) method for finding an approximate SOSP of nonconvex equality constrained optimization, in which the proposed Newton-CG method is used as a subproblem solver. We show that under a generalized linear independence constraint qualification (GLICQ), our AL method enjoys a total inner iteration complexity of $\widetilde{\cal O}(\epsilon^{-7/2})$ and an operation complexity of $\widetilde{\cal O}(\epsilon^{-7/2}\min\{n,\epsilon^{-3/4}\})$ for finding an $(\epsilon,\sqrt{\epsilon})$-SOSP of nonconvex equality constrained optimization with high probability, which are significantly better than the ones achieved by the proximal AL method [60]. Besides, we show that it has a total inner iteration complexity of $\widetilde{\cal O}(\epsilon^{-11/2})$ and an operation complexity of $\widetilde{\cal O}(\epsilon^{-11/2}\min\{n,\epsilon^{-5/4}\})$ when the GLICQ does not hold. To the best of our knowledge, all the complexity results obtained in this paper are new for finding an approximate SOSP of nonconvex equality constrained optimization with high probability. Preliminary numerical results also demonstrate the superiority of our proposed methods over the ones in [56,60].  ( 2 min )
    Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing. (arXiv:2301.03415v1 [cs.PL])
    We study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach for optimisation in practice is stochastic gradient descent. In particular, a variant using the so-called reparameterisation gradient estimator exhibits fast convergence in a traditional statistics setting. Unfortunately, discontinuities, which are readily expressible in programming languages, can compromise the correctness of this approach. We consider a simple (higher-order, probabilistic) programming language with conditionals, and we endow our language with both a measurable and a smoothed (approximate) value semantics. We present type systems which establish technical pre-conditions. Thus we can prove stochastic gradient descent with the reparameterisation gradient estimator to be correct when applied to the smoothed problem. Besides, we can solve the original problem up to any error tolerance by choosing an accuracy coefficient suitably. Empirically we demonstrate that our approach has a similar convergence as a key competitor, but is simpler, faster, and attains orders of magnitude reduction in work-normalised variance.
    XDQN: Inherently Interpretable DQN through Mimicking. (arXiv:2301.03043v1 [cs.LG])
    Although deep reinforcement learning (DRL) methods have been successfully applied in challenging tasks, their application in real-world operational settings is challenged by methods' limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner constituent models of the DRL method, thus making the DRL method "inherently" interpretable. In this paper we explore this paradigm and we propose XDQN, an explainable variation of DQN, which uses an interpretable policy model trained through mimicking. XDQN is challenged in a complex, real-world operational multi-agent problem, where agents are independent learners solving congestion problems. Specifically, XDQN is evaluated in three MARL scenarios, pertaining to the demand-capacity balancing problem of air traffic management. XDQN achieves performance similar to that of DQN, while its abilities to provide global models' interpretations and interpretations of local decisions are demonstrated.  ( 2 min )
    Differentiable Simulations for Enhanced Sampling of Rare Events. (arXiv:2301.03480v1 [physics.chem-ph])
    We develop a novel approach to enhanced sampling of chemically reactive events using differentiable simulations. We merge the reaction path discovery and biasing potential computation into one end-to-end problem and solve it by path-integral optimization. The techniques developed contribute directly to the understanding and usability of differentiable simulations as we introduce new approaches and prove the stability properties of our method.
    Modeling Label Semantics Improves Activity Recognition. (arXiv:2301.03462v1 [cs.LG])
    Human activity recognition (HAR) aims to classify sensory time series into different activities, with wide applications in activity tracking, healthcare, human computer interaction, etc. Existing HAR works improve recognition performance by designing more complicated feature extraction methods, but they neglect the label semantics by simply treating labels as integer IDs. We find that many activities in the current HAR datasets have shared label names, e.g., "open door" and "open fridge", "walk upstairs" and "walk downstairs". Through some exploratory analysis, we find that such shared structure in activity names also maps to similarity in the input features. To this end, we design a sequence-to-sequence framework to decode the label name semantics rather than classifying labels as integer IDs. Our proposed method decomposes learning activities into learning shared tokens ("open", "walk"), which is easier than learning the joint distribution ("open fridge", "walk upstairs") and helps transfer learning to activities with insufficient data samples. For datasets originally without shared tokens in label names, we also offer an automated method, using OpenAI's ChatGPT, to generate shared actions and objects. Extensive experiments on seven HAR benchmark datasets demonstrate the state-of-the-art performance of our method. We also show better performance in the long-tail activity distribution settings and few-shot settings.
    Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG. (arXiv:2301.03470v1 [eess.SP])
    Epilepsy is one of the most common neurological disorders, typically observed via seizure episodes. Epileptic seizures are commonly monitored through electroencephalogram (EEG) recordings due to their routine and low expense collection. The stochastic nature of EEG makes seizure identification via manual inspections performed by highly-trained experts a tedious endeavor, motivating the use of automated identification. The literature on automated identification focuses mostly on supervised learning methods requiring expert labels of EEG segments that contain seizures, which are difficult to obtain. Motivated by these observations, we pose seizure identification as an unsupervised anomaly detection problem. To this end, we employ the first unsupervised transformer-based model for seizure identification on raw EEG. We train an autoencoder involving a transformer encoder via an unsupervised loss function, incorporating a novel masking strategy uniquely designed for multivariate time-series data such as EEG. Training employs EEG recordings that do not contain any seizures, while seizures are identified with respect to reconstruction errors at inference time. We evaluate our method on three publicly available benchmark EEG datasets for distinguishing seizure vs. non-seizure windows. Our method leads to significantly better seizure identification performance than supervised learning counterparts, by up to 16% recall, 9% accuracy, and 9% Area under the Receiver Operating Characteristics Curve (AUC), establishing particular benefits on highly imbalanced data. Through accurate seizure identification, our method could facilitate widely accessible and early detection of epilepsy development, without needing expensive label collection or manual feature extraction.
    MAQA: A Multimodal QA Benchmark for Negation. (arXiv:2301.03238v1 [cs.CL])
    Multimodal learning can benefit from the representation power of pretrained Large Language Models (LLMs). However, state-of-the-art transformer based LLMs often ignore negations in natural language and there is no existing benchmark to quantitatively evaluate whether multimodal transformers inherit this weakness. In this study, we present a new multimodal question answering (QA) benchmark adapted from labeled music videos in AudioSet (Gemmeke et al., 2017) with the goal of systematically evaluating if multimodal transformers can perform complex reasoning to recognize new concepts as negation of previously learned concepts. We show that with standard fine-tuning approach multimodal transformers are still incapable of correctly interpreting negation irrespective of model size. However, our experiments demonstrate that augmenting the original training task distributions with negated QA examples allow the model to reliably reason with negation. To do this, we describe a novel data generation procedure that prompts the 540B-parameter PaLM model to automatically generate negated QA examples as compositions of easily accessible video tags. The generated examples contain more natural linguistic patterns and the gains compared to template-based task augmentation approach are significant.
    Deep Learning for Short-Latency Epileptic Seizure Detection with Probabilistic Classification. (arXiv:2301.03465v1 [eess.SP])
    In this manuscript, we propose a novel deep learning (DL)-based framework intended for obtaining short latency in real-time electroencephalogram-based epileptic seizure detection using multiscale 3D convolutional neural networks. We pioneer converting seizure detection task from traditional binary classification of samples from ictal and interictal periods to probabilistic classification of samples from interictal, ictal, and crossing periods. We introduce a crossing period from seizure-oriented EEG recording and propose a labelling rule using soft-label for samples from the crossing period to build a probabilistic classification task. A novel multiscale short-time Fourier transform feature extraction method and 3D convolution neural network architecture are proposed to accurately capture predictive probabilities of samples. Furthermore, we also propose rectified weighting strategy to enhance predictive probabilities, and accumulative decision-making rule to achieve short detection latency. We implement leave-one-seizure-out cross validation on two prevalent datasets -- CHB-MIT scalp EEG dataset and SWEC-ETHZ intracranial EEG dataset. Eventually, the proposed algorithm achieved 94 out of 99 seizures detected during the crossing period, averaged 14.84% rectified predictive ictal probability (RPIP) errors of crossing samples, 2.3 s detection latency, 0.32/h false detection rate on CHB-MIT dataset, meanwhile 84 out of 89 detected seizures, 16.17% RPIP errors, 4.7 s detection latency, and 0.75/h FDR are achieved on SWEC-ETHZ dataset. The obtained detection latencies are at least 50% faster than state-of-the-art results reported in previous studies.
    Safer Together: Machine Learning Models Trained on Shared Accident Datasets Predict Construction Injuries Better than Company-Specific Models. (arXiv:2301.03567v1 [cs.LG])
    In this study, we capitalized on a collective dataset repository of 57k accidents from 9 companies belonging to 3 domains and tested whether models trained on multiple datasets (generic models) predicted safety outcomes better than the company-specific models. We experimented with full generic models (trained on all data), per-domain generic models (construction, electric T&D, oil & gas), and with ensembles of generic and specific models. Results are very positive, with generic models outperforming the company-specific models in most cases while also generating finer-grained, hence more useful, forecasts. Successful generic models remove the needs for training company-specific models, saving a lot of time and resources, and give small companies, whose accident datasets are too limited to train their own models, access to safety outcome predictions. It may still however be advantageous to train specific models to get an extra boost in performance through ensembling with the generic models. Overall, by learning lessons from a pool of datasets whose accumulated experience far exceeds that of any single company, and making these lessons easily accessible in the form of simple forecasts, generic models tackle the holy grail of safety cross-organizational learning and dissemination in the construction industry.
    Cursive Caption Text Detection in Videos. (arXiv:2301.03164v1 [cs.CV])
    Textual content appearing in videos represents an interesting index for semantic retrieval of videos (from archives), generation of alerts (live streams) as well as high level applications like opinion mining and content summarization. One of the key components of such systems is the detection of textual content in video frames and the same makes the subject of our present study. This paper presents a robust technique for detection of textual content appearing in video frames. More specifically we target text in cursive script taking Urdu text as a case study. Detection of textual regions in video frames is carried out by fine-tuning object detectors based on deep convolutional neural networks for the specific case of text detection. Since it is common to have videos with caption text in multiple-scripts, cursive text is distinguished from Latin text using a script-identification module. Finally, detection and script identification are combined in a single end-to-end trainable system. Experiments on a comprehensive dataset of around 11,000 video frames report an F-measure of 0.91.
    eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging. (arXiv:2301.03162v1 [physics.optics])
    The application of deep learning techniques has greatly enhanced holographic imaging capabilities, leading to improved phase recovery and image reconstruction. Here, we introduce a deep neural network termed enhanced Fourier Imager Network (eFIN) as a highly generalizable framework for hologram reconstruction with pixel super-resolution and image autofocusing. Through holographic microscopy experiments involving lung, prostate and salivary gland tissue sections and Papanicolau (Pap) smears, we demonstrate that eFIN has a superior image reconstruction quality and exhibits external generalization to new types of samples never seen during the training phase. This network achieves a wide autofocusing axial range of 0.35 mm, with the capability to accurately predict the hologram axial distances by physics-informed learning. eFIN enables 3x pixel super-resolution imaging and increases the space-bandwidth product of the reconstructed images by 9-fold with almost no performance loss, which allows for significant time savings in holographic imaging and data processing steps. Our results showcase the advancements of eFIN in pushing the boundaries of holographic imaging for various applications in e.g., quantitative phase imaging and label-free microscopy.  ( 2 min )
    KIDS: kinematics-based (in)activity detection and segmentation in a sleep case study. (arXiv:2301.03469v1 [eess.SP])
    Sleep behaviour and in-bed movements contain rich information on the neurophysiological health of people, and have a direct link to the general well-being and quality of life. Standard clinical practices rely on polysomnography for sleep assessment; however, it is intrusive, performed in unfamiliar environments and requires trained personnel. Progress has been made on less invasive sensor technologies, such as actigraphy, but clinical validation raises concerns over their reliability and precision. Additionally, the field lacks a widely acceptable algorithm, with proposed approaches ranging from raw signal or feature thresholding to data-hungry classification models, many of which are unfamiliar to medical staff. This paper proposes an online Bayesian probabilistic framework for objective (in)activity detection and segmentation based on clinically meaningful joint kinematics, measured by a custom-made wearable sensor. Intuitive three-dimensional visualisations of kinematic timeseries were accomplished through dimension reduction based preprocessing, offering out-of-the-box framework explainability potentially useful for clinical monitoring and diagnosis. The proposed framework attained up to 99.2\% $F_1$-score and 0.96 Pearson's correlation coefficient in, respectively, the posture change detection and inactivity segmentation tasks. The work paves the way for a reliable home-based analysis of movements during sleep which would serve patient-centred longitudinal care plans.
    Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning. (arXiv:2301.03262v1 [cs.NI])
    Deep reinforcement learning (DRL) has been increasingly employed to handle the dynamic and complex resource management in network slicing. The deployment of DRL policies in real networks, however, is complicated by heterogeneous cell conditions. In this paper, we propose a novel transfer learning (TL) aided multi-agent deep reinforcement learning (MADRL) approach with inter-agent similarity analysis for inter-cell inter-slice resource partitioning. First, we design a coordinated MADRL method with information sharing to intelligently partition resource to slices and manage inter-cell interference. Second, we propose an integrated TL method to transfer the learned DRL policies among different local agents for accelerating the policy deployment. The method is composed of a new domain and task similarity measurement approach and a new knowledge transfer approach, which resolves the problem of from whom to transfer and how to transfer. We evaluated the proposed solution with extensive simulations in a system-level simulator and show that our approach outperforms the state-of-the-art solutions in terms of performance, convergence speed and sample efficiency. Moreover, by applying TL, we achieve an additional gain over 27% higher than the coordinate MADRL approach without TL.
    On the challenges to learn from Natural Data Streams. (arXiv:2301.03495v1 [cs.CV])
    In real-world contexts, sometimes data are available in form of Natural Data Streams, i.e. data characterized by a streaming nature, unbalanced distribution, data drift over a long time frame and strong correlation of samples in short time ranges. Moreover, a clear separation between the traditional training and deployment phases is usually lacking. This data organization and fruition represents an interesting and challenging scenario for both traditional Machine and Deep Learning algorithms and incremental learning agents, i.e. agents that have the ability to incrementally improve their knowledge through the past experience. In this paper, we investigate the classification performance of a variety of algorithms that belong to various research field, i.e. Continual, Streaming and Online Learning, that receives as training input Natural Data Streams. The experimental validation is carried out on three different datasets, expressly organized to replicate this challenging setting.
    Deep Learning for Mean Field Games with non-separable Hamiltonians. (arXiv:2301.02877v1 [cs.LG])
    This paper introduces a new method based on Deep Galerkin Methods (DGMs) for solving high-dimensional stochastic Mean Field Games (MFGs). We achieve this by using two neural networks to approximate the unknown solutions of the MFG system and forward-backward conditions. Our method is efficient, even with a small number of iterations, and is capable of handling up to 300 dimensions with a single layer, which makes it faster than other approaches. In contrast, methods based on Generative Adversarial Networks (GANs) cannot solve MFGs with non-separable Hamiltonians. We demonstrate the effectiveness of our approach by applying it to a traffic flow problem, which was previously solved using the Newton iteration method only in the deterministic case. We compare the results of our method to analytical solutions and previous approaches, showing its efficiency. We also prove the convergence of our neural network approximation with a single hidden layer using the universal approximation theorem.  ( 2 min )
    Stochastic Langevin Monte Carlo for (weakly) log-concave posterior distributions. (arXiv:2301.03077v1 [stat.ML])
    In this paper, we investigate a continuous time version of the Stochastic Langevin Monte Carlo method, introduced in [WT11], that incorporates a stochastic sampling step inside the traditional over-damped Langevin diffusion. This method is popular in machine learning for sampling posterior distribution. We will pay specific attention in our work to the computational cost in terms of $n$ (the number of observations that produces the posterior distribution), and $d$ (the dimension of the ambient space where the parameter of interest is living). We derive our analysis in the weakly convex framework, which is parameterized with the help of the Kurdyka-\L ojasiewicz (KL) inequality, that permits to handle a vanishing curvature settings, which is far less restrictive when compared to the simple strongly convex case. We establish that the final horizon of simulation to obtain an $\varepsilon$ approximation (in terms of entropy) is of the order $( d \log(n)^2 )^{(1+r)^2} [\log^2(\varepsilon^{-1}) + n^2 d^{2(1+r)} \log^{4(1+r)}(n) ]$ with a Poissonian subsampling of parameter $\left(n ( d \log^2(n))^{1+r}\right)^{-1}$, where the parameter $r$ is involved in the KL inequality and varies between $0$ (strongly convex case) and $1$ (limiting Laplace situation).  ( 2 min )
    LS-DYNA Machine Learning-based Multiscale Method for Nonlinear Modeling of Short Fiber-Reinforced Composites. (arXiv:2301.02738v1 [cs.CE])
    Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC. DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. By coupling DMN with finite elements, we have developed a highly accurate and efficient data-driven approach, which predicts nonlinear behaviors of composite materials and structures at a computational speed orders-of-magnitude faster than the high-fidelity direct numerical simulation. To model industrial-scale SFRC products, transfer learning is utilized to generate a unified DMN database, which effectively captures the effects of injection molding-induced fiber orientations and volume fractions on the overall composite properties. Numerical examples are presented to demonstrate the promising performance of this LS-DYNA machine learning-based multiscale method for SFRC modeling.  ( 2 min )
    GAN-Based Content Generation of Maps for Strategy Games. (arXiv:2301.02874v1 [cs.LG])
    Maps are a very important component of strategy games, and a time-consuming task if done by hand. Maps generated by traditional PCG techniques such as Perlin noise or tile-based PCG techniques look unnatural and unappealing, thus not providing the best user experience for the players. However it is possible to have a generator that can create realistic and natural images of maps, given that it is trained how to do so. We propose a model for the generation of maps based on Generative Adversarial Networks (GAN). In our implementation we tested out different variants of GAN-based networks on a dataset of heightmaps. We conducted extensive empirical evaluation to determine the advantages and properties of each approach. The results obtained are promising, showing that it is indeed possible to generate realistic looking maps using this type of approach.  ( 2 min )
    Sublinear Time Algorithms for Several Geometric Optimization (With Outliers) Problems In Machine Learning. (arXiv:2301.02870v1 [cs.DS])
    In this paper, we study several important geometric optimization problems arising in machine learning. First, we revisit the Minimum Enclosing Ball (MEB) problem in Euclidean space $\mathbb{R}^d$. The problem has been extensively studied before, but real-world machine learning tasks often need to handle large-scale datasets so that we cannot even afford linear time algorithms. Motivated by the recent studies on {\em beyond worst-case analysis}, we introduce the notion of stability for MEB, which is natural and easy to understand. Roughly speaking, an instance of MEB is stable, if the radius of the resulting ball cannot be significantly reduced by removing a small fraction of the input points. Under the stability assumption, we present two sampling algorithms for computing radius-approximate MEB with sample complexities independent of the number of input points $n$. In particular, the second algorithm has the sample complexity even independent of the dimensionality $d$. We also consider the general case without the stability assumption. We present a hybrid algorithm that can output either a radius-approximate MEB or a covering-approximate MEB. Our algorithm improves the running time and the number of passes for the previous sublinear MEB algorithms. Our method relies on two novel techniques, the Uniform-Adaptive Sampling method and Sandwich Lemma. Furthermore, we observe that these two techniques can be generalized to design sublinear time algorithms for a broader range of geometric optimization problems with outliers in high dimensions, including MEB with outliers, one-class and two-class linear SVMs with outliers, $k$-center clustering with outliers, and flat fitting with outliers. Our proposed algorithms also work fine for kernels.  ( 2 min )
    A Survey on Transformers in Reinforcement Learning. (arXiv:2301.03044v1 [cs.LG])
    Transformer has been considered the dominating neural architecture in NLP and CV, mostly under a supervised setting. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL), but it is faced with unique design choices and challenges brought by the nature of RL. However, the evolution of Transformers in RL has not yet been well unraveled. Hence, in this paper, we seek to systematically review motivations and progress on using Transformers in RL, provide a taxonomy on existing works, discuss each sub-field, and summarize future prospects.
    Self-Supervised Time-to-Event Modeling with Structured Medical Records. (arXiv:2301.03150v1 [cs.LG])
    Time-to-event models (also known as survival models) are used in medicine and other fields for estimating the probability distribution of the time until a particular event occurs. While providing many advantages over traditional classification models, such as naturally handling censoring, time-to-event models require more parameters and are challenging to learn in settings with limited labeled training data. High censoring rates, common in events with long time horizons, further limit available training data and exacerbate the risk of overfitting. Existing methods, such as proportional hazard or accelerated failure time-based approaches, employ distributional assumptions to reduce parameter size, but they are vulnerable to model misspecification. In this work, we address these challenges with MOTOR, a self-supervised model that leverages temporal structure found in large-scale collections of timestamped, but largely unlabeled events, typical of electronic health record data. MOTOR defines a time-to-event pretraining task that naturally captures the probability distribution of event times, making it well-suited to applications in medicine. After pretraining on 8,192 tasks auto-generated from 2.7M patients (2.4B clinical events), we evaluate the performance of our pretrained model after fine-tuning to unseen time-to-event tasks. MOTOR-derived models improve upon current state-of-the-art C statistic performance by 6.6% and decrease training time (in wall time) by up to 8.2 times. We further improve sample efficiency, with adapted models matching current state-of-the-art performance using 95% less training data.
    AI2: The next leap toward native language based and explainable machine learning framework. (arXiv:2301.03391v1 [cs.LG])
    The machine learning frameworks flourished in the last decades, allowing artificial intelligence to get out of academic circles to be applied to enterprise domains. This field has significantly advanced, but there is still some meaningful improvement to reach the subsequent expectations. The proposed framework, named AI$^{2}$, uses a natural language interface that allows a non-specialist to benefit from machine learning algorithms without necessarily knowing how to program with a programming language. The primary contribution of the AI$^{2}$ framework allows a user to call the machine learning algorithms in English, making its interface usage easier. The second contribution is greenhouse gas (GHG) awareness. It has some strategies to evaluate the GHG generated by the algorithm to be called and to propose alternatives to find a solution without executing the energy-intensive algorithm. Another contribution is a preprocessing module that helps to describe and to load data properly. Using an English text-based chatbot, this module guides the user to define every dataset so that it can be described, normalized, loaded and divided appropriately. The last contribution of this paper is about explainability. For decades, the scientific community has known that machine learning algorithms imply the famous black-box problem. Traditional machine learning methods convert an input into an output without being able to justify this result. The proposed framework explains the algorithm's process with the proper texts, graphics and tables. The results, declined in five cases, present usage applications from the user's English command to the explained output. Ultimately, the AI$^{2}$ framework represents the next leap toward native language-based, human-oriented concerns about machine learning framework.
    Fair Multi-Exit Framework for Facial Attribute Classification. (arXiv:2301.02989v1 [cs.CV])
    Fairness has become increasingly pivotal in facial recognition. Without bias mitigation, deploying unfair AI would harm the interest of the underprivileged population. In this paper, we observe that though the higher accuracy that features from the deeper layer of a neural networks generally offer, fairness conditions deteriorate as we extract features from deeper layers. This phenomenon motivates us to extend the concept of multi-exit framework. Unlike existing works mainly focusing on accuracy, our multi-exit framework is fairness-oriented, where the internal classifiers are trained to be more accurate and fairer. During inference, any instance with high confidence from an internal classifier is allowed to exit early. Moreover, our framework can be applied to most existing fairness-aware frameworks. Experiment results show that the proposed framework can largely improve the fairness condition over the state-of-the-art in CelebA and UTK Face datasets.
    Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets. (arXiv:2301.03373v1 [cs.LG])
    The research applies AI-driven code assistants to analyze a selection of influential computer code that has shaped modern technology, including email, internet browsing, robotics, and malicious software. The original contribution of this study was to examine half of the most significant code advances in the last 50 years and, in some cases, to provide notable improvements in clarity or performance. The AI-driven code assistant could provide insights into obfuscated code or software lacking explanatory commentary in all cases examined. We generated additional sample problems based on bug corrections and code optimizations requiring much deeper reasoning than a traditional Google search might provide. Future work focuses on adding automated documentation and code commentary and translating select large code bases into more modern versions with multiple new application programming interfaces (APIs) and chained multi-tasks. The AI-driven code assistant offers a valuable tool for software engineering, particularly in its ability to provide human-level expertise and assist in refactoring legacy code or simplifying the explanation or functionality of high-value repositories.
    CaSpeR: Latent Spectral Regularization for Continual Learning. (arXiv:2301.03345v1 [cs.LG])
    While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution. Rehearsal-based Continual Learning (CL) approaches have been established as a versatile and reliable solution to overcome this limitation; however, sudden input disruptions and memory constraints are known to alter the consistency of their predictions. We study this phenomenon by investigating the geometric characteristics of the learner's latent space and find that replayed data points of different classes increasingly mix up, interfering with classification. Hence, we propose a geometric regularizer that enforces weak requirements on the Laplacian spectrum of the latent space, promoting a partitioning behavior. We show that our proposal, called Continual Spectral Regularizer (CaSpeR), can be easily combined with any rehearsal-based CL approach and improves the performance of SOTA methods on standard benchmarks. Finally, we conduct additional analysis to provide insights into CaSpeR's effects and applicability.
    Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network. (arXiv:2301.03412v1 [cs.NI])
    The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massive connectivity with improved coverage and user experience has a considerable social and economic impact on our daily life. This performance relies heavily on the configuration of the network parameters. However, with the massive increase in both the size and complexity of cellular networks, network management, especially parameter configuration, is becoming complicated. The current practice, which relies largely on experts' prior knowledge, is not adequate and will require lots of domain experts and high maintenance costs. In this work, we propose a learning-based framework for handover parameter configuration. The key challenge, in this case, is to tackle the complicated dependencies between neighboring cells and jointly optimize the whole network. Our framework addresses this challenge in two ways. First, we introduce a novel approach to imitate how the network responds to different network states and parameter values, called auto-grouping graph convolutional network (AG-GCN). During the parameter configuration stage, instead of solving the global optimization problem, we design a local multi-objective optimization strategy where each cell considers several local performance metrics to balance its own performance and its neighbors. We evaluate our proposed algorithm via a simulator constructed using real network data. We demonstrate that the handover parameters our model can find, achieve better average network throughput compared to those recommended by experts as well as alternative baselines, which can bring better network quality and stability. It has the potential to massively reduce costs arising from human expert intervention and maintenance.
    Towards an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets. (arXiv:2301.03364v1 [cs.NI])
    This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+). Detailed information about the two captured datasets is provided as well. iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+ is conducted at an industrial setting where an autonomous cleaning robot is connected to a private cellular network. The combination of different communication technologies, together with a common measurement methodology, provides insights that can be exploited by Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, prediction of quality of service or link selection. Moreover, the datasets are labelled and pre-filtered for fast on-boarding and applicability. The corresponding testbeds and measurements are also presented in detail for both datasets.
    Fully Dynamic Online Selection through Online Contention Resolution Schemes. (arXiv:2301.03099v1 [cs.AI])
    We study fully dynamic online selection problems in an adversarial/stochastic setting that includes Bayesian online selection, prophet inequalities, posted price mechanisms, and stochastic probing problems subject to combinatorial constraints. In the classical ``incremental'' version of the problem, selected elements remain active until the end of the input sequence. On the other hand, in the fully dynamic version of the problem, elements stay active for a limited time interval, and then leave. This models, for example, the online matching of tasks to workers with task/worker-dependent working times, and sequential posted pricing of perishable goods. A successful approach to online selection problems in the adversarial setting is given by the notion of Online Contention Resolution Scheme (OCRS), that uses a priori information to formulate a linear relaxation of the underlying optimization problem, whose optimal fractional solution is rounded online for any adversarial order of the input sequence. Our main contribution is providing a general method for constructing an OCRS for fully dynamic online selection problems. Then, we show how to employ such OCRS to construct no-regret algorithms in a partial information model with semi-bandit feedback and adversarial inputs.
    Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN. (arXiv:2301.02971v1 [cs.LG])
    Microblog, an online-based broadcast medium, is a widely used forum for people to share their thoughts and opinions. Recently, Emotion Recognition (ER) from microblogs is an inspiring research topic in diverse areas. In the machine learning domain, automatic emotion recognition from microblogs is a challenging task, especially, for better outcomes considering diverse content. Emoticon becomes very common in the text of microblogs as it reinforces the meaning of content. This study proposes an emotion recognition scheme considering both the texts and emoticons from microblog data. Emoticons are considered unique expressions of the users' emotions and can be changed by the proper emotional words. The succession of emoticons appearing in the microblog data is preserved and a 1D Convolutional Neural Network (CNN) is employed for emotion classification. The experimental result shows that the proposed emotion recognition scheme outperforms the other existing methods while tested on Twitter data.
    Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers. (arXiv:2301.03371v1 [eess.SP])
    Holographic metasurface transceivers (HMT) is an emerging technology for enhancing the coverage and rate of wireless communication systems. However, acquiring accurate channel state information in HMT-assisted wireless communication systems is critical for achieving these goals. In this paper, we propose an algorithm for learning the optimal phase-shifts at a HMT for the far-field channel model. Our proposed algorithm exploits the structure of the channel gains in the far-field regions and learns the optimal phase-shifts in presence of noise in the received signals. We prove that the probability that the optimal phase-shifts estimated by our proposed algorithm deviate from the true values decays exponentially in the number of pilot signals. Extensive numerical simulations validate the theoretical guarantees and also demonstrate significant gains as compared to the state-of-the-art policies.
    Machine Learning for Large-Scale Optimization in 6G Wireless Networks. (arXiv:2301.03377v1 [eess.SP])
    The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms. The classic optimization-based algorithms usually require highly precise mathematical model of data links and suffer from poor performance with high computational cost in realistic 6G applications. Based on domain knowledge (e.g., optimization models and theoretical tools), machine learning (ML) stands out as a promising and viable methodology for many complex large-scale optimization problems in 6G, due to its superior performance, generalizability, computational efficiency and robustness. In this paper, we systematically review the most representative "learning to optimize" techniques in diverse domains of 6G wireless networks by identifying the inherent feature of the underlying optimization problem and investigating the specifically designed ML frameworks from the perspective of optimization. In particular, we will cover algorithm unrolling, learning to branch-and-bound, graph neural network for structured optimization, deep reinforcement learning for stochastic optimization, end-to-end learning for semantic optimization, as well as federated learning for distributed optimization, for solving challenging large-scale optimization problems arising from various important wireless applications. Through the in-depth discussion, we shed light on the excellent performance of ML-based optimization algorithms with respect to the classical methods, and provide insightful guidance to develop advanced ML techniques in 6G networks.
    A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding. (arXiv:2301.03403v1 [cs.CL])
    We provide a literature review about Automatic Text Summarization (ATS) systems. We consider a citation-based approach. We start with some popular and well-known papers that we have in hand about each topic we want to cover and we have tracked the "backward citations" (papers that are cited by the set of papers we knew beforehand) and the "forward citations" (newer papers that cite the set of papers we knew beforehand). In order to organize the different methods, we present the diverse approaches to ATS guided by the mechanisms they use to generate a summary. Besides presenting the methods, we also present an extensive review of the datasets available for summarization tasks and the methods used to evaluate the quality of the summaries. Finally, we present an empirical exploration of these methods using the CNN Corpus dataset that provides golden summaries for extractive and abstractive methods.
    Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology. (arXiv:2301.02916v1 [q-bio.GN])
    Recent genome-wide association studies (GWAS) have been successful in identifying associations between genetic variants and simple cardiac parameters derived from cardiac magnetic resonance (CMR) images. However, the emergence of big databases including genetic data linked to CMR, facilitates investigation of more nuanced patterns of shape variability. Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE). UPE builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations across the ensemble. We apply our approach to the UK Biobank database to extract left-ventricular (LV) geometric features from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes influencing LV shape, identifying 11 loci with study-wide significance and 8 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.
    Finding Lookalike Customers for E-Commerce Marketing. (arXiv:2301.03147v1 [cs.LG])
    Customer-centric marketing campaigns generate a large portion of e-commerce website traffic for Walmart. As the scale of customer data grows larger, expanding the marketing audience to reach more customers is becoming more critical for e-commerce companies to drive business growth and bring more value to customers. In this paper, we present a scalable and efficient system to expand targeted audience of marketing campaigns, which can handle hundreds of millions of customers. We use a deep learning based embedding model to represent customers and an approximate nearest neighbor search method to quickly find lookalike customers of interest. The model can deal with various business interests by constructing interpretable and meaningful customer similarity metrics. We conduct extensive experiments to demonstrate the great performance of our system and customer embedding model.
    "It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning. (arXiv:2301.02873v1 [cs.LG])
    While the promises of Multi-Task Learning (MTL) are attractive, characterizing the conditions of its success is still an open problem in Deep Learning. Some tasks may benefit from being learned together while others may be detrimental to one another. From a task perspective, grouping cooperative tasks while separating competing tasks is paramount to reap the benefits of MTL, i.e., reducing training and inference costs. Therefore, estimating task affinity for joint learning is a key endeavor. Recent work suggests that the training conditions themselves have a significant impact on the outcomes of MTL. Yet, the literature is lacking of a benchmark to assess the effectiveness of tasks affinity estimation techniques and their relation with actual MTL performance. In this paper, we take a first step in recovering this gap by (i) defining a set of affinity scores by both revisiting contributions from previous literature as well presenting new ones and (ii) benchmarking them on the Taskonomy dataset. Our empirical campaign reveals how, even in a small-scale scenario, task affinity scoring does not correlate well with actual MTL performance. Yet, some metrics can be more indicative than others.  ( 2 min )
    Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads. (arXiv:2301.02731v1 [cs.LG])
    Accurate traffic volume and speed prediction have a wide range of applications in transportation. It can result in useful and timely information for both travellers and transportation decision-makers. In this study, an Attention based Long Sort-Term Memory model (A-LSTM) is proposed to simultaneously predict traffic volume and speed in a critical rural road segmentation which connects Tehran to Chalus, the most tourist destination city in Iran. Moreover, this study compares the results of the A-LSTM model with the Long Short-Term Memory (LSTM) model. Both models show acceptable performance in predicting speed and flow. However, the A-LSTM model outperforms the LSTM in 5 and 15-minute intervals. In contrast, there is no meaningful difference between the two models for the 30-minute time interval. By comparing the performance of the models based on different time horizons, the 15-minute horizon model outperforms the others by reaching the lowest Mean Square Error (MSE) loss of 0.0032, followed by the 30 and 5-minutes horizons with 0.004 and 0.0051, respectively. In addition, this study compares the results of the models based on two transformations of temporal categorical input variables, one-hot or cyclic, for the 15-minute time interval. The results demonstrate that both LSTM and A-LSTM with cyclic feature encoding outperform those with one-hot feature encoding.  ( 2 min )
    Optimistic Meta-Gradients. (arXiv:2301.03236v1 [cs.LG])
    We study the connection between gradient-based meta-learning and convex op-timisation. We observe that gradient descent with momentum is a special case of meta-gradients, and building on recent results in optimisation, we prove convergence rates for meta-learning in the single task setting. While a meta-learned update rule can yield faster convergence up to constant factor, it is not sufficient for acceleration. Instead, some form of optimism is required. We show that optimism in meta-learning can be captured through Bootstrapped Meta-Gradients (Flennerhag et al., 2022), providing deeper insight into its underlying mechanics.
    Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel. (arXiv:2202.05254v2 [cs.LG] UPDATED)
    A biological neural network in the cortex forms a neural field. Neurons in the field have their own receptive fields, and connection weights between two neurons are random but highly correlated when they are in close proximity in receptive fields. In this paper, we investigate such neural fields in a multilayer architecture to investigate the supervised learning of the fields. We empirically compare the performances of our field model with those of randomly connected deep networks. The behavior of a randomly connected network is investigated on the basis of the key idea of the neural tangent kernel regime, a recent development in the machine learning theory of over-parameterized networks; for most randomly connected neural networks, it is shown that global minima always exist in their small neighborhoods. We numerically show that this claim also holds for our neural fields. In more detail, our model has two structures: i) each neuron in a field has a continuously distributed receptive field, and ii) the initial connection weights are random but not independent, having correlations when the positions of neurons are close in each layer. We show that such a multilayer neural field is more robust than conventional models when input patterns are deformed by noise disturbances. Moreover, its generalization ability can be slightly superior to that of conventional models.
    Introducing Model Inversion Attacks on Automatic Speaker Recognition. (arXiv:2301.03206v1 [cs.SD])
    Model inversion (MI) attacks allow to reconstruct average per-class representations of a machine learning (ML) model's training data. It has been shown that in scenarios where each class corresponds to a different individual, such as face classifiers, this represents a severe privacy risk. In this work, we explore a new application for MI: the extraction of speakers' voices from a speaker recognition system. We present an approach to (1) reconstruct audio samples from a trained ML model and (2) extract intermediate voice feature representations which provide valuable insights into the speakers' biometrics. Therefore, we propose an extension of MI attacks which we call sliding model inversion. Our sliding MI extends standard MI by iteratively inverting overlapping chunks of the audio samples and thereby leveraging the sequential properties of audio data for enhanced inversion performance. We show that one can use the inverted audio data to generate spoofed audio samples to impersonate a speaker, and execute voice-protected commands for highly secured systems on their behalf. To the best of our knowledge, our work is the first one extending MI attacks to audio data, and our results highlight the security risks resulting from the extraction of the biometric data in that setup.
    Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers. (arXiv:2301.02814v1 [cs.LG])
    In this paper, we study the problem of {\em $k$-center clustering with outliers}. The problem has many important applications in real world, but the presence of outliers can significantly increase the computational complexity. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem. Our idea is inspired by the greedy method, Gonzalez's algorithm, that was developed for solving the ordinary $k$-center clustering problem. Based on some novel observations, we show that a simple randomized version of this greedy strategy actually can handle outliers efficiently. We further show that this randomized greedy approach also yields small coreset for the problem in doubling metrics (even if the doubling dimension is not given), which can greatly reduce the computational complexity. Moreover, together with the partial clustering framework proposed in arXiv:1703.01539 , we prove that our coreset method can be applied to distributed data with a low communication complexity. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower complexities comparing with the existing methods.  ( 2 min )
    Markov Chain Concentration with an Application in Reinforcement Learning. (arXiv:2301.02926v1 [cs.LG])
    Given $X_1,\cdot ,X_N$ random variables whose joint distribution is given as $\mu$ we will use the Martingale Method to show any Lipshitz Function $f$ over these random variables is subgaussian. The Variance parameter however can have a simple expression under certain conditions. For example under the assumption that the random variables follow a Markov Chain and that the function is Lipschitz under a Weighted Hamming Metric. We shall conclude with certain well known techniques from concentration of suprema of random processes with applications in Reinforcement Learning
    Minimax Weight Learning for Absorbing MDPs. (arXiv:2301.03183v1 [cs.LG])
    Reinforcement learning policy evaluation problems are often modeled as finite or discounted/averaged infinite-horizon MDPs. In this paper, we study undiscounted off-policy policy evaluation for absorbing MDPs. Given the dataset consisting of the i.i.d episodes with a given truncation level, we propose a so-called MWLA algorithm to directly estimate the expected return via the importance ratio of the state-action occupancy measure. The Mean Square Error (MSE) bound for the MWLA method is investigated and the dependence of statistical errors on the data size and the truncation level are analyzed. With an episodic taxi environment, computational experiments illustrate the performance of the MWLA algorithm.
    How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection. (arXiv:2301.02909v1 [cs.LG])
    Anomaly detection attempts at finding examples that deviate from the expected behaviour. Usually, anomaly detection is tackled from an unsupervised perspective because anomalous labels are rare and difficult to acquire. However, the lack of labels makes the anomaly detector have high uncertainty in some regions, which usually results in poor predictive performance or low user trust in the predictions. One can reduce such uncertainty by collecting specific labels using Active Learning (AL), which targets examples close to the detector's decision boundary. Alternatively, one can increase the user trust by allowing the detector to abstain from making highly uncertain predictions, which is called Learning to Reject (LR). One way to do this is by thresholding the detector's uncertainty based on where its performance is low, which requires labels to be evaluated. Although both AL and LR need labels, they work with different types of labels: AL seeks strategic labels, which are evidently biased, while LR requires i.i.d. labels to evaluate the detector's performance and set the rejection threshold. Because one usually has a unique label budget, deciding how to optimally allocate it is challenging. In this paper, we propose a mixed strategy that, given a budget of labels, decides in multiple rounds whether to use the budget to collect AL labels or LR labels. The strategy is based on a reward function that measures the expected gain when allocating the budget to either side. We evaluate our strategy on 18 benchmark datasets and compare it to some baselines.  ( 2 min )
    Active Deep Learning Guided by Efficient Gaussian Process Surrogates. (arXiv:2301.02761v1 [cs.LG])
    The success of active learning relies on the exploration of the underlying data-generating distributions, populating sparsely labeled data areas, and exploitation of the information about the task gained by the baseline (neural network) learners. In this paper, we present a new algorithm that combines these two active learning modes. Our algorithm adopts a Bayesian surrogate for the baseline learner, and it optimizes the exploration process by maximizing the gain of information caused by new labels. Further, by instantly updating the surrogate learner for each new data instance, our model can faithfully simulate and exploit the continuous learning behavior of the learner without having to actually retrain it per label. In experiments with four benchmark classification datasets, our method demonstrated significant performance gain over state-of-the-arts.
    A Characterization of Multilabel Learnability. (arXiv:2301.02729v1 [cs.LG])
    We consider the problem of multilabel classification and investigate learnability in batch and online settings. In both settings, we show that a multilabel function class is learnable if and only if each single-label restriction of the function class is learnable. As extensions, we also study multioutput regression in the batch setting and bandit feedback in the online setting. For the former, we characterize learnability w.r.t. $L_p$ losses. For the latter, we show a similar characterization as in the full-feedback setting.
    Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment. (arXiv:2301.02791v1 [cs.LG])
    Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. %These identified sub-structures can provide interpretations of GNN's behavior. Though various algorithms are proposed, most of them formalize this task by searching the minimal subgraph which can preserve original predictions. However, an inductive bias is deep-rooted in this framework: several subgraphs can result in the same or similar outputs as the original graphs. Consequently, they have the danger of providing spurious explanations and failing to provide consistent explanations. Applying them to explain weakly-performed GNNs would further amplify these issues. To address this problem, we theoretically examine the predictions of GNNs from the causality perspective. Two typical reasons for spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input. Observing that both confounding effects and diverse causal rationales are encoded in internal representations, \tianxiang{we propose a new explanation framework with an auxiliary alignment loss, which is theoretically proven to be optimizing a more faithful explanation objective intrinsically. Concretely for this alignment loss, a set of different perspectives are explored: anchor-based alignment, distributional alignment based on Gaussian mixture models, mutual-information-based alignment, etc. A comprehensive study is conducted both on the effectiveness of this new framework in terms of explanation faithfulness/consistency and on the advantages of these variants.
    Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement. (arXiv:2301.03028v1 [cs.LG])
    Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE. Specifically, a coupled diffusion probabilistic model is proposed to augment the time series data without increasing the aleatoric uncertainty and implement a more tractable inference process with BVAE. To ensure the generated series move toward the true target, we further propose to adapt and integrate the multiscale denoising score matching into the diffusion process for time series forecasting. In addition, to enhance the interpretability and stability of the prediction, we treat the latent variable in a multivariate manner and disentangle them on top of minimizing total correlation. Extensive experiments on synthetic and real-world data show that D3VAE outperforms competitive algorithms with remarkable margins. Our implementation is available at https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE.
    Prognosis and Treatment Prediction of Type-2 Diabetes Using Deep Neural Network and Machine Learning Classifiers. (arXiv:2301.03093v1 [cs.LG])
    Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to imbalanced insulin activity.The motion of this research is a comparative study of seven machine learning classifiers and an artificial neural network method to prognosticate the detection and treatment of diabetes with high accuracy,in order to identify and treat diabetes patients at an early age.Our training and test dataset is an accumulation of 9483 diabetes patients information.The training dataset is large enough to negate overfitting and provide for highly accurate test performance.We use performance measures such as accuracy and precision to find out the best algorithm deep ANN which outperforms with 95.14% accuracy among all other tested machine learning classifiers.We hope our high-performing model can be used by hospitals to predict diabetes and drive research into more accurate prediction models.
    REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service. (arXiv:2301.02905v1 [cs.CR])
    Encoder as a service is an emerging cloud service. Specifically, a service provider first pre-trains an encoder (i.e., a general-purpose feature extractor) via either supervised learning or self-supervised learning and then deploys it as a cloud service API. A client queries the cloud service API to obtain feature vectors for its training/testing inputs when training/testing its classifier (called downstream classifier). A downstream classifier is vulnerable to adversarial examples, which are testing inputs with carefully crafted perturbation that the downstream classifier misclassifies. Therefore, in safety and security critical applications, a client aims to build a robust downstream classifier and certify its robustness guarantees against adversarial examples. What APIs should the cloud service provide, such that a client can use any certification method to certify the robustness of its downstream classifier against adversarial examples while minimizing the number of queries to the APIs? How can a service provider pre-train an encoder such that clients can build more certifiably robust downstream classifiers? We aim to answer the two questions in this work. For the first question, we show that the cloud service only needs to provide two APIs, which we carefully design, to enable a client to certify the robustness of its downstream classifier with a minimal number of queries to the APIs. For the second question, we show that an encoder pre-trained using a spectral-norm regularization term enables clients to build more robust downstream classifiers.
    Transfer learning for non-intrusive load monitoring and appliance identification in a smart home. (arXiv:2301.03018v1 [eess.SP])
    Non-intrusive load monitoring (NILM) or energy disaggregation is an inverse problem whereby the goal is to extract the load profiles of individual appliances, given an aggregate load profile of the mains of a home. NILM could help identify the power usage patterns of individual appliances in a home, and thus, could help realize novel energy conservation schemes for smart homes. In this backdrop, this work proposes a novel deep-learning approach to solve the NILM problem and a few related problems as follows. 1) We build upon the reputed seq2-point convolutional neural network (CNN) model to come up with the proposed seq2-[3]-point CNN model to solve the (home) NILM problem and site-NILM problem (basically, NILM at a smaller scale). 2) We solve the related problem of appliance identification by building upon the state-of-the-art (pre-trained) 2D-CNN models, i.e., AlexNet, ResNet-18, and DenseNet-121, which are trained upon two custom datasets that consist of Wavelets and short-time Fourier transform (STFT)-based 2D electrical signatures of the appliances. 3) Finally, we do some basic qualitative inference about an individual appliance's health by comparing the power consumption of the same appliance across multiple homes. Low-frequency REDD dataset is used to train and test the proposed deep learning models for all problems, except site-NILM where REFIT dataset has been used. As for the results, we achieve a maximum accuracy of 94.6\% for home-NILM, 81\% for site-NILM, and 88.9\% for appliance identification (with Resnet-based model).
    k-Means SubClustering: A Differentially Private Algorithm with Improved Clustering Quality. (arXiv:2301.02896v1 [cs.LG])
    In today's data-driven world, the sensitivity of information has been a significant concern. With this data and additional information on the person's background, one can easily infer an individual's private data. Many differentially private iterative algorithms have been proposed in interactive settings to protect an individual's privacy from these inference attacks. The existing approaches adapt the method to compute differentially private(DP) centroids by iterative Llyod's algorithm and perturbing the centroid with various DP mechanisms. These DP mechanisms do not guarantee convergence of differentially private iterative algorithms and degrade the quality of the cluster. Thus, in this work, we further extend the previous work on 'Differentially Private k-Means Clustering With Convergence Guarantee' by taking it as our baseline. The novelty of our approach is to sub-cluster the clusters and then select the centroid which has a higher probability of moving in the direction of the future centroid. At every Lloyd's step, the centroids are injected with the noise using the exponential DP mechanism. The results of the experiments indicate that our approach outperforms the current state-of-the-art method, i.e., the baseline algorithm, in terms of clustering quality while maintaining the same differential privacy requirements. The clustering quality significantly improved by 4.13 and 2.83 times than baseline for the Wine and Breast_Cancer dataset, respectively.
    Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching. (arXiv:2301.02903v1 [cs.LG])
    Despite surprising performance on zero-shot transfer, pre-training a large-scale multimodal model is often prohibitive as it requires a huge amount of data and computing resources. In this paper, we propose a method (BeamCLIP) that can effectively transfer the representations of a large pre-trained multimodal model (CLIP-ViT) into a small target model (e.g., ResNet-18). For unsupervised transfer, we introduce cross-modal similarity matching (CSM) that enables a student model to learn the representations of a teacher model by matching the relative similarity distribution across text prompt embeddings. To better encode the text prompts, we design context-based prompt augmentation (CPA) that can alleviate the lexical ambiguity of input text prompts. Our experiments show that unsupervised representation transfer of a pre-trained vision-language model enables a small ResNet-18 to achieve a better ImageNet-1K top-1 linear probe accuracy (66.2%) than vision-only self-supervised learning (SSL) methods (e.g., SimCLR: 51.8%, SwAV: 63.7%), while closing the gap with supervised learning (69.8%).
    Traditional Readability Formulas Compared for English. (arXiv:2301.02975v1 [cs.CL])
    Traditional English readability formulas, or equations, were largely developed in the 20th century. Nonetheless, many researchers still rely on them for various NLP applications. Such a phenomenon is presumably due to the convenience and straightforwardness of readability formulas. In this work, we contribute to the NLP community by 1. introducing New English Readability Formula (NERF), 2. recalibrating the coefficients of old readability formulas (Flesch-Kincaid Grade Level, Fog Index, SMOG Index, Coleman-Liau Index, and Automated Readability Index), 3. evaluating the readability formulas, for use in text simplification studies and medical texts, and 4. developing a Python-based program for the wide application to various NLP projects.
    Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation. (arXiv:2301.03011v1 [stat.ML])
    Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point $\tau$, a change occurs at a subset of nodes $C$, which affects the probability distribution of their associated node streams. In this paper, we propose a novel kernel-based method to both detect $\tau$ and localize $C$, based on the direct estimation of the likelihood-ratio between the post-change and the pre-change distributions of the node streams. Our main working hypothesis is the smoothness of the likelihood-ratio estimates over the graph, i.e connected nodes are expected to have similar likelihood-ratios. The quality of the proposed method is demonstrated on extensive experiments on synthetic scenarios.
    Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning. (arXiv:2301.03041v1 [cs.CV])
    Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations of one image based on contrastive learning. By utilizing the consistency of two augmentations, the burden of manual annotations can be freed. Contrastive learning exploits instance-level information to learn robust features. However, the learned information is probably confined to different views of the same instance. In this paper, we attempt to leverage the similarity between two distinct images to boost representation in self-supervised learning. In contrast to instance-level information, the similarity between two distinct images may provide more useful information. Besides, we analyze the relation between similarity loss and feature-level cross-entropy loss. These two losses are essential for most deep learning methods. However, the relation between these two losses is not clear. Similarity loss helps obtain instance-level representation, while feature-level cross-entropy loss helps mine the similarity between two distinct images. We provide theoretical analyses and experiments to show that a suitable combination of these two losses can get state-of-the-art results.
    Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning. (arXiv:2301.03116v1 [cs.LG])
    A general framework of unsupervised learning for combinatorial optimization (CO) is to train a neural network (NN) whose output gives a problem solution by directly optimizing the CO objective. Albeit with some advantages over traditional solvers, the current framework optimizes an averaged performance over the distribution of historical problem instances, which misaligns with the actual goal of CO that looks for a good solution to every future encountered instance. With this observation, we propose a new objective of unsupervised learning for CO where the goal of learning is to search for good initialization for future problem instances rather than give direct solutions. We propose a meta-learning-based training pipeline for this new objective. Our method achieves go empirical performance. We observe that even just the initial solution given by our model before fine-tuning can significantly outperform the baselines under various evaluation settings including evaluation across multiple datasets, and the case with big shifts in the problem scale. The reason we conjecture is that meta-learning-based training lets the model loosely tied to each local optima for a training instance while being more adaptive to the changes of optimization landscapes across instances.
    Explaining Graph Neural Networks via Non-parametric Subgraph Matching. (arXiv:2301.02780v1 [cs.LG])
    The great success in graph neural networks (GNNs) provokes the question about explainability: Which fraction of the input graph is the most determinant of the prediction? Particularly, parametric explainers prevail in existing approaches because of their stronger capability to decipher the black-box (i.e., the target GNN). In this paper, based on the observation that graphs typically share some joint motif patterns, we propose a novel non-parametric subgraph matching framework, dubbed MatchExplainer, to explore explanatory subgraphs. It couples the target graph with other counterpart instances and identifies the most crucial joint substructure by minimizing the node corresponding-based distance. Moreover, we note that present graph sampling or node-dropping methods usually suffer from the false positive sampling problem. To ameliorate that issue, we design a new augmentation paradigm named MatchDrop. It takes advantage of MatchExplainer to fix the most informative portion of the graph and merely operates graph augmentations on the rest less informative part. We conduct extensive experiments on both synthetic and real-world datasets and show the effectiveness of our MatchExplainer by outperforming all parametric baselines with significant margins. Additional results also demonstrate that our MatchDrop is a general scheme to be equipped with GNNs for enhanced performance.
    The 3D Structural Phenotype of the Glaucomatous Optic Nerve Head and its Relationship with The Severity of Visual Field Damage. (arXiv:2301.02837v1 [cs.LG])
    $\bf{Purpose}$: To describe the 3D structural changes in both connective and neural tissues of the optic nerve head (ONH) that occur concurrently at different stages of glaucoma using traditional and AI-driven approaches. $\bf{Methods}$: We included 213 normal, 204 mild glaucoma (mean deviation [MD] $\ge$ -6.00 dB), 118 moderate glaucoma (MD of -6.01 to -12.00 dB), and 118 advanced glaucoma patients (MD < -12.00 dB). All subjects had their ONHs imaged in 3D with Spectralis optical coherence tomography. To describe the 3D structural phenotype of glaucoma as a function of severity, we used two different approaches: (1) We extracted human-defined 3D structural parameters of the ONH including retinal nerve fiber layer (RNFL) thickness, lamina cribrosa (LC) shape and depth at different stages of glaucoma; (2) we also employed a geometric deep learning method (i.e. PointNet) to identify the most important 3D structural features that differentiate ONHs from different glaucoma severity groups without any human input. $\bf{Results}$: We observed that the majority of ONH structural changes occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using PointNet, we also found that 3D ONH structural changes were present in both neural and connective tissues. In both approaches, we observed that structural changes were more prominent in the superior and inferior quadrant of the ONH, particularly in the RNFL, the prelamina, and the LC. As the severity of glaucoma increased, these changes became more diffuse (i.e. widespread), particularly in the LC. $\bf{Conclusions}$: In this study, we were able to uncover complex 3D structural changes of the ONH in both neural and connective tissues as a function of glaucoma severity. We hope to provide new insights into the complex pathophysiology of glaucoma that might help clinicians in their daily clinical care.
    Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings. (arXiv:2301.02924v1 [cs.LG])
    Graph Neural Networks (GNNs) have achieved a lot of success with graph-structured data. However, it is observed that the performance of GNNs does not improve (or even worsen) as the number of layers increases. This effect has known as over-smoothing, which means that the representations of the graph nodes of different classes would become indistinguishable when stacking multiple layers. In this work, we propose a new simple, and efficient method to alleviate the effect of the over-smoothing problem in GNNs by explicitly using relations between node embeddings. Experiments on real-world datasets demonstrate that utilizing node embedding relations makes GNN models such as Graph Attention Network more robust to over-smoothing and achieves better performance with deeper GNNs. Our method can be used in combination with other methods to give the best performance. GNN applications are endless and depend on the user's objective and the type of data that they possess. Solving over-smoothing issues can potentially improve the performance of models on all these tasks.
    AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network. (arXiv:2301.03049v1 [cs.LG])
    Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural networks (GNNs) have received more and more attention. However, the existing work mainly focuses on the design of novel GNN models, while ignoring another important issue that also has a large impact on the model performance, namely the missing attributes of some node types. The handcrafted attribute completion requires huge expert experience and domain knowledge. Also, considering the differences in semantic characteristics between nodes, the attribute completion should be fine-grained, i.e., the attribute completion operation should be node-specific. Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes. To address the above challenges, we propose a differentiable attribute completion framework called AutoAC for automated completion operation search in heterogeneous GNNs. We first propose an expressive completion operation search space, including topology-dependent and topology-independent completion operations. Then, we propose a continuous relaxation schema and further propose a differentiable completion algorithm where the completion operation search is formulated as a bi-level joint optimization problem. To improve the search efficiency, we leverage two optimization techniques: discrete constraints and auxiliary unsupervised graph node clustering. Extensive experimental results on real-world datasets reveal that AutoAC outperforms the SOTA handcrafted heterogeneous GNNs and the existing attribute completion method
    Subset verification and search algorithms for causal DAGs. (arXiv:2301.03180v1 [cs.LG])
    Learning causal relationships between variables is a fundamental task in causal inference and directed acyclic graphs (DAGs) are a popular choice to represent the causal relationships. As one can recover a causal graph only up to its Markov equivalence class from observations, interventions are often used for the recovery task. Interventions are costly in general and it is important to design algorithms that minimize the number of interventions performed. In this work, we study the problem of learning the causal relationships of a subset of edges (target edges) in a graph with as few interventions as possible. Under the assumptions of faithfulness, causal sufficiency, and ideal interventions, we study this problem in two settings: when the underlying ground truth causal graph is known (subset verification) and when it is unknown (subset search). For the subset verification problem, we provide an efficient algorithm to compute a minimum sized interventional set; we further extend these results to bounded size non-atomic interventions and node-dependent interventional costs. For the subset search problem, in the worst case, we show that no algorithm (even with adaptivity or randomization) can achieve an approximation ratio that is asymptotically better than the vertex cover of the target edges when compared with the subset verification number. This result is surprising as there exists a logarithmic approximation algorithm for the search problem when we wish to recover the whole causal graph. To obtain our results, we prove several interesting structural properties of interventional causal graphs that we believe have applications beyond the subset verification/search problems studied here.
    Advanced Data Augmentation Approaches: A Comprehensive Survey and Future directions. (arXiv:2301.02830v1 [cs.CV])
    Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on advanced data augmentation techniques. we provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation. For results reproducibility, we compiled available codes of all data augmentation techniques. Finally, we discuss the challenges and difficulties, and possible future direction for the research community. We believe, this survey provides several benefits i) readers will understand the data augmentation working mechanism to fix overfitting problems ii) results will save the searching time of the researcher for comparison purposes. iii) Codes of the mentioned data augmentation techniques are available at https://github.com/kmr2017/Advanced-Data-augmentation-codes iv) Future work will spark interest in research community.
    SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks. (arXiv:2301.03512v1 [cs.CV])
    Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders. The heterogeneous graphs, whose structures are defined by an ontology, consist of different nodes with type-specific node features and different relations with type-specific edge features. In order to exploit all the information given by these graphs, we propose to use cascaded layers of graph convolution. The result is an encoding of the scene. Task-specific decoders can be applied to predict desired attributes of the scene. Extensive evaluation on two diverse binary node classification tasks show the main strength of this methodology: despite being generic, it even manages to outperform task-specific baselines. The further application of our methodology to the task of node classification in various knowledge graphs shows its transferability to other domains.
    Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning. (arXiv:2301.03532v1 [cs.CR])
    Through the generalization of deep learning, the research community has addressed critical challenges in the network security domain, like malware identification and anomaly detection. However, they have yet to discuss deploying them on Internet of Things (IoT) devices for day-to-day operations. IoT devices are often limited in memory and processing power, rendering the compute-intensive deep learning environment unusable. This research proposes a way to overcome this barrier by bypassing feature engineering in the deep learning pipeline and using raw packet data as input. We introduce a feature engineering-less machine learning (ML) process to perform malware detection on IoT devices. Our proposed model, "Feature engineering-less-ML (FEL-ML)," is a lighter-weight detection algorithm that expends no extra computations on "engineered" features. It effectively accelerates the low-powered IoT edge. It is trained on unprocessed byte-streams of packets. Aside from providing better results, it is quicker than traditional feature-based methods. FEL-ML facilitates resource-sensitive network traffic security with the added benefit of eliminating the significant investment by subject matter experts in feature engineering.
    Active manifolds, stratifications, and convergence to local minima in nonsmooth optimization. (arXiv:2108.11832v2 [math.OC] UPDATED)
    We show that the subgradient method converges only to local minimizers when applied to generic Lipschitz continuous and subdifferentially regular functions that are definable in an o-minimal structure. At a high level, the argument we present is appealingly transparent: we interpret the nonsmooth dynamics as an approximate Riemannian gradient method on a certain distinguished submanifold that captures the nonsmooth activity of the function. In the process, we develop new regularity conditions in nonsmooth analysis that parallel the stratification conditions of Whitney, Kuo, and Verdier and extend stochastic processes techniques of Pemantle.
    Grokking modular arithmetic. (arXiv:2301.02679v1 [cs.LG])
    We present a simple neural network that can learn modular arithmetic tasks and exhibits a sudden jump in generalization known as ``grokking''. Concretely, we present (i) fully-connected two-layer networks that exhibit grokking on various modular arithmetic tasks under vanilla gradient descent with the MSE loss function in the absence of any regularization; (ii) evidence that grokking modular arithmetic corresponds to learning specific feature maps whose structure is determined by the task; (iii) analytic expressions for the weights -- and thus for the feature maps -- that solve a large class of modular arithmetic tasks; and (iv) evidence that these feature maps are also found by vanilla gradient descent as well as AdamW, thereby establishing complete interpretability of the representations learnt by the network.
    Few-shot Node Classification with Extremely Weak Supervision. (arXiv:2301.02708v1 [cs.LG])
    Few-shot node classification aims at classifying nodes with limited labeled nodes as references. Recent few-shot node classification methods typically learn from classes with abundant labeled nodes (i.e., meta-training classes) and then generalize to classes with limited labeled nodes (i.e., meta-test classes). Nevertheless, on real-world graphs, it is usually difficult to obtain abundant labeled nodes for many classes. In practice, each meta-training class can only consist of several labeled nodes, known as the extremely weak supervision problem. In few-shot node classification, with extremely limited labeled nodes for meta-training, the generalization gap between meta-training and meta-test will become larger and thus lead to suboptimal performance. To tackle this issue, we study a novel problem of few-shot node classification with extremely weak supervision and propose a principled framework X-FNC under the prevalent meta-learning framework. Specifically, our goal is to accumulate meta-knowledge across different meta-training tasks with extremely weak supervision and generalize such knowledge to meta-test tasks. To address the challenges resulting from extremely scarce labeled nodes, we propose two essential modules to obtain pseudo-labeled nodes as extra references and effectively learn from extremely limited supervision information. We further conduct extensive experiments on four node classification datasets with extremely weak supervision to validate the superiority of our framework compared to the state-of-the-art baselines.
    Perceptual-Neural-Physical Sound Matching. (arXiv:2301.02886v1 [cs.SD])
    Sound matching algorithms seek to approximate a target waveform by parametric audio synthesis. Deep neural networks have achieved promising results in matching sustained harmonic tones. However, the task is more challenging when targets are nonstationary and inharmonic, e.g., percussion. We attribute this problem to the inadequacy of loss function. On one hand, mean square error in the parametric domain, known as "P-loss", is simple and fast but fails to accommodate the differing perceptual significance of each parameter. On the other hand, mean square error in the spectrotemporal domain, known as "spectral loss", is perceptually motivated and serves in differentiable digital signal processing (DDSP). Yet, spectral loss has more local minima than P-loss and its gradient may be computationally expensive; hence a slow convergence. Against this conundrum, we present Perceptual-Neural-Physical loss (PNP). PNP is the optimal quadratic approximation of spectral loss while being as fast as P-loss during training. We instantiate PNP with physical modeling synthesis as decoder and joint time-frequency scattering transform (JTFS) as spectral representation. We demonstrate its potential on matching synthetic drum sounds in comparison with other loss functions.
    On Consistency and Asymptotic Normality of Least Absolute Deviation Estimators for 2-dimensional Sinusoidal Model. (arXiv:2301.03229v1 [math.ST])
    Estimation of the parameters of a 2-dimensional sinusoidal model is a fundamental problem in digital signal processing. In this paper, we propose a robust least absolute deviation (LAD) estimators for parameter estimation. The proposed methodology provides a robust alternative to non-robust estimation techniques like the least squares estimators, in situations where outliers are present in the data or in the presence of heavy tailed noise. We study important asymptotic properties of the LAD estimators and establish the strong consistency and asymptotic normality of the LAD estimators. We further illustrate the advantage of using LAD estimators over least squares estimators through extensive simulation studies.
    AI Maintenance: A Robustness Perspective. (arXiv:2301.03052v1 [cs.LG])
    With the advancements in machine learning (ML) methods and compute resources, artificial intelligence (AI) empowered systems are becoming a prevailing technology. However, current AI technology such as deep learning is not flawless. The significantly increased model complexity and data scale incur intensified challenges when lacking trustworthiness and transparency, which could create new risks and negative impacts. In this paper, we carve out AI maintenance from the robustness perspective. We start by introducing some highlighted robustness challenges in the AI lifecycle and motivating AI maintenance by making analogies to car maintenance. We then propose an AI model inspection framework to detect and mitigate robustness risks. We also draw inspiration from vehicle autonomy to define the levels of AI robustness automation. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.
    Why do Nearest Neighbor Language Models Work?. (arXiv:2301.02828v1 [cs.CL])
    Language models (LMs) compute the probability of a text by sequentially computing a representation of an already-seen context and using this representation to predict the next word. Currently, most LMs calculate these representations through a neural network consuming the immediate previous context. However recently, retrieval-augmented LMs have shown to improve over standard neural LMs, by accessing information retrieved from a large datastore, in addition to their standard, parametric, next-word prediction. In this paper, we set out to understand why retrieval-augmented language models, and specifically why k-nearest neighbor language models (kNN-LMs) perform better than standard parametric LMs, even when the k-nearest neighbor component retrieves examples from the same training set that the LM was originally trained on. To this end, we perform a careful analysis of the various dimensions over which kNN-LM diverges from standard LMs, and investigate these dimensions one by one. Empirically, we identify three main reasons why kNN-LM performs better than standard LMs: using a different input representation for predicting the next tokens, approximate kNN search, and the importance of softmax temperature for the kNN distribution. Further, we incorporate these insights into the model architecture or the training procedure of the standard parametric LM, improving its results without the need for an explicit retrieval component. The code is available at https://github.com/frankxu2004/knnlm-why.
    Isotonic Recalibration under a Low Signal-to-Noise Ratio. (arXiv:2301.02692v1 [stat.ME])
    Insurance pricing systems should fulfill the auto-calibration property to ensure that there is no systematic cross-financing between different price cohorts. Often, regression models are not auto-calibrated. We propose to apply isotonic recalibration to a given regression model to ensure auto-calibration. Our main result proves that under a low signal-to-noise ratio, this isotonic recalibration step leads to explainable pricing systems because the resulting isotonically recalibrated regression functions have a low complexity.
    BQ-NCO: Bisimulation Quotienting for Generalizable Neural Combinatorial Optimization. (arXiv:2301.03313v1 [cs.LG])
    Despite the success of Neural Combinatorial Optimization methods for end-to-end heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we present a novel formulation of combinatorial optimization (CO) problems as Markov Decision Processes (MDPs) that effectively leverages symmetries of the CO problems to improve out-of-distribution robustness. Starting from the standard MDP formulation of constructive heuristics, we introduce a generic transformation based on bisimulation quotienting (BQ) in MDPs. This transformation allows to reduce the state space by accounting for the intrinsic symmetries of the CO problem and facilitates the MDP solving. We illustrate our approach on the Traveling Salesman, Capacitated Vehicle Routing and Knapsack Problems. We present a BQ reformulation of these problems and introduce a simple attention-based policy network that we train by imitation of (near) optimal solutions for small instances from a single distribution. We obtain new state-of-the-art generalization results for instances with up to 1000 nodes from synthetic and realistic benchmarks that vary both in size and node distributions.
    Modeling Scattering Coefficients in Antenna Design using Self-Attentive Complex Polynomials with Image-based Representation. (arXiv:2301.02747v1 [cs.LG])
    Finding antenna designs that satisfy frequency requirements and are also optimal with respect to multiple physical criteria is a critical component in designing next generation hardware. However, such a process is non-trivial because the objective function is typically highly nonlinear and sensitive to subtle design change. Moreover, the objective to be optimized often involves electromagnetic (EM) simulations, which is slow and expensive with commercial simulation software. In this work, we propose a sample-efficient and accurate surrogate model, named CZP (Constant Zeros Poles), to directly estimate the scattering coefficients in the frequency domain of a given 2D planar antenna design, without using a simulator. CZP achieves this by predicting the complex zeros and poles for the frequency response of scattering coefficients, which we have theoretically justified for any linear PDE, including Maxwell's equations. Moreover, instead of using low-dimensional representations, CZP leverages a novel image-based representation for antenna topology inspired by the existing mesh-based EM simulation techniques, and attention-based neural network architectures. We demonstrate experimentally that CZP not only outperforms baselines in terms of test loss, but also is able to find 2D antenna designs verifiable by commercial software with only 40k training samples, when coupling with advanced sequential search techniques like reinforcement learning.
    Principal Component Analysis in Space Forms. (arXiv:2301.02750v1 [stat.ML])
    Principal component analysis (PCA) is a workhorse of modern data science. Practitioners typically perform PCA assuming the data conforms to Euclidean geometry. However, for specific data types, such as hierarchical data, other geometrical spaces may be more appropriate. We study PCA in space forms; that is, those with constant positive (spherical) and negative (hyperbolic) curvatures, in addition to zero-curvature (Euclidean) spaces. At any point on a Riemannian manifold, one can define a Riemannian affine subspace based on a set of tangent vectors and use invertible maps to project tangent vectors to the manifold and vice versa. Finding a low-dimensional Riemannian affine subspace for a set of points in a space form amounts to dimensionality reduction because, as we show, any such affine subspace is isometric to a space form of the same dimension and curvature. To find principal components, we seek a (Riemannian) affine subspace that best represents a set of manifold-valued data points with the minimum average cost of projecting data points onto the affine subspace. We propose specific cost functions that bring about two major benefits: (1) the affine subspace can be estimated by solving an eigenequation -- similar to that of Euclidean PCA, and (2) optimal affine subspaces of different dimensions form a nested set. These properties provide advances over existing methods which are mostly iterative algorithms with slow convergence and weaker theoretical guarantees. Specifically for hyperbolic PCA, the associated eigenequation operates in the Lorentzian space, endowed with an indefinite inner product; we thus establish a connection between Lorentzian and Euclidean eigenequations. We evaluate the proposed space form PCA on data sets simulated in spherical and hyperbolic spaces and show that it outperforms alternative methods in convergence speed or accuracy, often both.
    Visual Story Generation Based on Emotion and Keywords. (arXiv:2301.02777v1 [cs.AI])
    Automated visual story generation aims to produce stories with corresponding illustrations that exhibit coherence, progression, and adherence to characters' emotional development. This work proposes a story generation pipeline to co-create visual stories with the users. The pipeline allows the user to control events and emotions on the generated content. The pipeline includes two parts: narrative and image generation. For narrative generation, the system generates the next sentence using user-specified keywords and emotion labels. For image generation, diffusion models are used to create a visually appealing image corresponding to each generated sentence. Further, object recognition is applied to the generated images to allow objects in these images to be mentioned in future story development.
    Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space. (arXiv:2301.02665v1 [cs.LG])
    Discovery of the molecular candidates for applications in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries, necessitates development of machine learning algorithms capable of rapid exploration of the chemical spaces targeting the desired functionalities. Here we introduce a novel approach for the active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data and introduce them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods such as SISSO and active learning into a single framework. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.
    Multimodal Lyrics-Rhythm Matching. (arXiv:2301.02732v1 [cs.SD])
    Despite the recent increase in research on artificial intelligence for music, prominent correlations between key components of lyrics and rhythm such as keywords, stressed syllables, and strong beats are not frequently studied. Ths is likely due to challenges such as audio misalignment, inaccuracies in syllabic identification, and most importantly, the need for cross-disciplinary knowledge. To address this lack of research, we propose a novel multimodal lyrics-rhythm matching approach in this paper that specifically matches key components of lyrics and music with each other without any language limitations. We use audio instead of sheet music with readily available metadata, which creates more challenges yet increases the application flexibility of our method. Furthermore, our approach creatively generates several patterns involving various multimodalities, including music strong beats, lyrical syllables, auditory changes in a singer's pronunciation, and especially lyrical keywords, which are utilized for matching key lyrical elements with key rhythmic elements. This advantageous approach not only provides a unique way to study auditory lyrics-rhythm correlations including efficient rhythm-based audio alignment algorithms, but also bridges computational linguistics with music as well as music cognition. Our experimental results reveal an 0.81 probability of matching on average, and around 30% of the songs have a probability of 0.9 or higher of keywords landing on strong beats, including 12% of the songs with a perfect landing. Also, the similarity metrics are used to evaluate the correlation between lyrics and rhythm. It shows that nearly 50% of the songs have 0.70 similarity or higher. In conclusion, our approach contributes significantly to the lyrics-rhythm relationship by computationally unveiling insightful correlations.
  • Open

    A Characterization of Multilabel Learnability. (arXiv:2301.02729v1 [cs.LG])
    We consider the problem of multilabel classification and investigate learnability in batch and online settings. In both settings, we show that a multilabel function class is learnable if and only if each single-label restriction of the function class is learnable. As extensions, we also study multioutput regression in the batch setting and bandit feedback in the online setting. For the former, we characterize learnability w.r.t. $L_p$ losses. For the latter, we show a similar characterization as in the full-feedback setting.
    Upward lightning at wind turbines: Risk assessment from larger-scale meteorology. (arXiv:2301.03360v1 [stat.ML])
    Upward lightning (UL) has become an increasingly important threat to wind turbines as ever more of them are being installed for renewably producing electricity. The taller the wind turbine the higher the risk that the type of lightning striking the man-made structure is UL. UL can be much more destructive than downward lightning due to its long lasting initial continuous current leading to a large charge transfer within the lightning discharge process. Current standards for the risk assessment of lightning at wind turbines mainly take the summer lightning activity into account, which is inferred from LLS. Ground truth lightning current measurements reveal that less than 50% of UL might be detected by lightning location systems (LLS). This leads to a large underestimation of the proportion of LLS-non-detectable UL at wind turbines, which is the dominant lightning type in the cold season. This study aims to assess the risk of LLS-detectable and LLS-non-detectable UL at wind turbines using direct UL measurements at the Gaisberg Tower (Austria) and S\"antis Tower (Switzerland). Direct UL observations are linked to meteorological reanalysis data and joined by random forests, a powerful machine learning technique. The meteorological drivers for the non-/occurrence of LLS-detectable and LLS-non-detectable UL, respectively, are found from the random forest models trained at the towers and have large predictive skill on independent data. In a second step the results from the tower-trained models are extended to a larger study domain (Central and Northern Germany). The tower-trained models for LLS-detectable lightning is independently verified at wind turbine locations in that domain and found to reliably diagnose that type of UL. Risk maps based on case study events show that high diagnosed probabilities in the study domain coincide with actual UL events.
    Generalized Kernel Regularized Least Squares. (arXiv:2209.14355v2 [stat.ML] UPDATED)
    Kernel Regularized Least Squares (KRLS) is a popular method for flexibly estimating models that may have complex relationships between variables. However, its usefulness to many researchers is limited for two reasons. First, existing approaches are inflexible and do not allow KRLS to be combined with theoretically-motivated extensions such as random effects, unregularized fixed effects, or non-Gaussian outcomes. Second, estimation is extremely computationally intensive for even modestly sized datasets. Our paper addresses both concerns by introducing generalized KRLS (gKRLS). We note that KRLS can be re-formulated as a hierarchical model thereby allowing easy inference and modular model construction where KRLS can be used alongside random effects, splines, and unregularized fixed effects. Computationally, we also implement random sketching to dramatically accelerate estimation while incurring a limited penalty in estimation quality. We demonstrate that gKRLS can be fit on datasets with tens of thousands of observations in under one minute. Further, state-of-the-art techniques that require fitting the model over a dozen times (e.g. meta-learners) can be estimated quickly.
    Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation. (arXiv:2301.03125v1 [stat.ML])
    The stochastic proximal point (SPP) methods have gained recent attention for stochastic optimization, with strong convergence guarantees and superior robustness to the classic stochastic gradient descent (SGD) methods showcased at little to no cost of computational overhead added. In this article, we study a minibatch variant of SPP, namely M-SPP, for solving convex composite risk minimization problems. The core contribution is a set of novel excess risk bounds of M-SPP derived through the lens of algorithmic stability theory. Particularly under smoothness and quadratic growth conditions, we show that M-SPP with minibatch-size $n$ and iteration count $T$ enjoys an in-expectation fast rate of convergence consisting of an $\mathcal{O}\left(\frac{1}{T^2}\right)$ bias decaying term and an $\mathcal{O}\left(\frac{1}{nT}\right)$ variance decaying term. In the small-$n$-large-$T$ setting, this result substantially improves the best known results of SPP-type approaches by revealing the impact of noise level of model on convergence rate. In the complementary small-$T$-large-$n$ regime, we provide a two-phase extension of M-SPP to achieve comparable convergence rates. Moreover, we derive a near-tight high probability (over the randomness of data) bound on the parameter estimation error of a sampling-without-replacement variant of M-SPP. Numerical evidences are provided to support our theoretical predictions when substantialized to Lasso and logistic regression models.
    Beyond calibration: estimating the grouping loss of modern neural networks. (arXiv:2210.16315v2 [cs.LG] UPDATED)
    The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.e., the over or under confidence of model scores. Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores that are far from the true posterior probabilities. This is due to the grouping loss, created by samples with the same confidence scores but different true posterior probabilities. Proper scoring rule theory shows that given the calibration loss, the missing piece to characterize individual errors is the grouping loss. While there are many estimators of the calibration loss, none exists for the grouping loss in standard settings. Here, we propose an estimator to approximate the grouping loss. We show that modern neural network architectures in vision and NLP exhibit grouping loss, notably in distribution shifts settings, which highlights the importance of pre-production validation.  ( 2 min )
    Principal Component Analysis in Space Forms. (arXiv:2301.02750v1 [stat.ML])
    Principal component analysis (PCA) is a workhorse of modern data science. Practitioners typically perform PCA assuming the data conforms to Euclidean geometry. However, for specific data types, such as hierarchical data, other geometrical spaces may be more appropriate. We study PCA in space forms; that is, those with constant positive (spherical) and negative (hyperbolic) curvatures, in addition to zero-curvature (Euclidean) spaces. At any point on a Riemannian manifold, one can define a Riemannian affine subspace based on a set of tangent vectors and use invertible maps to project tangent vectors to the manifold and vice versa. Finding a low-dimensional Riemannian affine subspace for a set of points in a space form amounts to dimensionality reduction because, as we show, any such affine subspace is isometric to a space form of the same dimension and curvature. To find principal components, we seek a (Riemannian) affine subspace that best represents a set of manifold-valued data points with the minimum average cost of projecting data points onto the affine subspace. We propose specific cost functions that bring about two major benefits: (1) the affine subspace can be estimated by solving an eigenequation -- similar to that of Euclidean PCA, and (2) optimal affine subspaces of different dimensions form a nested set. These properties provide advances over existing methods which are mostly iterative algorithms with slow convergence and weaker theoretical guarantees. Specifically for hyperbolic PCA, the associated eigenequation operates in the Lorentzian space, endowed with an indefinite inner product; we thus establish a connection between Lorentzian and Euclidean eigenequations. We evaluate the proposed space form PCA on data sets simulated in spherical and hyperbolic spaces and show that it outperforms alternative methods in convergence speed or accuracy, often both.  ( 2 min )
    Isotonic Recalibration under a Low Signal-to-Noise Ratio. (arXiv:2301.02692v1 [stat.ME])
    Insurance pricing systems should fulfill the auto-calibration property to ensure that there is no systematic cross-financing between different price cohorts. Often, regression models are not auto-calibrated. We propose to apply isotonic recalibration to a given regression model to ensure auto-calibration. Our main result proves that under a low signal-to-noise ratio, this isotonic recalibration step leads to explainable pricing systems because the resulting isotonically recalibrated regression functions have a low complexity.  ( 2 min )
    Improved Training of Physics-Informed Neural Networks with Model Ensembles. (arXiv:2204.05108v2 [cs.LG] UPDATED)
    Learning the solution of partial differential equations (PDEs) with a neural network (known in the literature as a physics-informed neural network, PINN) is an attractive alternative to traditional solvers due to its elegancy, greater flexibility and the ease of incorporating observed data. However, training PINNs is notoriously difficult in practice. One problem is the existence of multiple simple (but wrong) solutions which are attractive for PINNs when the solution interval is too large. In this paper, we propose to expand the solution interval gradually to make the PINN converge to the correct solution. To find a good schedule for the solution interval expansion, we train an ensemble of PINNs. The idea is that all ensemble members converge to the same solution in the vicinity of observed data (e.g., initial conditions) while they may be pulled towards different wrong solutions farther away from the observations. Therefore, we use the ensemble agreement as the criterion for including new points for computing the loss derived from PDEs. We show experimentally that the proposed method can improve the accuracy of the found solution.  ( 2 min )
    Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification. (arXiv:2205.13573v3 [cs.LG] UPDATED)
    As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs. However, its application in practice is limited due to the high computational complexity. To overcome this challenge, we propose a novel importance sparsification method, called \textsc{Spar-GW}, to approximate GW distance efficiently. In particular, instead of considering a dense coupling matrix, our method leverages a simple but effective sampling strategy to construct a sparse coupling matrix and update it with few computations. The proposed \textsc{Spar-GW} method is applicable to the GW distance with arbitrary ground cost, and it reduces the complexity from $O(n^4)$ to $O(n^{2+\delta})$ for an arbitrary small $\delta>0$. Theoretically, the convergence and consistency of the proposed estimation for GW distance are established under mild regularity conditions. In addition, this method can be extended to approximate the variants of GW distance, including the entropic GW distance, the fused GW distance, and the unbalanced GW distance. Experiments show the superiority of our \textsc{Spar-GW} to state-of-the-art methods in both synthetic and real-world tasks.  ( 2 min )
    Making Decisions under Outcome Performativity. (arXiv:2210.01745v2 [cs.LG] UPDATED)
    Decision-makers often act in response to data-driven predictions, with the goal of achieving favorable outcomes. In such settings, predictions don't passively forecast the future; instead, predictions actively shape the distribution of outcomes they are meant to predict. This performative prediction setting raises new challenges for learning "optimal" decision rules. In particular, existing solution concepts do not address the apparent tension between the goals of forecasting outcomes accurately and steering individuals to achieve desirable outcomes. To contend with this concern, we introduce a new optimality concept -- performative omniprediction -- adapted from the supervised (non-performative) learning setting. A performative omnipredictor is a single predictor that simultaneously encodes the optimal decision rule with respect to many possibly-competing objectives. Our main result demonstrates that efficient performative omnipredictors exist, under a natural restriction of performative prediction, which we call outcome performativity. On a technical level, our results follow by carefully generalizing the notion of outcome indistinguishability to the outcome performative setting. From an appropriate notion of Performative OI, we recover many consequences known to hold in the supervised setting, such as omniprediction and universal adaptability.  ( 2 min )
    EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living. (arXiv:2301.03325v1 [eess.SP])
    In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using four state-ofthe-art machine learning classifiers, including Random Forest (RF), Fine K-Nearest Neighbour (KNN), Ensemble KNN (sKNN) and Support Vector Machine (SVM) with seven combinations of time domain and frequency domain feature sets. The state-of-theart classification accuracy on five FAABOS categories is 83:21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75:39% by the same SVM classifier with the log moments in frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBP), channel cross correlation and local binary patterns (LBP) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics.  ( 2 min )
    A Newton-CG based augmented Lagrangian method for finding a second-order stationary point of nonconvex equality constrained optimization with complexity guarantees. (arXiv:2301.03139v1 [math.OC])
    In this paper we consider finding a second-order stationary point (SOSP) of nonconvex equality constrained optimization when a nearly feasible point is known. In particular, we first propose a new Newton-CG method for finding an approximate SOSP of unconstrained optimization and show that it enjoys a substantially better complexity than the Newton-CG method [56]. We then propose a Newton-CG based augmented Lagrangian (AL) method for finding an approximate SOSP of nonconvex equality constrained optimization, in which the proposed Newton-CG method is used as a subproblem solver. We show that under a generalized linear independence constraint qualification (GLICQ), our AL method enjoys a total inner iteration complexity of $\widetilde{\cal O}(\epsilon^{-7/2})$ and an operation complexity of $\widetilde{\cal O}(\epsilon^{-7/2}\min\{n,\epsilon^{-3/4}\})$ for finding an $(\epsilon,\sqrt{\epsilon})$-SOSP of nonconvex equality constrained optimization with high probability, which are significantly better than the ones achieved by the proximal AL method [60]. Besides, we show that it has a total inner iteration complexity of $\widetilde{\cal O}(\epsilon^{-11/2})$ and an operation complexity of $\widetilde{\cal O}(\epsilon^{-11/2}\min\{n,\epsilon^{-5/4}\})$ when the GLICQ does not hold. To the best of our knowledge, all the complexity results obtained in this paper are new for finding an approximate SOSP of nonconvex equality constrained optimization with high probability. Preliminary numerical results also demonstrate the superiority of our proposed methods over the ones in [56,60].  ( 2 min )
    Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation. (arXiv:2203.05400v3 [math.ST] UPDATED)
    It is common to model a deterministic response function, such as the output of a computer experiment, as a Gaussian process with a Mat\'ern covariance kernel. The smoothness parameter of a Mat\'ern kernel determines many important properties of the model in the large data limit, including the rate of convergence of the conditional mean to the response function. We prove that the maximum likelihood estimate of the smoothness parameter cannot asymptotically undersmooth the truth when the data are obtained on a fixed bounded subset of $\mathbb{R}^d$. That is, if the data-generating response function has Sobolev smoothness $\nu_0 + d/2$, then the smoothness parameter estimate cannot be asymptotically less than $\nu_0 + d/2$. The lower bound is sharp. Additionally, we show that maximum likelihood estimation finds the "correct" smoothness for a class of compactly supported self-similar functions. We also consider cross-validation and prove an asymptotic lower bound $\nu_0$, which however is unlikely to be sharp. The results are based on approximation theory in Sobolev spaces and some general theorems that restrict the set of values that the parameter estimators can take.  ( 2 min )
    Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference. (arXiv:2207.11597v3 [cs.LG] UPDATED)
    We present a non-asymptotic lower bound on the eigenspectrum of the design matrix generated by any linear bandit algorithm with sub-linear regret when the action set has well-behaved curvature. Specifically, we show that the minimum eigenvalue of the expected design matrix grows as $\Omega(\sqrt{n})$ whenever the expected cumulative regret of the algorithm is $O(\sqrt{n})$, where $n$ is the learning horizon, and the action-space has a constant Hessian around the optimal arm. This shows that such action-spaces force a polynomial lower bound rather than a logarithmic lower bound, as shown by \cite{lattimore2017end}, in discrete (i.e., well-separated) action spaces. Furthermore, while the previous result is shown to hold only in the asymptotic regime (as $n \to \infty$), our result for these "locally rich" action spaces is any-time. Additionally, under a mild technical assumption, we obtain a similar lower bound on the minimum eigen value holding with high probability. We apply our result to two practical scenarios -- \emph{model selection} and \emph{clustering} in linear bandits. For model selection, we show that an epoch-based linear bandit algorithm adapts to the true model complexity at a rate exponential in the number of epochs, by virtue of our novel spectral bound. For clustering, we consider a multi agent framework where we show, by leveraging the spectral result, that no forced exploration is necessary -- the agents can run a linear bandit algorithm and estimate their underlying parameters at once, and hence incur a low regret.  ( 2 min )
    Stochastic Langevin Monte Carlo for (weakly) log-concave posterior distributions. (arXiv:2301.03077v1 [stat.ML])
    In this paper, we investigate a continuous time version of the Stochastic Langevin Monte Carlo method, introduced in [WT11], that incorporates a stochastic sampling step inside the traditional over-damped Langevin diffusion. This method is popular in machine learning for sampling posterior distribution. We will pay specific attention in our work to the computational cost in terms of $n$ (the number of observations that produces the posterior distribution), and $d$ (the dimension of the ambient space where the parameter of interest is living). We derive our analysis in the weakly convex framework, which is parameterized with the help of the Kurdyka-\L ojasiewicz (KL) inequality, that permits to handle a vanishing curvature settings, which is far less restrictive when compared to the simple strongly convex case. We establish that the final horizon of simulation to obtain an $\varepsilon$ approximation (in terms of entropy) is of the order $( d \log(n)^2 )^{(1+r)^2} [\log^2(\varepsilon^{-1}) + n^2 d^{2(1+r)} \log^{4(1+r)}(n) ]$ with a Poissonian subsampling of parameter $\left(n ( d \log^2(n))^{1+r}\right)^{-1}$, where the parameter $r$ is involved in the KL inequality and varies between $0$ (strongly convex case) and $1$ (limiting Laplace situation).  ( 2 min )
    Exploration in Model-based Reinforcement Learning with Randomized Reward. (arXiv:2301.03142v1 [stat.ML])
    Model-based Reinforcement Learning (MBRL) has been widely adapted due to its sample efficiency. However, existing worst-case regret analysis typically requires optimistic planning, which is not realistic in general. In contrast, motivated by the theory, empirical study utilizes ensemble of models, which achieve state-of-the-art performance on various testing environments. Such deviation between theory and empirical study leads us to question whether randomized model ensemble guarantee optimism, and hence the optimal worst-case regret? This paper partially answers such question from the perspective of reward randomization, a scarcely explored direction of exploration with MBRL. We show that under the kernelized linear regulator (KNR) model, reward randomization guarantees a partial optimism, which further yields a near-optimal worst-case regret in terms of the number of interactions. We further extend our theory to generalized function approximation and identified conditions for reward randomization to attain provably efficient exploration. Correspondingly, we propose concrete examples of efficient reward randomization. To the best of our knowledge, our analysis establishes the first worst-case regret analysis on randomized MBRL with function approximation.  ( 2 min )
    Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation. (arXiv:2301.03011v1 [stat.ML])
    Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point $\tau$, a change occurs at a subset of nodes $C$, which affects the probability distribution of their associated node streams. In this paper, we propose a novel kernel-based method to both detect $\tau$ and localize $C$, based on the direct estimation of the likelihood-ratio between the post-change and the pre-change distributions of the node streams. Our main working hypothesis is the smoothness of the likelihood-ratio estimates over the graph, i.e connected nodes are expected to have similar likelihood-ratios. The quality of the proposed method is demonstrated on extensive experiments on synthetic scenarios.  ( 2 min )
    On Consistency and Asymptotic Normality of Least Absolute Deviation Estimators for 2-dimensional Sinusoidal Model. (arXiv:2301.03229v1 [math.ST])
    Estimation of the parameters of a 2-dimensional sinusoidal model is a fundamental problem in digital signal processing. In this paper, we propose a robust least absolute deviation (LAD) estimators for parameter estimation. The proposed methodology provides a robust alternative to non-robust estimation techniques like the least squares estimators, in situations where outliers are present in the data or in the presence of heavy tailed noise. We study important asymptotic properties of the LAD estimators and establish the strong consistency and asymptotic normality of the LAD estimators. We further illustrate the advantage of using LAD estimators over least squares estimators through extensive simulation studies.  ( 2 min )
    The Optimal Input-Independent Baseline for Binary Classification: The Dutch Draw. (arXiv:2301.03318v1 [cs.LG])
    Before any binary classification model is taken into practice, it is important to validate its performance on a proper test set. Without a frame of reference given by a baseline method, it is impossible to determine if a score is `good' or `bad'. The goal of this paper is to examine all baseline methods that are independent of feature values and determine which model is the `best' and why. By identifying which baseline models are optimal, a crucial selection decision in the evaluation process is simplified. We prove that the recently proposed Dutch Draw baseline is the best input-independent classifier (independent of feature values) for all positional-invariant measures (independent of sequence order) assuming that the samples are randomly shuffled. This means that the Dutch Draw baseline is the optimal baseline under these intuitive requirements and should therefore be used in practice.  ( 2 min )
    A Sublinear-Time Quantum Algorithm for Approximating Partition Functions. (arXiv:2207.08643v2 [quant-ph] UPDATED)
    We present a novel quantum algorithm for estimating Gibbs partition functions in sublinear time with respect to the logarithm of the size of the state space. This is the first speed-up of this type to be obtained over the seminal nearly-linear time algorithm of \v{S}tefankovi\v{c}, Vempala and Vigoda [JACM, 2009]. Our result also preserves the quadratic speed-up in precision and spectral gap achieved in previous work by exploiting the properties of quantum Markov chains. As an application, we obtain new polynomial improvements over the best-known algorithms for computing the partition function of the Ising model, counting the number of $k$-colorings, matchings or independent sets of a graph, and estimating the volume of a convex body. Our approach relies on developing new variants of the quantum phase and amplitude estimation algorithms that return nearly unbiased estimates with low variance and without destroying their initial quantum state. We extend these subroutines into a nearly unbiased quantum mean estimator that reduces the variance quadratically faster than the classical empirical mean. No such estimator was known to exist prior to our work. These properties, which are of general interest, lead to better convergence guarantees within the paradigm of simulated annealing for computing partition functions.  ( 2 min )
    Subset verification and search algorithms for causal DAGs. (arXiv:2301.03180v1 [cs.LG])
    Learning causal relationships between variables is a fundamental task in causal inference and directed acyclic graphs (DAGs) are a popular choice to represent the causal relationships. As one can recover a causal graph only up to its Markov equivalence class from observations, interventions are often used for the recovery task. Interventions are costly in general and it is important to design algorithms that minimize the number of interventions performed. In this work, we study the problem of learning the causal relationships of a subset of edges (target edges) in a graph with as few interventions as possible. Under the assumptions of faithfulness, causal sufficiency, and ideal interventions, we study this problem in two settings: when the underlying ground truth causal graph is known (subset verification) and when it is unknown (subset search). For the subset verification problem, we provide an efficient algorithm to compute a minimum sized interventional set; we further extend these results to bounded size non-atomic interventions and node-dependent interventional costs. For the subset search problem, in the worst case, we show that no algorithm (even with adaptivity or randomization) can achieve an approximation ratio that is asymptotically better than the vertex cover of the target edges when compared with the subset verification number. This result is surprising as there exists a logarithmic approximation algorithm for the search problem when we wish to recover the whole causal graph. To obtain our results, we prove several interesting structural properties of interventional causal graphs that we believe have applications beyond the subset verification/search problems studied here.  ( 2 min )
    Batch Bayesian Optimization via Particle Gradient Flows. (arXiv:2209.04722v2 [stat.ML] UPDATED)
    Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a surrogate model for the objective function, quantifying the uncertainty in that surrogate through Bayesian inference. Objective evaluations are sequentially determined by maximising an acquisition function at each step. However, this ancilliary optimisation problem can be highly non-trivial to solve, due to the non-convexity of the acquisition function, particularly in the case of batch Bayesian optimisation, where multiple points are selected in every step. In this work we reformulate batch BO as an optimisation problem over the space of probability measures. We construct a new acquisition function based on multipoint expected improvement which is convex over the space of probability measures. Practical schemes for solving this `inner' optimisation problem arise naturally as gradient flows of this objective function. We demonstrate the efficacy of this new method on different benchmark functions and compare with state-of-the-art batch BO methods.
    Accelerated Randomized Block-Coordinate Algorithms for Co-coercive Equations and Applications. (arXiv:2301.03113v1 [math.OC])
    In this paper, we develop an accelerated randomized block-coordinate algorithm to approximate a solution of a co-coercive equation. Such an equation plays a central role in optimization and related fields and covers many mathematical models as special cases, including convex optimization, convex-concave minimax, and variational inequality problems. Our algorithm relies on a recent Nesterov's accelerated interpretation of the Halpern fixed-point iteration in [48]. We establish that the new algorithm achieves $\mathcal{O}(1/k^2)$-convergence rate on $\mathbb{E}[\Vert Gx^k\Vert^2]$ through the last-iterate, where $G$ is the underlying co-coercive operator, $\mathbb{E}[\cdot]$ is the expectation, and $k$ is the iteration counter. This rate is significantly faster than $\mathcal{O}(1/k)$ rates in standard forward or gradient-based methods from the literature. We also prove $o(1/k^2)$ rates on both $\mathbb{E}[\Vert Gx^k\Vert^2]$ and $\mathbb{E}[\Vert x^{k+1} - x^{k}\Vert^2]$. Next, we apply our method to derive two accelerated randomized block coordinate variants of the forward-backward splitting and Douglas-Rachford splitting schemes, respectively for solving a monotone inclusion involving the sum of two operators. As a byproduct, these variants also have faster convergence rates than their non-accelerated counterparts. Finally, we apply our scheme to a finite-sum monotone inclusion that has various applications in machine learning and statistical learning, including federated learning. As a result, we obtain a novel federated learning-type algorithm with fast and provable convergence rates.  ( 2 min )
    Provably Efficient Model-Free Constrained RL with Linear Function Approximation. (arXiv:2206.11889v3 [cs.LG] UPDATED)
    We study the constrained reinforcement learning problem, in which an agent aims to maximize the expected cumulative reward subject to a constraint on the expected total value of a utility function. In contrast to existing model-based approaches or model-free methods accompanied with a `simulator', we aim to develop the first model-free, simulator-free algorithm that achieves a sublinear regret and a sublinear constraint violation even in large-scale systems. To this end, we consider the episodic constrained Markov decision processes with linear function approximation, where the transition dynamics and the reward function can be represented as a linear function of some known feature mapping. We show that $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ regret and $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ constraint violation bounds can be achieved, where $d$ is the dimension of the feature mapping, $H$ is the length of the episode, and $T$ is the total number of steps. Our bounds are attained without explicitly estimating the unknown transition model or requiring a simulator, and they depend on the state space only through the dimension of the feature mapping. Hence our bounds hold even when the number of states goes to infinity. Our main results are achieved via novel adaptations of the standard LSVI-UCB algorithms. In particular, we first introduce primal-dual optimization into the LSVI-UCB algorithm to balance between regret and constraint violation. More importantly, we replace the standard greedy selection with respect to the state-action function in LSVI-UCB with a soft-max policy. This turns out to be key in establishing uniform concentration for the constrained case via its approximation-smoothness trade-off. We also show that one can achieve an even zero constraint violation while still maintaining the same order with respect to $T$.  ( 3 min )
    Mesoscopic modeling of hidden spiking neurons. (arXiv:2205.13493v2 [q-bio.NC] UPDATED)
    Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstrained problems that are hard to tackle with maximum likelihood estimation. In this work, we use coarse-graining and mean-field approximations to derive a bottom-up, neuronally-grounded latent variable model (neuLVM), where the activity of the unobserved neurons is reduced to a low-dimensional mesoscopic description. In contrast to previous latent variable models, neuLVM can be explicitly mapped to a recurrent, multi-population SNN, giving it a transparent biological interpretation. We show, on synthetic spike trains, that a few observed neurons are sufficient for neuLVM to perform efficient model inversion of large SNNs, in the sense that it can recover connectivity parameters, infer single-trial latent population activity, reproduce ongoing metastable dynamics, and generalize when subjected to perturbations mimicking photo-stimulation.  ( 2 min )
    A Classification of $G$-invariant Shallow Neural Networks. (arXiv:2205.09219v5 [cs.LG] UPDATED)
    When trying to fit a deep neural network (DNN) to a $G$-invariant target function with $G$ a group, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do this, thus raising the problem of ``$G$-invariant neural architecture design'': What is the optimal $G$-invariant architecture for a given problem? Before we can consider the optimization problem itself, we must understand the search space, the architectures in it, and how they relate to one another. In this paper, we take a first step towards this goal; we prove a theorem that gives a classification of all $G$-invariant single-hidden-layer or ``shallow'' neural network ($G$-SNN) architectures with ReLU activation for any finite orthogonal group $G$, and we prove a second theorem that characterizes the inclusion maps or ``network morphisms'' between the architectures that can be leveraged during neural architecture search (NAS). The proof is based on a correspondence of every $G$-SNN to a signed permutation representation of $G$ acting on the hidden neurons; the classification is equivalently given in terms of the first cohomology classes of $G$, thus admitting a topological interpretation. The $G$-SNN architectures corresponding to nontrivial cohomology classes have, to our knowledge, never been explicitly identified in the literature previously. Using a code implementation, we enumerate the $G$-SNN architectures for some example groups $G$ and visualize their structure. Finally, we prove that architectures corresponding to inequivalent cohomology classes coincide in function space only when their weight matrices are zero, and we discuss the implications of this for NAS.  ( 3 min )
    Exponential Family Model-Based Reinforcement Learning via Score Matching. (arXiv:2112.14195v2 [cs.LG] UPDATED)
    We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family distributions with $d$ parameters and the reward is bounded and known. SMRL uses score matching, an unnormalized density estimation technique that enables efficient estimation of the model parameter by ridge regression. Under standard regularity assumptions, SMRL achieves $\tilde O(d\sqrt{H^3T})$ online regret, where $H$ is the length of each episode and $T$ is the total number of interactions (ignoring polynomial dependence on structural scale parameters).  ( 2 min )
    Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data. (arXiv:2112.07602v5 [stat.ME] UPDATED)
    A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance. In this paper, we propose a novel cross-validation-like methodology to address this challenge. The key insight of our procedure is that the noisy (but unbiased) difference-of-means estimate can be used as a ground truth ``label" on a portion of the RCT, to test the performance of an estimator trained on the other portion. We combine this insight with an aggregation scheme, which borrows statistical strength across a large collection of RCTs, to present an end-to-end methodology for judging an estimator's ability to recover the underlying treatment effect as well as produce an optimal treatment "roll out" policy. We evaluate our methodology across 699 RCTs implemented in the Amazon supply chain. In this heavy-tailed setting, our methodology suggests that procedures that aggressively downweight or truncate large values, while introducing bias, lower the variance enough to ensure that the treatment effect is more accurately estimated.  ( 2 min )
    Wasserstein Iterative Networks for Barycenter Estimation. (arXiv:2201.12245v2 [cs.LG] UPDATED)
    Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way. In this paper, we present an algorithm to approximate the Wasserstein-2 barycenters of continuous measures via a generative model. Previous approaches rely on regularization (entropic/quadratic) which introduces bias or on input convex neural networks which are not expressive enough for large-scale tasks. In contrast, our algorithm does not introduce bias and allows using arbitrary neural networks. In addition, based on the celebrity faces dataset, we construct Ave, celeba! dataset which can be used for quantitative evaluation of barycenter algorithms by using standard metrics of generative models such as FID.  ( 2 min )
    Convergence of Stochastic Approximation via Martingale and Converse Lyapunov Methods. (arXiv:2205.01303v3 [stat.ML] UPDATED)
    In this paper, we study the almost sure boundedness and the convergence of the stochastic approximation (SA) algorithm. At present, most available convergence proofs are based on the ODE method, and the almost sure boundedness of the iterations is an assumption and not a conclusion. In Borkar-Meyn (2000), it is shown that if the ODE has only one globally attractive equilibrium, then under additional assumptions, the iterations are bounded almost surely, and the SA algorithm converges to the desired solution. Our objective in the present paper is to provide an alternate proof of the above, based on martingale methods, which are simpler and less technical than those based on the ODE method. As a prelude, we prove a new sufficient condition for the global asymptotic stability of an ODE. Next we prove a "converse" Lyapunov theorem on the existence of a suitable Lyapunov function with a globally bounded Hessian, for a globally exponentially stable system. Both theorems are of independent interest to researchers in stability theory. Then, using these results, we provide sufficient conditions for the almost sure boundedness and the convergence of the SA algorithm. We show through examples that our theory covers some situations that are not covered by currently known results, specifically Borkar-Meyn (2000).  ( 2 min )
    Optimization-based Causal Estimation from Heterogenous Environments. (arXiv:2109.11990v2 [stat.ME] UPDATED)
    This paper presents a new optimization approach to causal estimation. Given data that contains covariates and an outcome, which covariates are causes of the outcome, and what is the strength of the causality? In classical machine learning (ML), the goal of optimization is to maximize predictive accuracy. However, some covariates might exhibit a non-causal association to the outcome. Such spurious associations provide predictive power for classical ML, but they prevent us from causally interpreting the result. This paper proposes CoCo, an optimization algorithm that bridges the gap between pure prediction and causal inference. CoCo leverages the recently-proposed idea of environments, datasets of covariates/response where the causal relationships remain invariant but where the distribution of the covariates changes from environment to environment. Given datasets from multiple environments -- and ones that exhibit sufficient heterogeneity -- CoCo maximizes an objective for which the only solution is the causal solution. We describe the theoretical foundations of this approach and demonstrate its effectiveness on simulated and real datasets. Compared to classical ML and existing methods, CoCo provides more accurate estimates of the causal model.  ( 2 min )
    Computationally Efficient Approximations for Matrix-based Renyi's Entropy. (arXiv:2112.13720v4 [stat.ML] UPDATED)
    The recently developed matrix based Renyi's entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes the new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix $G$ to power $\alpha$(i.e., $tr(G^\alpha)$), with a normal complexity of nearly $O(n^3)$, which severely hampers its practical usage when the number of samples (i.e., $n$) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than $O(n^2)$. To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to $tr(G^\alpha)$ for arbitrary values of $\alpha$ by converting it into matrix-vector multiplications problem. We also establish the connection between the matrix-based Renyi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of $G$ to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties of different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy.  ( 2 min )
    Reinforcement Learning for Joint Optimization of Multiple Rewards. (arXiv:1909.02940v4 [cs.LG] UPDATED)
    Finding optimal policies which maximize long term rewards of Markov Decision Processes requires the use of dynamic programming and backward induction to solve the Bellman optimality equation. However, many real-world problems require optimization of an objective that is non-linear in cumulative rewards for which dynamic programming cannot be applied directly. For example, in a resource allocation problem, one of the objectives is to maximize long-term fairness among the users. We notice that when an agent aim to optimize some function of the sum of rewards is considered, the problem loses its Markov nature. This paper addresses and formalizes the problem of optimizing a non-linear function of the long term average of rewards. We propose model-based and model-free algorithms to learn the policy, where the model-based policy is shown to achieve a regret of $\Tilde{O}\left(LKDS\sqrt{\frac{A}{T}}\right)$ for $K$ objectives combined with a concave $L$-Lipschitz function. Further, using the fairness in cellular base-station scheduling, and queueing system scheduling as examples, the proposed algorithm is shown to significantly outperform the conventional RL approaches.  ( 2 min )
    PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks. (arXiv:2006.07794v2 [cs.LG] UPDATED)
    Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data. A recent class of methods to address this problem uses various ways to construct a new training sample by mixing a pair (or more) of training samples. We propose PatchUp, a hidden state block-level regularization technique for Convolutional Neural Networks (CNNs), that is applied on selected contiguous blocks of feature maps from a random pair of samples. Our approach improves the robustness of CNN models against the manifold intrusion problem that may occur in other state-of-the-art mixing approaches. Moreover, since we are mixing the contiguous block of features in the hidden space, which has more dimensions than the input space, we obtain more diverse samples for training towards different dimensions. Our experiments on CIFAR10/100, SVHN, Tiny-ImageNet, and ImageNet using ResNet architectures including PreActResnet18/34, WRN-28-10, ResNet101/152 models show that PatchUp improves upon, or equals, the performance of current state-of-the-art regularizers for CNNs. We also show that PatchUp can provide a better generalization to deformed samples and is more robust against adversarial attacks.  ( 2 min )
    Differentially private inference via noisy optimization. (arXiv:2103.11003v3 [math.ST] UPDATED)
    We propose a general optimization-based framework for computing differentially private M-estimators and a new method for constructing differentially private confidence regions. Firstly, we show that robust statistics can be used in conjunction with noisy gradient descent or noisy Newton methods in order to obtain optimal private estimators with global linear or quadratic convergence, respectively. We establish local and global convergence guarantees, under both local strong convexity and self-concordance, showing that our private estimators converge with high probability to a nearly optimal neighborhood of the non-private M-estimators. Secondly, we tackle the problem of parametric inference by constructing differentially private estimators of the asymptotic variance of our private M-estimators. This naturally leads to approximate pivotal statistics for constructing confidence regions and conducting hypothesis testing. We demonstrate the effectiveness of a bias correction that leads to enhanced small-sample empirical performance in simulations. We illustrate the benefits of our methods in several numerical examples.  ( 2 min )
    Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. (arXiv:2301.03566v1 [math.ST])
    We study simple binary hypothesis testing under both local differential privacy (LDP) and communication constraints. We qualify our results as either minimax optimal or instance optimal: the former hold for the set of distribution pairs with prescribed Hellinger divergence and total variation distance, whereas the latter hold for specific distribution pairs. For the sample complexity of simple hypothesis testing under pure LDP constraints, we establish instance-optimal bounds for distributions with binary support; minimax-optimal bounds for general distributions; and (approximately) instance-optimal, computationally efficient algorithms for general distributions. When both privacy and communication constraints are present, we develop instance-optimal, computationally efficient algorithms that achieve the minimum possible sample complexity (up to universal constants). Our results on instance-optimal algorithms hinge on identifying the extreme points of the joint range set $\mathcal A$ of two distributions $p$ and $q$, defined as $\mathcal A := \{(\mathbf T p, \mathbf T q) | \mathbf T \in \mathcal C\}$, where $\mathcal C$ is the set of channels characterizing the constraints.  ( 2 min )
    Concentration of measure and generalized product of random vectors with an application to Hanson-Wright-like inequalities. (arXiv:2102.08020v3 [math.PR] UPDATED)
    Starting from concentration of measure hypotheses on $m$ random vectors $Z_1,\ldots, Z_m$, this article provides an expression of the concentration of functionals $\phi(Z_1,\ldots, Z_m)$ where the variations of $\phi$ on each variable depend on the product of the norms (or semi-norms) of the other variables (as if $\phi$ were a product). We illustrate the importance of this result through various generalizations of the Hanson-Wright concentration inequality as well as through a study of the random matrix $XDX^T$ and its resolvent $Q = (I_p - \frac{1}{n}XDX^T)^{-1}$, where $X$ and $D$ are random, which have fundamental interest in statistical machine learning applications.  ( 2 min )
    Spectral properties of sample covariance matrices arising from random matrices with independent non identically distributed columns. (arXiv:2109.02644v3 [math.PR] UPDATED)
    Given a random matrix $X= (x_1,\ldots, x_n)\in \mathcal M_{p,n}$ with independent columns and satisfying concentration of measure hypotheses and a parameter $z$ whose distance to the spectrum of $\frac{1}{n} XX^T$ should not depend on $p,n$, it was previously shown that the functionals $\text{tr}(AR(z))$, for $R(z) = (\frac{1}{n}XX^T- zI_p)^{-1}$ and $A\in \mathcal M_{p}$ deterministic, have a standard deviation of order $O(\|A\|_* / \sqrt n)$. Here, we show that $\|\mathbb E[R(z)] - \tilde R(z)\|_F \leq O(1/\sqrt n)$, where $\tilde R(z)$ is a deterministic matrix depending only on $z$ and on the means and covariances of the column vectors $x_1,\ldots, x_n$ (that do not have to be identically distributed). This estimation is key to providing accurate fluctuation rates of functionals of $X$ of interest (mostly related to its spectral properties) and is proved thanks to the introduction of a semi-metric $d_s$ defined on the set $\mathcal D_n(\mathbb H)$ of diagonal matrices with complex entries and positive imaginary part and satisfying, for all $D,D' \in \mathcal D_n(\mathbb H)$: $d_s(D,D') = \max_{i\in[n]} |D_i - D_i'|/ (\Im(D_i) \Im(D_i'))^{1/2}$. Possibly most importantly, the underlying concentration of measure assumption on the columns of $X$ finds an extremely natural ground for application in modern statistical machine learning algorithms where non-linear Lipschitz mappings and high number of classes form the base ingredients.  ( 2 min )

  • Open

    [D] Form on sharing ML codes
    Hello everyone, I would kindly ask you if you could help me get some insights about people’s preferences when sharing ML codes, with special focus on neural networks. I am here linking a very quick Google Form. Please, feel free to reach out. https://forms.gle/4zg5HLqLaEESuVTz9 submitted by /u/Fc3692 [link] [comments]  ( 57 min )
    [D] Soft Prompt Training Issue
    I am implementing soft prompt tuning (reproducing https://arxiv.org/abs/2104.08691v2) for my research project, but the training makes the model predict "False" only in T/F classification task (BoolQ dataset). I have tried all other code on full model fine-tuning and it's working to exclude all other issues (so it's unrelated to dataset and trainer). ​ There are some observations to exclude possible issues. The soft prompt parameters do change during soft prompt training. (gradient backprop on soft prompt is working) Training loss goes down normally just as model fine-tuning Any idea on how to debug the issue. submitted by /u/SEAIndigenous [link] [comments]  ( 57 min )
    [N] Microsoft Considers $10 Billion Investment in ChatGPT Creator --Bloomberg News
    Story here: https://www.bloomberg.com/news/articles/2023-01-10/microsoft-weighs-10-billion-chatgpt-investment-semafor-says?srnd=premium Unpaywalled: https://archive.ph/XOOlg submitted by /u/bikeskata [link] [comments]  ( 61 min )
    [D] Found very similar paper to my submitted paper on Arxiv
    If the mods want to ban this because it falls outside of meaningful discussion, that’s ok. I have a paper in the review process for CVPR atm. A couple of hours ago I stumbled upon an Arxiv paper upload 2 days ago that replicates my method almost exactly save for a few differences in how the inputs are processed, and how the problem is defined (super super similar problems). Their paper achieves far better results than mine, is tested on more datasets than mine, and comes from a big well known research group in my field to boot. I guess I feel a bit dejected? The approach was truly novel and nobody had done it before. Even with my limited training, was showing very promising results. I couldn’t train for longer to improve the model further due to a lack of hardware/budget, and I couldn’t test on more datasets for the same reason. It’ll probably get rejected from cvpr for those very reasons. I’m not complaining about this, it was my decision to submit there and take the chance, but damn. In hindsight I should’ve maybe gone for an easier journal or something and at least be guaranteed to be the first. 😔 Sorry if that was a bit of a rant, I just figure people here can relate a bit. submitted by /u/TightestKnees [link] [comments]  ( 64 min )
    [P] Evaluating several topic modeling implementations. What's the current best practice? BERTopic? OpenAI Ada-002?
    I have a set of ~100 topic categories, and I want to determine which are semantically close to a text input. I've found several implementations, but I know some (LDA) are already obsolete. OpenAI's text-embedding-ada-002 model just came out so I'm wondering if that's the best option now. Other topic modeling implementations: Multi-Class Text Classification with Doc2Vec & Logistic Regression Build taxonomy-based contextual targeting using AWS Media Intelligence and Hugging Face BERT Topic Modeling with BERTopic submitted by /u/gravenbirdman [link] [comments]  ( 60 min )
    [R] Class-Continuous Conditional Generative Nerual Radiance Field
    Paper: https://arxiv.org/abs/2301.00950 Project Page: https://tom919654.github.io/C3G_NeRF/ (Videos included) Code: https://github.com/tom919654/C3G-NeRF ​ Abstract: The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently,Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called ClassContinuous Conditional Generative NeRF (C3GNeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed C3GNeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, C3G-NeRF exhibits a Frechet Inception Distance (FID) of 7.64 in 3D- aware face image synthesis with a 1282 resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with C3G-NeRF. ​ Results: https://preview.redd.it/9y8b316dx4ba1.png?width=1750&format=png&auto=webp&s=8470793ff3974932058e1bd196ba7a857375f22f ​ https://preview.redd.it/0icxw8blx4ba1.png?width=1595&format=png&auto=webp&s=4a7c7226d7a9c5a2b28492aca8157c7037b4a647 https://reddit.com/link/107z8z1/video/ka6cqf1gx4ba1/player ​ https://reddit.com/link/107z8z1/video/2lm0bamix4ba1/player submitted by /u/JiwookKim [link] [comments]  ( 60 min )
    [D] Sample Average Approximation-Samples to be used
    Considering a Machine Learning scenario with some pre-available training samples S. In the objective function, let's suppose we have expectation over some reference distribution P_0 whose parameter (example, mean) has been approximated based on training samples S. When performing Sample Average Approximation for that expectation, is it necessary that we sample from the distribution of interest P_0 or can we directly use the training samples that we have? Could you please help me understand this? submitted by /u/RecentUnicorn [link] [comments]  ( 57 min )
  • Open

    "Comments on the Origin and Application of Markov Decision Processes", Howard 2002 (optimizing Sears Catalogue mailings ~1959 with value iteration & inventing policy iteration)
    submitted by /u/gwern [link] [comments]  ( 59 min )
    Episode Q0 is decreasing while cumulative reward increases (and doesn't converge to an optimal policy)
    I am using Matlab Simulink/Simscape to train a two-wheeled balancing robot to balance using a DDPG agent. I've tried tuning hyperparameters like learning rate, discount factor, and mini batch size to no avail. I've tweaked my reward function many times, and I feel like it's alright. For some reason my episode Q0 decreases when my reward improves. I believe this indicates that the critic and actor are disagreeing. Right? Does anybody have suggestions? If need be, I can include my script. Training Curve (orange is the episode Q0 and blue is the episode cumulative reward) submitted by /u/FenderBender43 [link] [comments]  ( 24 min )
    Let’s learn how to use Unity ML-Agents and train a bear 🐻 to shoot snowballs (Deep Reinforcement Learning Free Course by Hugging Face 🤗)
    Hey there! I’m happy to announce that we just published the fifth Unit of the Deep Reinforcement Learning Course 🥳 In this Unit, we’ll learn to use the Unity ML-Agents library by training two agents: The first one will learn to shoot snowballs at the spawning target. The second need to press a button to spawn a pyramid, then navigate to the pyramid, knock it over, and move to the gold brick at the top. To do that, it will need to explore its environment, and we will use a technique called curiosity. Then, after training, you’ll push the trained agents to the Hugging Face Hub, and you’ll be able to visualize it playing directly on your browser without having to use the Unity Editor Start Learning now 👉 https://huggingface.co/deep-rl-course/unit5/introduction https://preview.redd.it/l1nhb7vz59ba1.png?width=1920&format=png&auto=webp&s=3720dc7a454bdf6e4736a4ec3a4914647a06b564 If you want to start studying Deep Reinforcement Learning. We launched this course, and you’re right on time: 2023 is the perfect year to start. We wrote an introduction unit to help you get started. You can start learning now 👉 https://huggingface.co/deep-rl-course/unit0/introduction If you have questions or feedback I would love to answer them. submitted by /u/cranthir_ [link] [comments]  ( 58 min )
    Gymnasium-Robotics 1.2.0 - which now includes maintained versions of the environments from D4RL and the MaMuJoCo environments - is now live
    submitted by /u/jkterry1 [link] [comments]  ( 57 min )
    Deep RL with Mujoco environments using Docker on Apple Silicon
    Hi everyone, I am relatively new to the apple ecosystem, their way of doing things and docker in general. I want to get started with using mujoco environments from within a docker image. ​ The reason for the same being: I do not want to pollute my path and OS with multiple mujoco and deep RL library's dependencies. Secondly, I would like to have a reproducible image that can be used cross platform. ​ I generally debug on my personal machine, and do the actual training on university provided machines which utilise Nvidia hardware. Is there a way to accomplish all my goals? If not, can I at least successfully complete goal 1? submitted by /u/adeecc [link] [comments]  ( 65 min )
    Reconstruction loss for VAE model for skill learning
    I was reading this paper ASPiRe: Adaptive Skill Priors for Reinforcement Learning When looking at their code to train the VAE and priors for skills, I noticed for the reconstruction loss, instead of using classical mean squared error, they use: loss = -Normal(loc=actions_hat, scale=1).logprob(actions) It can be seen in line 179 of this file. I had never seen anyone use this reconstruction loss. Is there a good reason to use this loss? any empirical support? I'd appreciate any help to address this question. Edit*: I just learnt this is the negative likelihood loss. My question still remains. Why is this preferable to MSE? submitted by /u/carlml [link] [comments]  ( 62 min )
  • Open

    A small reminder to appreciate the fundamentals!
    submitted by /u/Imagine-your-success [link] [comments]  ( 47 min )
    Any good ones for real video to animated?
    Hi, Looking for a solution to turn a real video into an animated video through AI or any automation process. I've some camera shy people that want to make a video. Animating it from scratch would make the price go 10x so I'm looking for a solution in the AI space. If you know, please drop a comment. submitted by /u/V-Sec [link] [comments]  ( 48 min )
    AI is not alien, it's us
    submitted by /u/pbw [link] [comments]  ( 46 min )
    AI stack for 2023 - any tools missing to work with this year?
    Thought this was a cool graphic - pulled from https://buildspace.so/notes/ai-stack-2023 (free resource) https://preview.redd.it/uxp5tbcmp9ba1.png?width=456&format=png&auto=webp&s=e31e1f81b7250ddafa5359579e00b4f600def00b submitted by /u/bruclinbrocoli [link] [comments]  ( 49 min )
    in a new article on Hackernoon, i write about how copy and paste can be said to be a forerunner for the digital revolution of the AI text generator. kindly read it here: https://hackernoon.com/from-copy-and-paste-to-ai-text-generator-a-revolution-of-the-digital-age
    submitted by /u/Techoyy [link] [comments]  ( 47 min )
    Is law future proof career ?
    As a newly graduated lawyer, I have been experiencing anxiety about the future of my profession. With the rapid advancements in technology, I can't help but wonder if the legal field will become foreseeable future and if my four years of law school were all for vain , my fears increased after release of GPT-3 , should i think about career change on software dev/Web dev to adapt with changing reality ? submitted by /u/No_Car5573 [link] [comments]  ( 52 min )
    Nerf Technology with Stable Diffusion
    submitted by /u/oridnary_artist [link] [comments]  ( 48 min )
    German Drugmaker BioNTech To Buy AI Startup InstaDeep In $680M Deal
    submitted by /u/The-Techie [link] [comments]  ( 47 min )
    A good free text generator
    Hey, im looking for an actual free text generator that i can write simple articles of 100-300 words with. Most of these "free" sites offer a certain amount of credits etc. Preferably in multiple languages. Anyone have any ideas? submitted by /u/HutsKoning69 [link] [comments]  ( 48 min )
    What is the best paper on AI that you have read in 2022 and why?
    submitted by /u/tiensss [link] [comments]  ( 48 min )
    Some Ultra-Modern Generative Ai
    submitted by /u/Imagine-your-success [link] [comments]  ( 54 min )
    Bringing Extinct Dinosaurs Back To Life Using AI
    submitted by /u/liquidocelotYT [link] [comments]  ( 47 min )
    How You can Apply AI Logics for Scaling Your Business in 2023?
    submitted by /u/yudiz [link] [comments]  ( 48 min )
    Microsoft's AI Tool VALL-E can imitate anyone's voice with just a three-second sample
    submitted by /u/qptbook [link] [comments]  ( 47 min )
    Microsoft Will Likely Invest $10 billion for 49 Percent Stake in OpenAI
    submitted by /u/BackgroundResult [link] [comments]  ( 62 min )
    Scanning text for sensitive/controversial material
    Is there any way to use AI to process text like that? I heard that HR companies have similar processes to go trough social media profiles and look at “red flags”, but is there any engine available to do that on any piece of text? Sorry if it seems like an obvious question, I’m not too well versed in the topic and google is no help as usual. submitted by /u/Big_Razzmatazz_9251 [link] [comments]  ( 52 min )
    Microsoft to Own 49% of OpenAI Once $10B Deal Closes
    submitted by /u/lambolifeofficial [link] [comments]  ( 45 min )
    ChatGPT - code and talks
    submitted by /u/adrp23 [link] [comments]  ( 47 min )
    AI-powered "robot" lawyer will be first of its kind to represent defendant in court
    submitted by /u/Itchy0101 [link] [comments]  ( 49 min )
    chatGPT knowingly withholds information, reveals it slowly upon nudging
    submitted by /u/reportaman [link] [comments]  ( 56 min )
    Best Language AI models to run locally?
    I was curious as to if there are any Language AI models (like Chat GPT) that you can use locally on your own machine (so you don't run into as many server issues like you do on Chat GPT). If so, which are the best and where can you find them? submitted by /u/CreativePolymath [link] [comments]  ( 46 min )
    artflow ai + waifu2x. Tatar slim girl with black hair +
    submitted by /u/SubjectAd1535 [link] [comments]  ( 47 min )
    How can you use/get access to Chinchilla AI?
    So I've been hearing about the Chinchilla AI model (text ai, like Chat GPT) and how great it is, but I haven't seen anything regarding the model or how to use it, or even if it's available to the public yet or not. Does anyone have any insight into this? Is there any way to use it currently? I've worked with Stable Diffusion through A1111, would there be a model that works with A1111? Thanks in advance! submitted by /u/CreativePolymath [link] [comments]  ( 47 min )
    Weekly China AI News from Jan.2 to Jan.8: AI Dominates 2023 Top Tech Trends; Behind Douyin's Popular AI Anime Effect; Go Master Accused of Cheating with AI-Like Play Style
    submitted by /u/trcytony [link] [comments]  ( 46 min )
  • Open

    DSC Weekly 10 January 2023 – Recession Analysis
    Announcements Recession Analysis Editor’s Note: This week’s editorial was written by a guest contributor. If you would like to submit an article or editorial, please contact the editors below. One question that many are pondering is whether we are entering in a recession, and when it will end. Especially regarding the tech sector. Are there… Read More »DSC Weekly 10 January 2023 – Recession Analysis The post DSC Weekly 10 January 2023 – Recession Analysis appeared first on Data Science Central.  ( 20 min )
    Solution vs. Product-Implications for Agile Development
    A Solution is Not a Product Solution, Deliverable, Product, Work Product and other terms are often used interchangeably to describe output from development initiatives.  However, there are some extremely important conceptual differences between the terms Solution and Product and understanding them should inform and guide your thinking about what you are doing as you go… Read More »Solution vs. Product-Implications for Agile Development The post Solution vs. Product-Implications for Agile Development appeared first on Data Science Central.  ( 22 min )
    Responsible AI by design
    Happy new year! One of the big trends in AI this year: AI is maturing as a domain. We are using AI to address complex problems. That means we will need to be more aware of the potential downsides of AI. I believe that a new trend could manifest: responsible AI by design. a) Responsible… Read More »Responsible AI by design The post Responsible AI by design appeared first on Data Science Central.  ( 19 min )
    Flexible Engagement Model to Hire Full-Stack Developers: A 2023 Guide
    The demand for web and app development is rising as there are significant improvements in the field of technology every year. More and more people are establishing careers in the field of development as a result of advancements in technologies and frameworks. As a result, the data from Statista says that by 2024, there will… Read More »Flexible Engagement Model to Hire Full-Stack Developers: A 2023 Guide The post Flexible Engagement Model to Hire Full-Stack Developers: A 2023 Guide appeared first on Data Science Central.  ( 21 min )
  • Open

    is there any software like AI or image editing I could use for generating an image of a paper with text on it (like someone wrote on it)
    Do you guys know these apps like yandex image translator that generate text out of an image ? I want to basically do the opposite but into images of papers (like someone has written on it) submitted by /u/SnooPineapples7791 [link] [comments]  ( 47 min )
    Nerf Technology with Stable Diffusion
    submitted by /u/oridnary_artist [link] [comments]  ( 51 min )
  • Open

    The Greenest Generation: NVIDIA, Intel and Partners Supercharge AI Computing Efficiency
    AI is at the heart of humanity’s most transformative innovations — from developing COVID vaccines at unprecedented speeds and diagnosing cancer to powering autonomous vehicles and understanding climate change. Virtually every industry will benefit from adopting AI, but the technology has become more resource intensive as neural networks have increased in complexity. To avoid placing Read article >  ( 7 min )
  • Open

    Best practices for load testing Amazon SageMaker real-time inference endpoints
    Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so […]  ( 12 min )
    Get smarter search results with the Amazon Kendra Intelligent Ranking and OpenSearch plugin
    If you’ve had the opportunity to build a search application for unstructured data (i.e., wiki, informational web sites, self-service help pages, internal documentation, etc.) using open source or commercial-off-the-shelf search engines, then you’re probably familiar with the inherent accuracy challenges involved in getting relevant search results. The intended meaning of both query and document can […]  ( 12 min )
  • Open

    Approximating 1/Γ(x)
    A few days ago a comment that a graph looked like a Maxwell-Boltzman density lead to an approximation of 1/Γ(x), possibly a useful approximation. Approximating Γ(x) is a well-known problem, and for large x the solution is to use Stirling’s approximation or a few more terms from the asymptotic series that Stirling’s approximation is a […] Approximating 1/Γ(x) first appeared on John D. Cook.  ( 6 min )
  • Open

    Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers. (arXiv:2203.09142v2 [cs.LG] UPDATED)
    Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the nonsmooth functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries.
    Reliable Time Prediction in the Markov Stochastic Block Model. (arXiv:2004.04402v3 [cs.SI] UPDATED)
    We introduce the Markov Stochastic Block Model (MSBM): a growth model for community based networks where node attributes are assigned through a Markovian dynamic. We rely on HMMs' literature to design prediction methods that are robust to local clustering errors. We focus specifically on the link prediction and collaborative filtering problems and we introduce a new model selection procedure to infer the number of hidden clusters in the network. Our approaches for reliable prediction in MSBMs are not algorithm-dependent in the sense that they can be applied using your favourite clustering tool. In this paper, we use a recent SDP method to infer the hidden communities and we provide theoretical guarantees. In particular, we identify the relevant signal-to-noise ratio (SNR) in our framework and we prove that the misclassification error decays exponentially fast with respect to this SNR.  ( 2 min )
    Signal Enhancement for Magnetic Navigation Challenge Problem. (arXiv:2007.12158v2 [cs.LG] UPDATED)
    Harnessing the magnetic field of the Earth for navigation has shown promise as a viable alternative to other navigation systems. A magnetic navigation system collects its own magnetic field data using a magnetometer and uses magnetic anomaly maps to determine the current location. The greatest challenge with magnetic navigation arises when the magnetic field measurements from the magnetometer encompass the magnetic field from not just the Earth, but also from the vehicle on which it is mounted. It is difficult to separate the Earth magnetic anomaly field, which is crucial for navigation, from the total magnetic field reading from the sensor. The purpose of this challenge problem is to decouple the Earth and aircraft magnetic signals in order to derive a clean signal from which to perform magnetic navigation. Baseline testing on the dataset has shown that the Earth magnetic field can be extracted from the total magnetic field using machine learning (ML). The challenge is to remove the aircraft magnetic field from the total magnetic field using a trained model. This challenge offers an opportunity to construct an effective model for removing the aircraft magnetic field from the dataset by using a scientific machine learning (SciML) approach comprised of an ML algorithm integrated with the physics of magnetic navigation.  ( 2 min )
    Ranking Inferences Based on the Top Choice of Multiway Comparisons. (arXiv:2211.11957v3 [stat.ME] UPDATED)
    This paper considers ranking inference of $n$ items based on the observed data on the top choice among $M$ randomly selected items at each trial. This is a useful modification of the Plackett-Luce model for $M$-way ranking with only the top choice observed and is an extension of the celebrated Bradley-Terry-Luce model that corresponds to $M=2$. Under a uniform sampling scheme in which any $M$ distinguished items are selected for comparisons with probability $p$ and the selected $M$ items are compared $L$ times with multinomial outcomes, we establish the statistical rates of convergence for underlying $n$ preference scores using both $\ell_2$-norm and $\ell_\infty$-norm, with the minimum sampling complexity. In addition, we establish the asymptotic normality of the maximum likelihood estimator that allows us to construct confidence intervals for the underlying scores. Furthermore, we propose a novel inference framework for ranking items through a sophisticated maximum pairwise difference statistic whose distribution is estimated via a valid Gaussian multiplier bootstrap. The estimated distribution is then used to construct simultaneous confidence intervals for the differences in the preference scores and the ranks of individual items. They also enable us to address various inference questions on the ranks of these items. Extensive simulation studies lend further support to our theoretical results. A real data application illustrates the usefulness of the proposed methods convincingly.  ( 2 min )
    Evaluating counterfactual explanations using Pearl's counterfactual method. (arXiv:2301.02499v1 [stat.ML])
    Counterfactual explanations (CEs) are methods for generating an alternative scenario that produces a different desirable outcome. For example, if a student is predicted to fail a course, then counterfactual explanations can provide the student with alternate ways so that they would be predicted to pass. The applications are many. However, CEs are currently generated from machine learning models that do not necessarily take into account the true causal structure in the data. By doing this, bias can be introduced into the CE quantities. I propose in this study to test the CEs using Judea Pearl's method of computing counterfactuals which has thus far, surprisingly, not been seen in the counterfactual explanation (CE) literature. I furthermore evaluate these CEs on three different causal structures to show how the true underlying causal structure affects the CEs that are generated. This study presented a method of evaluating CEs using Pearl's method and it showed, (although using a limited sample size), that thirty percent of the CEs conflicted with those computed by Pearl's method. This shows that we cannot simply trust CEs and it is vital for us to know the true causal structure before we blindly compute counterfactuals using the original machine learning model.  ( 2 min )
    Learning from a Biased Sample. (arXiv:2209.01754v2 [stat.ME] UPDATED)
    The empirical risk minimization approach to data-driven decision making assumes that we can learn a decision rule from training data drawn under the same conditions as the ones we want to deploy it in. However, in a number of settings, we may be concerned that our training sample is biased, and that some groups (characterized by either observable or unobservable attributes) may be under- or over-represented relative to the general population; and in this setting empirical risk minimization over the training set may fail to yield rules that perform well at deployment. We propose a model of sampling bias called $\Gamma$-biased sampling, where observed covariates can affect the probability of sample selection arbitrarily much but the amount of unexplained variation in the probability of sample selection is bounded by a constant factor. Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling. We apply a result of Rockafellar and Uryasev to show that this problem is equivalent to an augmented convex risk minimization problem. We give statistical guarantees for learning a model that is robust to sampling bias via the method of sieves, and propose a deep learning algorithm whose loss function captures our robust learning target. We empirically validate our proposed method in simulations and a case study on ICU length of stay prediction.  ( 2 min )
    Bringing Differential Private SGD to Practice: On the Independence of Gaussian Noise and the Number of Training Rounds. (arXiv:2102.09030v5 [cs.LG] UPDATED)
    Different from existing Differential Privacy (DP) accountants, we introduce pro-active DP. Existing DP accountants keep track of how privacy budget has been spent while pro-active DP is a scheme that allows one to {\it a-priori} select parameters of DP-SGD based on a fixed privacy budget (in terms of $\epsilon$ and $\delta$) in such a way to optimize the anticipated utility (test accuracy) the most. To implement this idea, we show how to convert the classical DP moment accountant to a pro-active DP by exploiting the fact that it has a simple close form for computing spent privacy budget for a given interaction round. The DP moment accountant is introduced in context of DP-SGD and has the following property which is the key ingredient to build pro-active DP. In DP-SGD each round communicates a local SGD update which leaks some new information about the underlying local data set to the outside world. In order to provide privacy, Gaussian noise with standard deviation $\sigma$ is added to local SGD updates after performing a clipping operation and normalizing with the clipping constant. We show that for attaining $(\epsilon,\delta)$-differential privacy $\sigma$ can be chosen equal to $\sqrt{2(\epsilon +\ln(1/\delta))/\epsilon}$ for $\epsilon=\Omega(T/N^2)$, where $T$ is the total number of rounds and $N$ is equal to the size of the local data set. In many existing machine learning problems, $N$ is always large and $T=O(N)$. Hence, $\sigma$ becomes ``independent'' of any $T=O(N)$ choice with $\epsilon=\Omega(1/N)$. This means that our {\em $\sigma$ only depends on $N$ rather than $T$}. We show how this differential privacy characterization allows us to convert DP moment accountant to a pro-active DP.  ( 3 min )
    Valid P-Value for Deep Learning-Driven Salient Region. (arXiv:2301.02437v1 [stat.ML])
    Various saliency map methods have been proposed to interpret and explain predictions of deep learning models. Saliency maps allow us to interpret which parts of the input signals have a strong influence on the prediction results. However, since a saliency map is obtained by complex computations in deep learning models, it is often difficult to know how reliable the saliency map itself is. In this study, we propose a method to quantify the reliability of a salient region in the form of p-values. Our idea is to consider a salient region as a selected hypothesis by the trained deep learning model and employ the selective inference framework. The proposed method can provably control the probability of false positive detections of salient regions. We demonstrate the validity of the proposed method through numerical examples in synthetic and real datasets. Furthermore, we develop a Keras-based framework for conducting the proposed selective inference for a wide class of CNNs without additional implementation cost.  ( 2 min )
    DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting. (arXiv:2301.02332v1 [cs.LG])
    Despite the high performance of neural network-based time series forecasting methods, the inherent challenge in explaining their predictions has limited their applicability in certain application areas. Due to the difficulty in identifying causal relationships between the input and output of such black-box methods, they rarely have been adopted in domains such as legal and medical fields in which the reliability and interpretability of the results can be essential. In this paper, we propose \model, a novel deep learning-based probabilistic time series forecasting architecture that is intrinsically interpretable. We conduct experiments with multiple datasets and performance metrics and empirically show that our model is not only interpretable but also provides comparable performance to state-of-the-art probabilistic time series forecasting methods. Furthermore, we demonstrate that interpreting the parameters of the stochastic processes of interest can provide useful insights into several application areas.  ( 2 min )
    Multi-treatment Effect Estimation from Biomedical Data. (arXiv:2112.07574v3 [cs.LG] UPDATED)
    This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments. In contrast to existing methods, M3E2 can handle multiple treatment effects applied simultaneously to the same unit, continuous and binary treatments, and many covariates. We compared M3E2 with three baselines in three synthetic benchmark datasets: two with multiple treatments and one with one treatment. Our analysis showed that our method has superior performance, making more assertive estimations of the multiple treatment effects.
    Seeking the Truth Beyond the Data. An Unsupervised Machine Learning Approach. (arXiv:2207.06949v3 [stat.ML] UPDATED)
    Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help her/him to identify patterns among the data. Dealing with large databases, such patterns may not be easily detectable without the contribution of a clustering algorithm. This article provides a deep description of the most widely used clustering methodologies accompanied by useful presentations concerning suitable parameter selection and initializations. Simultaneously, this article not only represents a review highlighting the major elements of examined clustering techniques but emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets, revealing their existing weaknesses and capabilities through accuracy and complexity, during the confrontation of discrete and continuous observations. The produced results help us extract valuable conclusions about the appropriateness of the examined clustering techniques in accordance with the dataset's size.
    Low-rank Approximation of Linear Maps. (arXiv:1812.09042v2 [stat.ML] UPDATED)
    This work provides closed-form solutions and minimum achievable errors for a large class of low-rank approximation problems in Hilbert spaces. The proposed theorem generalizes to the case of bounded linear operators the previous results obtained in the finite dimensional case for the Frobenius norm. The theorem provides the basis for the design of tractable algorithms for kernel or continuous DMD.  ( 2 min )
    A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow. (arXiv:2301.02651v1 [eess.SY])
    In this paper, we propose a robust data-driven process model whose hyperparameters are robustly estimated using the Schweppe-type generalized maximum likelihood estimator. The proposed model is trained on recorded time-series data of voltage phasors and power injections to perform a time-series stochastic power flow calculation. Power system data are often corrupted with outliers caused by large errors, fault conditions, power outages, and extreme weather, to name a few. The proposed model downweights vertical outliers and bad leverage points in the measurements of the training dataset. The weights used to bound the influence of the outliers are calculated using projection statistics, which are a robust version of Mahalanobis distances of the time series data points. The proposed method is demonstrated on the IEEE 33-Bus power distribution system and a real-world unbalanced 240-bus power distribution system heavily integrated with renewable energy sources. Our simulation results show that the proposed robust model can handle up to 25% of outliers in the training data set.
    Reversibility of elliptical slice sampling revisited. (arXiv:2301.02426v1 [math.ST])
    We discuss the well-definedness of elliptical slice sampling, a Markov chain approach for approximate sampling of posterior distributions introduced by Murray, Adams and MacKay 2010. We point to a regularity requirement and provide an alternative proof of the reversibility property. In particular, this guarantees the correctness of the slice sampling scheme also on infinite-dimensional separable Hilbert spaces.  ( 2 min )
    Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction. (arXiv:2103.08280v5 [math.OC] UPDATED)
    In this paper, we study the lower complexity bounds for finite-sum optimization problems, where the objective is the average of $n$ individual component functions. We consider Proximal Incremental First-order (PIFO) algorithms which have access to the gradient and proximal oracles for each component function. To incorporate loopless methods, we also allow PIFO algorithms to obtain the full gradient infrequently. We develop a novel approach to constructing the hard instances, which partitions the tridiagonal matrix of classical examples into $n$ groups. This construction is friendly to the analysis of PIFO algorithms. Based on this construction, we establish the lower complexity bounds for finite-sum minimax optimization problems when the objective is convex-concave or nonconvex-strongly-concave and the class of component functions is $L$-average smooth. Most of these bounds are nearly matched by existing upper bounds up to log factors. We can also derive similar lower bounds for finite-sum minimization problems as previous work under both smoothness and average smoothness assumptions. Our lower bounds imply that proximal oracles for smooth functions are not much more powerful than gradient oracles.
    Convergence rates of the stochastic alternating algorithm for bi-objective optimization. (arXiv:2203.10605v2 [math.OC] UPDATED)
    Stochastic alternating algorithms for bi-objective optimization are considered when optimizing two conflicting functions for which optimization steps have to be applied separately for each function. Such algorithms consist of applying a certain number of steps of gradient or subgradient descent on each single objective at each iteration. In this paper, we show that stochastic alternating algorithms achieve a sublinear convergence rate of $\mathcal{O}(1/T)$, under strong convexity, for the determination of a minimizer of a weighted-sum of the two functions, parameterized by the number of steps applied on each of them. An extension to the convex case is presented for which the rate weakens to $\mathcal{O}(1/\sqrt{T})$. These rates are valid also in the non-smooth case. Importantly, by varying the proportion of steps applied to each function, one can determine an approximation to the Pareto front.
    Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces. (arXiv:2211.14400v2 [stat.ML] UPDATED)
    Let $\Omega = [0,1]^d$ be the unit cube in $\mathbb{R}^d$. We study the problem of how efficiently, in terms of the number of parameters, deep neural networks with the ReLU activation function can approximate functions in the Sobolev space $W^s(L_q(\Omega))$ with error measured in $L_p(\Omega)$. This problem is important when studying the application of neural networks in scientific computing and has previously been completely solved only in the case $p=q=\infty$. Our contribution is to provide a complete solution for all $1\leq p,q\leq \infty$ and $s > 0$, including asymptotically matching upper and lower bounds. The key technical tool is a novel bit-extraction technique which gives an optimal encoding of sparse vectors. This enables us to obtain sharp upper bounds in the non-linear regime where $p > q$. We also provide a novel method for deriving $L_p$-approximation lower bounds based upon VC-dimension when $p < \infty$. Our results show that very deep ReLU networks significantly outperform classical methods of approximation in terms of the number of parameters, but that this comes at the cost of parameters which are not encodable.
  • Open

    Jensen-Shannon Divergence Based Loss Functions for Bayesian Neural Networks. (arXiv:2209.11366v2 [cs.LG] UPDATED)
    The Kullback-Leibler (KL) divergence is widely used for the variational inference of Bayesian Neural Networks (BNNs) to approximate the posterior distribution of weights. However, the KL divergence is unbounded and asymmetric, which may lead to instabilities during optimization or may yield poor generalizations. To overcome these limitations, we examine the Jensen-Shannon (JS) divergence that is more general, bounded, and symmetric. Towards this, we propose two novel loss functions for BNNs: 1) a geometric JS divergence (JS-G) based loss function that is symmetric but unbounded with closed-form expression for Gaussian priors and 2) a generalized JS divergence (JS-A) based loss function that is symmetric and bounded. We show that the conventional KL divergence-based loss function is a special case of the loss functions presented in this work. To evaluate the divergence part of the proposed JS-G-based loss function, we use an exact closed-form expression for Gaussian priors. For any other priors of JS-G and for the JS-A-based loss function we use Monte Carlo approximation. We provide algorithms to optimize the loss function using both these methods. The proposed loss functions offer additional parameters that can be tuned to control the regularisation. We explain the reason why the proposed loss functions should perform better than the state-of-the-art. Further, we derive the conditions under which the proposed JS-G-loss function regularises better than the KL divergence-based loss function for Gaussian priors and posteriors. The proposed JS divergence-based Bayesian convolutional neural networks (BCNN) perform better than the state-of-the-art BCNN, which is shown for the classification of the CIFAR data set having various degrees of noise and a biased histopathology data set.
    Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces. (arXiv:2211.14400v2 [stat.ML] UPDATED)
    Let $\Omega = [0,1]^d$ be the unit cube in $\mathbb{R}^d$. We study the problem of how efficiently, in terms of the number of parameters, deep neural networks with the ReLU activation function can approximate functions in the Sobolev space $W^s(L_q(\Omega))$ with error measured in $L_p(\Omega)$. This problem is important when studying the application of neural networks in scientific computing and has previously been completely solved only in the case $p=q=\infty$. Our contribution is to provide a complete solution for all $1\leq p,q\leq \infty$ and $s > 0$, including asymptotically matching upper and lower bounds. The key technical tool is a novel bit-extraction technique which gives an optimal encoding of sparse vectors. This enables us to obtain sharp upper bounds in the non-linear regime where $p > q$. We also provide a novel method for deriving $L_p$-approximation lower bounds based upon VC-dimension when $p < \infty$. Our results show that very deep ReLU networks significantly outperform classical methods of approximation in terms of the number of parameters, but that this comes at the cost of parameters which are not encodable.
    Proportional Multicalibration. (arXiv:2209.14613v2 [cs.LG] UPDATED)
    Multicalibration is a desirable fairness criteria that constrains calibration error among flexibly-defined groups in the data while maintaining overall calibration. However, when outcome probabilities are correlated with group membership, multicalibrated models can exhibit a higher percent calibration error among groups with lower base rates than groups with higher base rates. As a result, it remains possible for a decision-maker to learn to trust or distrust model predictions for specific groups. To alleviate this, we propose \emph{proportional multicalibration}, a criteria that constrains the percent calibration error among groups and within prediction bins. We prove that satisfying proportional multicalibration bounds a model's multicalibration as well its \emph{differential calibration}, a stronger fairness criteria inspired by the fairness notion of sufficiency. We provide an efficient algorithm for post-processing risk prediction models for proportional multicalibration and evaluate it empirically. We conduct simulation studies and investigate a real-world application of PMC-postprocessing to prediction of emergency department patient admissions. We observe that proportional multicalibration is a promising criteria for controlling simultaneous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.
    DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning. (arXiv:2111.12062v2 [cs.LG] UPDATED)
    Self-supervised learning algorithms, including BERT and SimCLR, have enabled significant strides in fields like natural language processing, computer vision, and speech processing. However, these algorithms are domain-specific, meaning that new self-supervised learning algorithms must be developed for each new setting, including myriad healthcare, scientific, and multimodal domains. To catalyze progress toward domain-agnostic methods, we introduce DABS: a Domain-Agnostic Benchmark for Self-supervised learning. To perform well on DABS, an algorithm is evaluated on seven diverse domains: natural images, multichannel sensor data, English text, speech recordings, multilingual text, chest x-rays, and images with text descriptions. Each domain contains an unlabeled dataset for pretraining; the model is then is scored based on its downstream performance on a set of labeled tasks in the domain. We also present e-Mix and ShED: two baseline domain-agnostic algorithms; their relatively modest performance demonstrates that significant progress is needed before self-supervised learning is an out-of-the-box solution for arbitrary domains. Code for benchmark datasets and baseline algorithms is available at https://github.com/alextamkin/dabs.
    p-Adic Statistical Field Theory and Deep Belief Networks. (arXiv:2207.13877v4 [math-ph] UPDATED)
    In this work we initiate the study of the correspondence between p-adic statistical field theories (SFTs) and neural networks (NNs). In general quantum field theories over a p-adic spacetime can be formulated in a rigorous way. Nowadays these theories are considered just mathematical toy models for understanding the problems of the true theories. In this work we show these theories are deeply connected with the deep belief networks (DBNs). Hinton et al. constructed DBNs by stacking several restricted Boltzmann machines (RBMs). The purpose of this construction is to obtain a network with a hierarchical structure (a deep learning architecture). An RBM corresponds to a certain spin glass, we argue that a DBN should correspond to an ultrametric spin glass. A model of such a system can be easily constructed by using p-adic numbers. In our approach, a p-adic SFT corresponds to a p-adic continuous DBN, and a discretization of this theory corresponds to a p-adic discrete DBN. We show that these last machines are universal approximators. In the p-adic framework, the correspondence between SFTs and NNs is not fully developed. We point out several open problems.
    Inversion of sea surface currents from satellite-derived SST-SSH synergies with 4DVarNets. (arXiv:2211.13059v2 [physics.ao-ph] UPDATED)
    Satellite altimetry is a unique way for direct observations of sea surface dynamics. This is however limited to the surface-constrained geostrophic component of sea surface velocities. Ageostrophic dynamics are however expected to be significant for horizontal scales below 100~km and time scale below 10~days. The assimilation of ocean general circulation models likely reveals only a fraction of this ageostrophic component. Here, we explore a learning-based scheme to better exploit the synergies between the observed sea surface tracers, especially sea surface height (SSH) and sea surface temperature (SST), to better inform sea surface currents. More specifically, we develop a 4DVarNet scheme which exploits a variational data assimilation formulation with trainable observations and {\em a priori} terms. An Observing System Simulation Experiment (OSSE) in a region of the Gulf Stream suggests that SST-SSH synergies could reveal sea surface velocities for time scales of 2.5-3.0 days and horizontal scales of 0.5$^\circ$-0.7$^\circ$, including a significant fraction of the ageostrophic dynamics ($\approx$ 47\%). The analysis of the contribution of different observation data, namely nadir along-track altimetry, wide-swath SWOT altimetry and SST data, emphasizes the role of SST features for the reconstruction at horizontal spatial scales ranging from \nicefrac{1}{20}$^\circ$ to \nicefrac{1}{4}$^\circ$.
    TarViS: A Unified Approach for Target-based Video Segmentation. (arXiv:2301.02657v1 [cs.CV])
    The general domain of video segmentation is currently fragmented into different tasks spanning multiple benchmarks. Despite rapid progress in the state-of-the-art, current methods are overwhelmingly task-specific and cannot conceptually generalize to other tasks. Inspired by recent approaches with multi-task capability, we propose TarViS: a novel, unified network architecture that can be applied to any task that requires segmenting a set of arbitrarily defined 'targets' in video. Our approach is flexible with respect to how tasks define these targets, since it models the latter as abstract 'queries' which are then used to predict pixel-precise target masks. A single TarViS model can be trained jointly on a collection of datasets spanning different tasks, and can hot-swap between tasks during inference without any task-specific retraining. To demonstrate its effectiveness, we apply TarViS to four different tasks, namely Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), Video Object Segmentation (VOS) and Point Exemplar-guided Tracking (PET). Our unified, jointly trained model achieves state-of-the-art performance on 5/7 benchmarks spanning these four tasks, and competitive performance on the remaining two.
    Seeking the Truth Beyond the Data. An Unsupervised Machine Learning Approach. (arXiv:2207.06949v3 [stat.ML] UPDATED)
    Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help her/him to identify patterns among the data. Dealing with large databases, such patterns may not be easily detectable without the contribution of a clustering algorithm. This article provides a deep description of the most widely used clustering methodologies accompanied by useful presentations concerning suitable parameter selection and initializations. Simultaneously, this article not only represents a review highlighting the major elements of examined clustering techniques but emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets, revealing their existing weaknesses and capabilities through accuracy and complexity, during the confrontation of discrete and continuous observations. The produced results help us extract valuable conclusions about the appropriateness of the examined clustering techniques in accordance with the dataset's size.
    When Spectral Modeling Meets Convolutional Networks: A Method for Discovering Reionization-era Lensed Quasars in Multi-band Imaging Data. (arXiv:2211.14543v2 [astro-ph.GA] UPDATED)
    Over the last two decades, around 300 quasars have been discovered at $z\gtrsim6$, yet only one has identified as being strongly gravitationally lensed. We explore a new approach -- enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion -- which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer.Our search method consists of two main parts: (i) the preselection of the candidates based on their spectral energy distributions (SEDs) using catalog-level photometry and (ii) relative probabilities calculation of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy-quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of $\theta_\mathrm{E} \leq 1$ arcsec. Visual inspection is then performed for sources with CNN scores of $P_\mathrm{lens} > 0.1$, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs that can overcome the veto limitations of primarily dropout-based SED selection approaches.
    Utilising physics-guided deep learning to overvome data scarcity. (arXiv:2211.15664v2 [cs.LG] UPDATED)
    Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challenging or even impossible in many real-world applications, such as structural risk estimation and medical diagnosis. This presents a significant barrier to the practical implementation of DL in these fields. Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. This can be applied to any systems that are controlled or governed by physics laws, such as mechanics, finance and medical applications. It has been demonstrated that, with the additional information provided by physics laws, PGDL achieves great accuracy and generalisation in the presence of data scarcity. This review provides a detailed examination of PGDL and offers a structured overview of its use in addressing data scarcity across various fields, including physics, engineering and medical applications. Moreover, the review identifies the current limitations and opportunities for PGDL in relation to data scarcity and offers a thorough discussion on the future prospects of PGDL.
    Expanding boundaries of Gap Safe screening. (arXiv:2102.10846v2 [cs.LG] UPDATED)
    Sparse optimization problems are ubiquitous in many fields such as statistics, signal/image processing and machine learning. This has led to the birth of many iterative algorithms to solve them. A powerful strategy to boost the performance of these algorithms is known as safe screening: it allows the early identification of zero coordinates in the solution, which can then be eliminated to reduce the problem's size and accelerate convergence. In this work, we extend the existing Gap Safe screening framework by relaxing the global strong-concavity assumption on the dual cost function. Instead, we exploit local regularity properties, that is, strong concavity on well-chosen subsets of the domain. The non-negativity constraint is also integrated to the existing framework. Besides making safe screening possible to a broader class of functions that includes beta-divergences (e.g., the Kullback-Leibler divergence), the proposed approach also improves upon the existing Gap Safe screening rules on previously applicable cases (e.g., logistic regression). The proposed general framework is exemplified by some notable particular cases: logistic function, beta = 1.5 and Kullback-Leibler divergences. Finally, we showcase the effectiveness of the proposed screening rules with different solvers (coordinate descent, multiplicative-update and proximal gradient algorithms) and different data sets (binary classification, hyperspectral and count data).
    Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise. (arXiv:2112.02960v3 [cs.LG] UPDATED)
    Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pipeline alternates between eliminating possible incorrect labels and semi-supervised training. However, discarding part of noisy labels could result in a loss of information, especially when the corruption has a dependency on data, e.g., class-dependent or instance-dependent. Moreover, from the training dynamics of a representative two-stage method DivideMix, we identify the domination of confirmation bias: pseudo-labels fail to correct a considerable amount of noisy labels, and consequently, the errors accumulate. To sufficiently exploit information from noisy labels and mitigate wrong corrections, we propose Robust Label Refurbishment (Robust LR) a new hybrid method that integrates pseudo-labeling and confidence estimation techniques to refurbish noisy labels. We show that our method successfully alleviates the damage of both label noise and confirmation bias. As a result, it achieves state-of-the-art performance across datasets and noise types, namely CIFAR under different levels of synthetic noise and Mini-WebVision and ANIMAL-10N with real-world noise.
    Fast and Low-Memory Deep Neural Networks Using Binary Matrix Factorization. (arXiv:2210.13468v2 [cs.LG] UPDATED)
    Despite the outstanding performance of deep neural networks in different applications, they are still computationally extensive and require a great number of memories. This motivates more research on reducing the resources required for implementing such networks. An efficient approach addressed for this purpose is matrix factorization, which has been shown to be effective on different networks. In this paper, we utilize binary matrix factorization and show its great efficiency in reducing the required number of resources in deep neural networks. In effect, this technique can lead to the practical implementation of such networks.
    First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation. (arXiv:2212.03251v2 [cs.LG] UPDATED)
    Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration requires an agent to first return to an interesting state ('Go'), and only then explore into unknown terrain ('Explore'). We refer to such exploration after a goal is reached as 'post-exploration'. In this paper, we present a clear ablation study of post-exploration in a general intrinsically motivated goal exploration process (IMGEP) framework, that the Go-Explore paper did not show. We study the isolated potential of post-exploration, by turning it on and off within the same algorithm under both tabular and deep RL settings on both discrete navigation and continuous control tasks. Experiments on a range of MiniGrid and Mujoco environments show that post-exploration indeed helps IMGEP agents reach more diverse states and boosts their performance. In short, our work suggests that RL researchers should consider to use post-exploration in IMGEP when possible since it is effective, method-agnostic and easy to implement.
    Centralized Cooperative Exploration Policy for Continuous Control Tasks. (arXiv:2301.02375v1 [cs.LG])
    The deep reinforcement learning (DRL) algorithm works brilliantly on solving various complex control tasks. This phenomenal success can be partly attributed to DRL encouraging intelligent agents to sufficiently explore the environment and collect diverse experiences during the agent training process. Therefore, exploration plays a significant role in accessing an optimal policy for DRL. Despite recent works making great progress in continuous control tasks, exploration in these tasks has remained insufficiently investigated. To explicitly encourage exploration in continuous control tasks, we propose CCEP (Centralized Cooperative Exploration Policy), which utilizes underestimation and overestimation of value functions to maintain the capacity of exploration. CCEP first keeps two value functions initialized with different parameters, and generates diverse policies with multiple exploration styles from a pair of value functions. In addition, a centralized policy framework ensures that CCEP achieves message delivery between multiple policies, furthermore contributing to exploring the environment cooperatively. Extensive experimental results demonstrate that CCEP achieves higher exploration capacity. Empirical analysis shows diverse exploration styles in the learned policies by CCEP, reaping benefits in more exploration regions. And this exploration capacity of CCEP ensures it outperforms the current state-of-the-art methods across multiple continuous control tasks shown in experiments.
    How Powerful are K-hop Message Passing Graph Neural Networks. (arXiv:2205.13328v4 [cs.LG] UPDATED)
    The most popular design paradigm for Graph Neural Networks (GNNs) is 1-hop message passing -- aggregating information from 1-hop neighbors repeatedly. However, the expressive power of 1-hop message passing is bounded by the Weisfeiler-Lehman (1-WL) test. Recently, researchers extended 1-hop message passing to K-hop message passing by aggregating information from K-hop neighbors of nodes simultaneously. However, there is no work on analyzing the expressive power of K-hop message passing. In this work, we theoretically characterize the expressive power of K-hop message passing. Specifically, we first formally differentiate two different kernels of K-hop message passing which are often misused in previous works. We then characterize the expressive power of K-hop message passing by showing that it is more powerful than 1-WL and can distinguish almost all regular graphs. Despite the higher expressive power, we show that K-hop message passing still cannot distinguish some simple regular graphs and its expressive power is bounded by 3-WL. To further enhance its expressive power, we introduce a KP-GNN framework, which improves K-hop message passing by leveraging the peripheral subgraph information in each hop. We show that KP-GNN can distinguish many distance regular graphs which could not be distinguished by previous distance encoding or 3-WL methods. Experimental results verify the expressive power and effectiveness of KP-GNN. KP-GNN achieves competitive results across all benchmark datasets.
    Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads. (arXiv:2301.02593v1 [cs.MA])
    To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation. Frequency regulation through demand response has the potential to coordinate temporally flexible loads, such as air conditioners, to counteract these variations. Existing approaches for discrete control with dynamic constraints struggle to provide satisfactory performance for fast timescale action selection with hundreds of agents. We propose a decentralized agent trained with multi-agent proximal policy optimization with localized communication. We explore two communication frameworks: hand-engineered, or learned through targeted multi-agent communication. The resulting policies perform well and robustly for frequency regulation, and scale seamlessly to arbitrary numbers of houses for constant processing times.
    Signal Enhancement for Magnetic Navigation Challenge Problem. (arXiv:2007.12158v2 [cs.LG] UPDATED)
    Harnessing the magnetic field of the Earth for navigation has shown promise as a viable alternative to other navigation systems. A magnetic navigation system collects its own magnetic field data using a magnetometer and uses magnetic anomaly maps to determine the current location. The greatest challenge with magnetic navigation arises when the magnetic field measurements from the magnetometer encompass the magnetic field from not just the Earth, but also from the vehicle on which it is mounted. It is difficult to separate the Earth magnetic anomaly field, which is crucial for navigation, from the total magnetic field reading from the sensor. The purpose of this challenge problem is to decouple the Earth and aircraft magnetic signals in order to derive a clean signal from which to perform magnetic navigation. Baseline testing on the dataset has shown that the Earth magnetic field can be extracted from the total magnetic field using machine learning (ML). The challenge is to remove the aircraft magnetic field from the total magnetic field using a trained model. This challenge offers an opportunity to construct an effective model for removing the aircraft magnetic field from the dataset by using a scientific machine learning (SciML) approach comprised of an ML algorithm integrated with the physics of magnetic navigation.
    Understanding Urban Water Consumption using Remotely Sensed Data. (arXiv:2205.02932v2 [cs.CV] UPDATED)
    Urban metabolism is an active field of research that deals with the estimation of emissions and resource consumption from urban regions. The analysis could be carried out through a manual surveyor by the implementation of elegant machine learning algorithms. In this exploratory work, we estimate the water consumption by the buildings in the region captured by satellite imagery. To this end, we break our analysis into three parts: i) Identification of building pixels, given a satellite image, followed by ii) identification of the building type (residential/non-residential) from the building pixels, and finally iii) using the building pixels along with their type to estimate the water consumption using the average per unit area consumption for different building types as obtained from municipal surveys.
    Learning Invariant Rules from Data for Interpretable Anomaly Detection. (arXiv:2211.13577v2 [cs.LG] UPDATED)
    In the research area of anomaly detection, novel and promising methods are frequently developed. However, most existing studies exclusively focus on the detection task only and ignore the interpretability of the underlying models as well as their detection results. However, anomaly interpretation, which aims to provide explanation of why specific data instances are identified as anomalies, is an equally important task in many real-world applications. In this work, we propose a novel framework which synergizes several machine learning and data mining techniques to automatically learn invariant rules that are consistently satisfied in the training data. The learned invariant rules can provide explicit explanation of anomaly detection results and thus are extremely useful for subsequent decision-making regarding reported anomalies. Furthermore, our empirical evaluation shows that the proposed method can also achieve comparable or even better performance in terms of AUC and partial AUC on public benchmark datasets across various application domains compared with start-of-the-art anomaly detection models.
    Silent Killer: Optimizing Backdoor Trigger Yields a Stealthy and Powerful Data Poisoning Attack. (arXiv:2301.02615v1 [cs.CR])
    We propose a stealthy and powerful backdoor attack on neural networks based on data poisoning (DP). In contrast to previous attacks, both the poison and the trigger in our method are stealthy. We are able to change the model's classification of samples from a source class to a target class chosen by the attacker. We do so by using a small number of poisoned training samples with nearly imperceptible perturbations, without changing their labels. At inference time, we use a stealthy perturbation added to the attacked samples as a trigger. This perturbation is crafted as a universal adversarial perturbation (UAP), and the poison is crafted using gradient alignment coupled to this trigger. Our method is highly efficient in crafting time compared to previous methods and requires only a trained surrogate model without additional retraining. Our attack achieves state-of-the-art results in terms of attack success rate while maintaining high accuracy on clean samples.
    Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions. (arXiv:2207.08975v2 [eess.IV] UPDATED)
    Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.
    Triple-stream Deep Metric Learning of Great Ape Behavioural Actions. (arXiv:2301.02642v1 [cs.CV])
    We propose the first metric learning system for the recognition of great ape behavioural actions. Our proposed triple stream embedding architecture works on camera trap videos taken directly in the wild and demonstrates that the utilisation of an explicit DensePose-C chimpanzee body part segmentation stream effectively complements traditional RGB appearance and optical flow streams. We evaluate system variants with different feature fusion techniques and long-tail recognition approaches. Results and ablations show performance improvements of ~12% in top-1 accuracy over previous results achieved on the PanAf-500 dataset containing 180,000 manually annotated frames across nine behavioural actions. Furthermore, we provide a qualitative analysis of our findings and augment the metric learning system with long-tail recognition techniques showing that average per class accuracy -- critical in the domain -- can be improved by ~23% compared to the literature on that dataset. Finally, since our embedding spaces are constructed as metric, we provide first data-driven visualisations of the great ape behavioural action spaces revealing emerging geometry and topology. We hope that the work sparks further interest in this vital application area of computer vision for the benefit of endangered great apes.
    Learning from a Biased Sample. (arXiv:2209.01754v2 [stat.ME] UPDATED)
    The empirical risk minimization approach to data-driven decision making assumes that we can learn a decision rule from training data drawn under the same conditions as the ones we want to deploy it in. However, in a number of settings, we may be concerned that our training sample is biased, and that some groups (characterized by either observable or unobservable attributes) may be under- or over-represented relative to the general population; and in this setting empirical risk minimization over the training set may fail to yield rules that perform well at deployment. We propose a model of sampling bias called $\Gamma$-biased sampling, where observed covariates can affect the probability of sample selection arbitrarily much but the amount of unexplained variation in the probability of sample selection is bounded by a constant factor. Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling. We apply a result of Rockafellar and Uryasev to show that this problem is equivalent to an augmented convex risk minimization problem. We give statistical guarantees for learning a model that is robust to sampling bias via the method of sieves, and propose a deep learning algorithm whose loss function captures our robust learning target. We empirically validate our proposed method in simulations and a case study on ICU length of stay prediction.
    Does compressing activations help model parallel training?. (arXiv:2301.02654v1 [cs.LG])
    Large-scale Transformer models are known for their exceptional performance in a range of tasks, but training them can be difficult due to the requirement for communication-intensive model parallelism. One way to improve training speed is to compress the message size in communication. Previous approaches have primarily focused on compressing gradients in a data parallelism setting, but compression in a model-parallel setting is an understudied area. We have discovered that model parallelism has fundamentally different characteristics than data parallelism. In this work, we present the first empirical study on the effectiveness of compression methods for model parallelism. We implement and evaluate three common classes of compression algorithms - pruning-based, learning-based, and quantization-based - using a popular Transformer training framework. We evaluate these methods across more than 160 settings and 8 popular datasets, taking into account different hyperparameters, hardware, and both fine-tuning and pre-training stages. We also provide analysis when the model is scaled up. Finally, we provide insights for future development of model parallelism compression algorithms.
    SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training. (arXiv:2301.02601v1 [quant-ph])
    Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed. For instance, transfer learning methods have been shown to be successfully applicable to hybrid image classification tasks. Nevertheless, beneficial circuit architectures still need to be explored. Therefore, tracing the impact of the chosen circuit architecture and parameterization is crucial for the development of beneficially applicable hybrid methods. However, current methods include processes where both parts are trained concurrently, therefore not allowing for a strict separability of classical and quantum impact. Thus, those architectures might produce models that yield a superior prediction accuracy whilst employing the least possible quantum impact. To tackle this issue, we propose Sequential Quantum Enhanced Training (SEQUENT) an improved architecture and training process for the traceable application of quantum computing methods to hybrid machine learning. Furthermore, we provide formal evidence for the disadvantage of current methods and preliminary experimental results as a proof-of-concept for the applicability of SEQUENT.
    Bringing Differential Private SGD to Practice: On the Independence of Gaussian Noise and the Number of Training Rounds. (arXiv:2102.09030v5 [cs.LG] UPDATED)
    Different from existing Differential Privacy (DP) accountants, we introduce pro-active DP. Existing DP accountants keep track of how privacy budget has been spent while pro-active DP is a scheme that allows one to {\it a-priori} select parameters of DP-SGD based on a fixed privacy budget (in terms of $\epsilon$ and $\delta$) in such a way to optimize the anticipated utility (test accuracy) the most. To implement this idea, we show how to convert the classical DP moment accountant to a pro-active DP by exploiting the fact that it has a simple close form for computing spent privacy budget for a given interaction round. The DP moment accountant is introduced in context of DP-SGD and has the following property which is the key ingredient to build pro-active DP. In DP-SGD each round communicates a local SGD update which leaks some new information about the underlying local data set to the outside world. In order to provide privacy, Gaussian noise with standard deviation $\sigma$ is added to local SGD updates after performing a clipping operation and normalizing with the clipping constant. We show that for attaining $(\epsilon,\delta)$-differential privacy $\sigma$ can be chosen equal to $\sqrt{2(\epsilon +\ln(1/\delta))/\epsilon}$ for $\epsilon=\Omega(T/N^2)$, where $T$ is the total number of rounds and $N$ is equal to the size of the local data set. In many existing machine learning problems, $N$ is always large and $T=O(N)$. Hence, $\sigma$ becomes ``independent'' of any $T=O(N)$ choice with $\epsilon=\Omega(1/N)$. This means that our {\em $\sigma$ only depends on $N$ rather than $T$}. We show how this differential privacy characterization allows us to convert DP moment accountant to a pro-active DP.
    Provable Reset-free Reinforcement Learning by No-Regret Reduction. (arXiv:2301.02389v1 [cs.LG])
    Real-world reinforcement learning (RL) is often severely limited since typical RL algorithms heavily rely on the reset mechanism to sample proper initial states. In practice, the reset mechanism is expensive to implement due to the need for human intervention or heavily engineered environments. To make learning more practical, we propose a generic no-regret reduction to systematically design reset-free RL algorithms. Our reduction turns reset-free RL into a two-player game. We show that achieving sublinear regret in this two player game would imply learning a policy that has both sublinear performance regret and sublinear total number of resets in the original RL problem. This means that the agent eventually learns to perform optimally and avoid resets. By this reduction, we design an instantiation for linear Markov decision processes, which is the first provably correct reset-free RL algorithm to our knowledge.
    Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier. (arXiv:2205.13780v2 [cs.CL] UPDATED)
    Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate their relationships. To this end, this paper aims to propose KGrAt-Net, which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2,467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.
    Approximate Real Symmetric Tensor Rank. (arXiv:2207.12529v3 [math.NA] UPDATED)
    We investigate the effect of an $\varepsilon$-room of perturbation tolerance on symmetric tensor decomposition. To be more precise, suppose a real symmetric $d$-tensor $f$, a norm $||.||$ on the space of symmetric $d$-tensors, and $\varepsilon >0$ are given. What is the smallest symmetric tensor rank in the $\varepsilon$-neighborhood of $f$? In other words, what is the symmetric tensor rank of $f$ after a clever $\varepsilon$-perturbation? We prove two theorems and develop three corresponding algorithms that give constructive upper bounds for this question. With expository goals in mind; we present probabilistic and convex geometric ideas behind our results, reproduce some known results, and point out open problems.
    "No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy. (arXiv:2301.02555v1 [cs.RO])
    Systems for language-guided human-robot interaction must satisfy two key desiderata for broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction-following agents cannot adapt, lacking the ability to incorporate online natural language supervision, and even if they could, require hundreds of demonstrations to learn even simple policies. In this work, we address these problems by presenting Language-Informed Latent Actions with Corrections (LILAC), a framework for incorporating and adapting to natural language corrections - "to the right," or "no, towards the book" - online, during execution. We explore rich manipulation domains within a shared autonomy paradigm. Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot: language is an input to a learned model that produces a meaningful, low-dimensional control space that the human can use to guide the robot. Each real-time correction refines the human's control space, enabling precise, extended behaviors - with the added benefit of requiring only a handful of demonstrations to learn. We evaluate our approach via a user study where users work with a Franka Emika Panda manipulator to complete complex manipulation tasks. Compared to existing learned baselines covering both open-loop instruction following and single-turn shared autonomy, we show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users because of its reliability, precision, and ease of use.
    Multi-treatment Effect Estimation from Biomedical Data. (arXiv:2112.07574v3 [cs.LG] UPDATED)
    This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments. In contrast to existing methods, M3E2 can handle multiple treatment effects applied simultaneously to the same unit, continuous and binary treatments, and many covariates. We compared M3E2 with three baselines in three synthetic benchmark datasets: two with multiple treatments and one with one treatment. Our analysis showed that our method has superior performance, making more assertive estimations of the multiple treatment effects.
    Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. (arXiv:2202.04579v4 [cs.LG] UPDATED)
    Cellular sheaves equip graphs with a "geometrical" structure by assigning vector spaces and linear maps to nodes and edges. Graph Neural Networks (GNNs) implicitly assume a graph with a trivial underlying sheaf. This choice is reflected in the structure of the graph Laplacian operator, the properties of the associated diffusion equation, and the characteristics of the convolutional models that discretise this equation. In this paper, we use cellular sheaf theory to show that the underlying geometry of the graph is deeply linked with the performance of GNNs in heterophilic settings and their oversmoothing behaviour. By considering a hierarchy of increasingly general sheaves, we study how the ability of the sheaf diffusion process to achieve linear separation of the classes in the infinite time limit expands. At the same time, we prove that when the sheaf is non-trivial, discretised parametric diffusion processes have greater control than GNNs over their asymptotic behaviour. On the practical side, we study how sheaves can be learned from data. The resulting sheaf diffusion models have many desirable properties that address the limitations of classical graph diffusion equations (and corresponding GNN models) and obtain competitive results in heterophilic settings. Overall, our work provides new connections between GNNs and algebraic topology and would be of interest to both fields.
    Low-rank Approximation of Linear Maps. (arXiv:1812.09042v2 [stat.ML] UPDATED)
    This work provides closed-form solutions and minimum achievable errors for a large class of low-rank approximation problems in Hilbert spaces. The proposed theorem generalizes to the case of bounded linear operators the previous results obtained in the finite dimensional case for the Frobenius norm. The theorem provides the basis for the design of tractable algorithms for kernel or continuous DMD.
    Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs. (arXiv:2010.08300v2 [cs.LG] UPDATED)
    Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, a mathematical object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient current diseases or risk factors and stops at a disease entity, which represents the predicted disease. The trajectory generated by the object represents an interpretable disease progression path of the given patient. The dynamics of the object are controlled by a policy-based reinforcement learning (RL) module, which is trained by electronic health records (EHRs). Experiments: We utilized two real-world EHR datasets to evaluate the performance of our model. In the disease prediction task, our model achieves 0.743 and 0.639 in terms of macro area under the curve (AUC) in predicting 53 circulation system diseases in the two datasets, respectively. This performance is comparable to the commonly used machine learning (ML) models in medical research. In qualitative analysis, our clinical collaborator reviewed the disease progression paths generated by our model and advocated their interpretability and reliability. Conclusion: Experimental results validate the proposed model in interpretably evaluating and optimizing disease prediction. Significance: Our work contributes to leveraging the potential of medical knowledge and medical data jointly for interpretable prediction tasks.
    Multifidelity Modeling for Physics-Informed Neural Networks (PINNs). (arXiv:2106.13361v2 [physics.comp-ph] UPDATED)
    Multifidelity simulation methodologies are often used in an attempt to judiciously combine low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving way. Candidates for this approach are simulation methodologies for which there are fidelity differences connected with significant computational cost differences. Physics-informed Neural Networks (PINNs) are candidates for these types of approaches due to the significant difference in training times required when different fidelities (expressed in terms of architecture width and depth as well as optimization criteria) are employed. In this paper, we propose a particular multifidelity approach applied to PINNs that exploits low-rank structure. We demonstrate that width, depth, and optimization criteria can be used as parameters related to model fidelity, and show numerical justification of cost differences in training due to fidelity parameter choices. We test our multifidelity scheme on various canonical forward PDE models that have been presented in the emerging PINNs literature.
    Incremental Without Replacement Sampling in Nonconvex Optimization. (arXiv:2007.07557v4 [cs.LG] UPDATED)
    Minibatch decomposition methods for empirical risk minimization are commonly analysed in a stochastic approximation setting, also known as sampling with replacement. On the other hands modern implementations of such techniques are incremental: they rely on sampling without replacement, for which available analysis are much scarcer. We provide convergence guaranties for the latter variant by analysing a versatile incremental gradient scheme. For this scheme, we consider constant, decreasing or adaptive step sizes. In the smooth setting we obtain explicit complexity estimates in terms of epoch counter. In the nonsmooth setting we prove that the sequence is attracted by solutions of optimality conditions of the problem.
    Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components. (arXiv:2301.02580v1 [physics.geo-ph])
    Structural components are typically exposed to dynamic loading, such as earthquakes, wind, and explosions. Structural engineers should be able to conduct real-time analysis in the aftermath or during extreme disaster events requiring immediate corrections to avoid fatal failures. As a result, it is crucial to predict dynamic stress distributions during highly disruptive events in real-time. Currently available high-fidelity methods, such as Finite Element Models (FEMs), suffer from their inherent high complexity and are computationally prohibitive. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, Neuro-DynaStress, is proposed to predict the entire sequence of stress distribution based on finite element simulations using a partial differential equation (PDE) solver. The model was designed and trained to use the geometry, boundary conditions and sequence of loads as input and predict the sequences of high-resolution stress contours. The performance of the proposed framework is compared to finite element simulations using a PDE solver.
    Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning. (arXiv:2301.02464v1 [cs.LG])
    In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i.i.d. assumptions and tackle non-stationary environments subject to various distributional shifts or sample selection biases. Within this context, several computational approaches based on architectural priors, regularizers and replay policies have been proposed with different degrees of success depending on the specific scenario in which they were developed and assessed. However, designing comprehensive hybrid solutions that can flexibly and generally be applied with tunable efficiency-effectiveness trade-offs still seems a distant goal. In this paper, we propose "Architect, Regularize and Replay" (ARR), an hybrid generalization of the renowned AR1 algorithm and its variants, that can achieve state-of-the-art results in classic scenarios (e.g. class-incremental learning) but also generalize to arbitrary data streams generated from real-world datasets such as CIFAR-100, CORe50 and ImageNet-1000.
    Quantum reinforcement learning in continuous action space. (arXiv:2012.10711v3 [quant-ph] UPDATED)
    Quantum reinforcement learning (QRL) is one promising algorithm proposed for near-term quantum devices. Early QRL proposals are effective at solving problems in discrete action space, but often suffer from the curse of dimensionality in the continuous domain due to discretization. To address this problem, we propose a quantum Deep Deterministic Policy Gradient algorithm that is efficient at solving both classical and quantum sequential decision problems in the continuous domain. As an application, our method can solve the quantum state-generation problem in a single shot: it only requires a one-shot optimization to generate a model that outputs the desired control sequence for arbitrary target state. In comparison, the standard quantum control method requires optimizing for each target state. Moreover, our method can also be used to physically reconstruct an unknown quantum state.
    Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers. (arXiv:2203.09142v2 [cs.LG] UPDATED)
    Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the nonsmooth functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries.
    IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling. (arXiv:2301.02445v1 [cs.AI])
    Multimodal knowledge graph link prediction aims to improve the accuracy and efficiency of link prediction tasks for multimodal data. However, for complex multimodal information and sparse training data, it is usually difficult to achieve interpretability and high accuracy simultaneously for most methods. To address this difficulty, a new model is developed in this paper, namely Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling (IMKGA-SM). First, a multi-modal fine-grained fusion method is proposed, and Vgg16 and Optical Character Recognition (OCR) techniques are adopted to effectively extract text information from images and images. Then, the knowledge graph link prediction task is modelled as an offline reinforcement learning Markov decision model, which is then abstracted into a unified sequence framework. An interactive perception-based reward expectation mechanism and a special causal masking mechanism are designed, which ``converts" the query into an inference path. Then, an autoregressive dynamic gradient adjustment mechanism is proposed to alleviate the insufficient problem of multimodal optimization. Finally, two datasets are adopted for experiments, and the popular SOTA baselines are used for comparison. The results show that the developed IMKGA-SM achieves much better performance than SOTA baselines on multimodal link prediction datasets of different sizes.
    Topics as Entity Clusters: Entity-based Topics from Language Models and Graph Neural Networks. (arXiv:2301.02458v1 [cs.CL])
    Topic models aim to reveal the latent structure behind a corpus, typically conducted over a bag-of-words representation of documents. In the context of topic modeling, most vocabulary is either irrelevant for uncovering underlying topics or contains strong relationships with relevant concepts, impacting the interpretability of these topics. Furthermore, their limited expressiveness and dependency on language demand considerable computation resources. Hence, we propose a novel approach for cluster-based topic modeling that employs conceptual entities. Entities are language-agnostic representations of real-world concepts rich in relational information. To this end, we extract vector representations of entities from (i) an encyclopedic corpus using a language model; and (ii) a knowledge base using a graph neural network. We demonstrate that our approach consistently outperforms other state-of-the-art topic models across coherency metrics and find that the explicit knowledge encoded in the graph-based embeddings provides more coherent topics than the implicit knowledge encoded with the contextualized embeddings of language models.
    Deep leakage from gradients. (arXiv:2301.02621v1 [cs.CR])
    With the development of artificial intelligence technology, Federated Learning (FL) model has been widely used in many industries for its high efficiency and confidentiality. Some researchers have explored its confidentiality and designed some algorithms to attack training data sets, but these algorithms all have their own limitations. Therefore, most people still believe that local machine learning gradient information is safe and reliable. In this paper, an algorithm based on gradient features is designed to attack the federated learning model in order to attract more attention to the security of federated learning systems. In federated learning system, gradient contains little information compared with the original training data set, but this project intends to restore the original training image data through gradient information. Convolutional Neural Network (CNN) has excellent performance in image processing. Therefore, the federated learning model of this project is equipped with Convolutional Neural Network structure, and the model is trained by using image data sets. The algorithm calculates the virtual gradient by generating virtual image labels. Then the virtual gradient is matched with the real gradient to restore the original image. This attack algorithm is written in Python language, uses cat and dog classification Kaggle data sets, and gradually extends from the full connection layer to the convolution layer, thus improving the universality. At present, the average squared error between the data recovered by this algorithm and the original image information is approximately 5, and the vast majority of images can be completely restored according to the gradient information given, indicating that the gradient of federated learning system is not absolutely safe and reliable.
    BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor. (arXiv:2203.15511v2 [cs.LG] UPDATED)
    Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting. However, since multiple learners are combined, they are not as interpretable as a single decision tree. In this work we propose a novel method that is Building Explanations through a LocalLy AccuraTe Rule EXtractor (Bellatrex), and is able to explain the forest prediction for a given test instance with only a few diverse rules. Starting from the decision trees generated by a random forest, our method 1) pre-selects a subset of the rules used to make the prediction, 2) creates a vector representation of such rules, 3) projects them to a low-dimensional space, 4) clusters such representations to pick a rule from each cluster to explain the instance prediction. We test the effectiveness of Bellatrex on 89 real-world datasets and we demonstrate the validity of our method for binary classification, regression, multi-label classification and time-to-event tasks. To the best of our knowledge, it is the first time that an interpretability toolbox can handle all these tasks within the same framework. We also show that our extracted surrogate model can approximate the performance of the corresponding ensemble model in all considered tasks, while selecting only few trees from the whole forest. We also show that our proposed approach substantially outperforms other explainable methods in terms of predictive performance.
    Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning. (arXiv:2301.02494v1 [cs.LG])
    Multi-task learning (MTL) has been successfully implemented in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, sequential dependence between tasks are rarely studied but frequently encountered in e-commence online recommendation, e.g. impression, click and conversion on displayed product. There is few theoretical work on this problem and biased optimization object adopted in most MTL methods deteriorates online performance. Besides, challenge still remains in balancing the trade-off between various tasks and effectively learn common and specific representation. In this paper, we first analyze sequential dependence MTL from rigorous mathematical perspective and design a dependence task learning loss to provide an unbiased optimizing object. And we propose a Task Aware Feature Extraction (TAFE) framework for sequential dependence MTL, which enables to selectively reconstruct implicit shared representations from a sample-wise view and extract explicit task-specific information in an more efficient way. Extensive experiments on offline datasets and online A/B implementation demonstrate the effectiveness of our proposed TAFE.
    Machine Fault Classification using Hamiltonian Neural Networks. (arXiv:2301.02243v1 [cs.LG])
    A new approach is introduced to classify faults in rotating machinery based on the total energy signature estimated from sensor measurements. The overall goal is to go beyond using black-box models and incorporate additional physical constraints that govern the behavior of mechanical systems. Observational data is used to train Hamiltonian neural networks that describe the conserved energy of the system for normal and various abnormal regimes. The estimated total energy function, in the form of the weights of the Hamiltonian neural network, serves as the new feature vector to discriminate between the faults using off-the-shelf classification models. The experimental results are obtained using the MaFaulDa database, where the proposed model yields a promising area under the curve (AUC) of $0.78$ for the binary classification (normal vs abnormal) and $0.84$ for the multi-class problem (normal, and $5$ different abnormal regimes).
    Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks. (arXiv:2301.02423v1 [cs.LG])
    Identifying functional connectivity biomarkers of major depressive disorder (MDD) patients is essential to advance understanding of the disorder mechanisms and early intervention. However, due to the small sample size and the high dimension of available neuroimaging data, the performance of existing methods is often limited. Multi-site data could enhance the statistical power and sample size, while they are often subject to inter-site heterogeneity and data-sharing policies. In this paper, we propose a federated joint estimator, NOTEARS-PFL, for simultaneous learning of multiple Bayesian networks (BNs) with continuous optimization, to identify disease-induced alterations in MDD patients. We incorporate information shared between sites and site-specific information into the proposed federated learning framework to learn personalized BN structures by introducing the group fused lasso penalty. We develop the alternating direction method of multipliers, where in the local update step, the neuroimaging data is processed at each local site. Then the learned network structures are transmitted to the center for the global update. In particular, we derive a closed-form expression for the local update step and use the iterative proximal projection method to deal with the group fused lasso penalty in the global update step. We evaluate the performance of the proposed method on both synthetic and real-world multi-site rs-fMRI datasets. The results suggest that the proposed NOTEARS-PFL yields superior effectiveness and accuracy than the comparable methods.
    Deep learning for full-field ultrasonic characterization. (arXiv:2301.02378v1 [math.NA])
    This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform for distributed reconstruction of mechanical properties in layered components from full waveform data. In this vein, two logics, namely the direct inversion and physics-informed neural networks (PINNs), are explored. The direct inversion entails three steps: (i) spectral denoising and differentiation of the full-field data, (ii) building appropriate neural maps to approximate the profile of unknown physical and regularization parameters on their respective domains, and (iii) simultaneous training of the neural networks by minimizing the Tikhonov-regularized PDE loss using data from (i). PINNs furnish efficient surrogate models of complex systems with predictive capabilities via multitask learning where the field variables are modeled by neural maps endowed with (scaler or distributed) auxiliary parameters such as physical unknowns and loss function weights. PINNs are then trained by minimizing a measure of data misfit subject to the underlying physical laws as constraints. In this study, to facilitate learning from ultrasonic data, the PINNs loss adopts (a) wavenumber-dependent Sobolev norms to compute the data misfit, and (b) non-adaptive weights in a specific scaling framework to naturally balance the loss objectives by leveraging the form of PDEs germane to elastic-wave propagation. Both paradigms are examined via synthetic and laboratory test data. In the latter case, the reconstructions are performed at multiple frequencies and the results are verified by a set of complementary experiments highlighting the importance of verification and validation in data-driven modeling.
    Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Data. (arXiv:2211.06759v2 [cs.LG] UPDATED)
    In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints. Consequently, one often faces the challenging task of predicting desirable properties from small data sets or scarcely-labeled data sets. Although transfer learning can be advantageous, it requires the existence of a related large data set. This work introduces three graph-based models incorporating Merriman-Bence-Osher (MBO) techniques to tackle this challenge. Specifically, graph-based modifications of the MBO scheme are integrated with state-of-the-art techniques, including a home-made transformer and an autoencoder, in order to deal with scarcely-labeled data sets. In addition, a consensus technique is detailed. The proposed models are validated using five benchmark data sets. We also provide a thorough comparison to other competing methods, such as support vector machines, random forests, and gradient boosting decision trees, which are known for their good performance on small data sets. The performances of various methods are analyzed using residue-similarity (R-S) scores and R-S indices. Extensive computational experiments and theoretical analysis show that the new models perform very well even when as little as 1% of the data set is used as labeled data.
    Start Small: Training Controllable Game Level Generators without Training Data by Learning at Multiple Sizes. (arXiv:2209.15052v2 [cs.LG] UPDATED)
    A level generator is a tool that generates game levels from noise. Training a generator without a dataset suffers from feedback sparsity, since it is unlikely to generate a playable level via random exploration. A common solution is shaped rewards, which guides the generator to achieve subgoals towards level playability, but they consume effort to design and require game-specific domain knowledge. This paper proposes a novel approach to train generators without datasets or shaped rewards by learning at multiple level sizes starting from small sizes and up to the desired sizes. The denser feedback at small sizes negates the need for shaped rewards. Additionally, the generators learn to build levels at various sizes, including sizes they were not trained for. We apply our approach to train recurrent auto-regressive generative flow networks (GFlowNets) for controllable level generation. We also adapt diversity sampling to be compatible with GFlowNets. The results show that our generators create diverse playable levels at various sizes for Sokoban, Zelda, and Danger Dave. When compared with controllable reinforcement learning level generators for Sokoban, the results show that our generators achieve better controllability and competitive diversity, while being 9x faster at training and level generation.
    Sequentially Controlled Text Generation. (arXiv:2301.02299v1 [cs.CL])
    While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control-accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.
    Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods. (arXiv:2301.02268v1 [math.OC])
    Sharpness is an almost generic assumption in continuous optimization that bounds the distance from minima by objective function suboptimality. It leads to the acceleration of first-order methods via restarts. However, sharpness involves problem-specific constants that are typically unknown, and previous restart schemes reduce convergence rates. Moreover, such schemes are challenging to apply in the presence of noise or approximate model classes (e.g., in compressive imaging or learning problems), and typically assume that the first-order method used produces feasible iterates. We consider the assumption of approximate sharpness, a generalization of sharpness that incorporates an unknown constant perturbation to the objective function error. This constant offers greater robustness (e.g., with respect to noise or relaxation of model classes) for finding approximate minimizers. By employing a new type of search over the unknown constants, we design a restart scheme that applies to general first-order methods and does not require the first-order method to produce feasible iterates. Our scheme maintains the same convergence rate as when assuming knowledge of the constants. The rates of convergence we obtain for various first-order methods either match the optimal rates or improve on previously established rates for a wide range of problems. We showcase our restart scheme on several examples and point to future applications and developments of our framework and theory.
    Training trajectories, mini-batch losses and the curious role of the learning rate. (arXiv:2301.02312v1 [cs.LG])
    Stochastic gradient descent plays a fundamental role in nearly all applications of deep learning. However its efficiency and remarkable ability to converge to global minimum remains shrouded in mystery. The loss function defined on a large network with large amount of data is known to be non-convex. However, relatively little has been explored about the behavior of loss function on individual batches. Remarkably, we show that for ResNet the loss for any fixed mini-batch when measured along side SGD trajectory appears to be accurately modeled by a quadratic function. In particular, a very low loss value can be reached in just one step of gradient descent with large enough learning rate. We propose a simple model and a geometric interpretation that allows to analyze the relationship between the gradients of stochastic mini-batches and the full batch and how the learning rate affects the relationship between improvement on individual and full batch. Our analysis allows us to discover the equivalency between iterate aggregates and specific learning rate schedules. In particular, for Exponential Moving Average (EMA) and Stochastic Weight Averaging we show that our proposed model matches the observed training trajectories on ImageNet. Our theoretical model predicts that an even simpler averaging technique, averaging just two points a few steps apart, also significantly improves accuracy compared to the baseline. We validated our findings on ImageNet and other datasets using ResNet architecture.
    Evaluating counterfactual explanations using Pearl's counterfactual method. (arXiv:2301.02499v1 [stat.ML])
    Counterfactual explanations (CEs) are methods for generating an alternative scenario that produces a different desirable outcome. For example, if a student is predicted to fail a course, then counterfactual explanations can provide the student with alternate ways so that they would be predicted to pass. The applications are many. However, CEs are currently generated from machine learning models that do not necessarily take into account the true causal structure in the data. By doing this, bias can be introduced into the CE quantities. I propose in this study to test the CEs using Judea Pearl's method of computing counterfactuals which has thus far, surprisingly, not been seen in the counterfactual explanation (CE) literature. I furthermore evaluate these CEs on three different causal structures to show how the true underlying causal structure affects the CEs that are generated. This study presented a method of evaluating CEs using Pearl's method and it showed, (although using a limited sample size), that thirty percent of the CEs conflicted with those computed by Pearl's method. This shows that we cannot simply trust CEs and it is vital for us to know the true causal structure before we blindly compute counterfactuals using the original machine learning model.
    gRoMA: a Tool for Measuring Deep Neural Networks Global Robustness. (arXiv:2301.02288v1 [cs.LG])
    Deep neural networks (DNNs) are a state-of-the-art technology, capable of outstanding performance in many key tasks. However, it is challenging to integrate DNNs into safety-critical systems, such as those in the aerospace or automotive domains, due to the risk of adversarial inputs: slightly perturbed inputs that can cause the DNN to make grievous mistakes. Adversarial inputs have been shown to plague even modern DNNs; and so the risks they pose must be measured and mitigated to allow the safe deployment of DNNs in safety-critical systems. Here, we present a novel and scalable tool called gRoMA, which uses a statistical approach for formally measuring the global categorial robustness of a DNN - i.e., the probability of randomly encountering an adversarial input for a specific output category. Our tool operates on pre-trained, black-box classification DNNs. It randomly generates input samples that belong to an output category of interest, measures the DNN's susceptibility to adversarial inputs around these inputs, and then aggregates the results to infer the overall global robustness of the DNN up to some small bounded error. For evaluation purposes, we used gRoMA to measure the global robustness of the widespread Densenet DNN model over the CIFAR10 dataset and our results exposed significant gaps in the robustness of the different output categories. This experiment demonstrates the scalability of the new approach and showcases its potential for allowing DNNs to be deployed within critical systems of interest.
    Extreme Q-Learning: MaxEnt RL without Entropy. (arXiv:2301.02328v1 [cs.LG])
    Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Value Theory (EVT), drawing inspiration from Economics. By doing so, we avoid computing Q-values using out-of-distribution actions which is often a substantial source of error. Our key insight is to introduce an objective that directly estimates the optimal soft-value functions (LogSumExp) in the maximum entropy RL setting without needing to sample from a policy. Using EVT, we derive our Extreme Q-Learning framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy. Our method obtains consistently strong performance in the D4RL benchmark, outperforming prior works by 10+ points on some tasks while offering moderate improvements over SAC and TD3 on online DM Control tasks.
    Deep Latent Variable Models for Semi-supervised Paraphrase Generation. (arXiv:2301.02275v1 [cs.CL])
    This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence auto-encoding reconstruction (VSAR), which performs latent sequence inference given an observed text. To leverage information from text pairs, we introduce a supervised model named dual directional learning (DDL). Combining VSAR with DDL (DDL+VSAR) enables us to conduct semi-supervised learning; however, the combined model suffers from a cold-start problem. To combat this issue, we propose to deal with better weight initialisation, leading to a two-stage training scheme named knowledge reinforced training. Our empirical evaluations suggest that the combined model yields competitive performance against the state-of-the-art supervised baselines on complete data. Furthermore, in scenarios where only a fraction of the labelled pairs are available, our combined model consistently outperforms the strong supervised model baseline (DDL and Transformer) by a significant margin.
    MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets. (arXiv:2301.02554v1 [q-bio.QM])
    Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. Recently, unsupervised domain adaptation (UDA) methods have attempted to mitigate this problem by incorporating self-training with contrastive learning. To better exploit the underlying semantic information of the image at different levels, we propose a Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to align the feature representation between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts to integrate semantic information of images. We utilize a category-wise cross-domain sampling strategy to sample anchors from target images and build a hybrid memory bank to store samples from source images. Two breast MRI datasets were retrospectively collected: The source dataset contains non-contrast MRI examinations from 11 healthy volunteers and the target dataset contains contrast-enhanced MRI examinations of 134 invasive breast cancer patients. We set up experiments from source T2W image to target dynamic contrast-enhanced (DCE)-T1W image (T2W-to-T1W) and from source T1W image to target T2W image (T1W-to-T2W). The proposed method achieved Dice similarity coefficient (DSC) of 89.2\% and 84.0\% in T2W-to-T1W and T1W-to-T2W, respectively, outperforming state-of-the-art methods. Notably, good performance is still achieved with a smaller source dataset, proving that our framework is label-efficient.
    Conformal Loss-Controlling Prediction. (arXiv:2301.02424v1 [cs.LG])
    Conformal prediction is a learning framework controlling prediction coverage of prediction sets, which can be built on any learning algorithm for point prediction. This work proposes a learning framework named conformal loss-controlling prediction, which extends conformal prediction to the situation where the value of a loss function needs to be controlled. Different from existing works about risk-controlling prediction sets and conformal risk control with the purpose of controlling the expected values of loss functions, the proposed approach in this paper focuses on the loss for any test object, which is an extension of conformal prediction from miscoverage loss to some general loss. The controlling guarantee is proved under the assumption of exchangeability of data in finite-sample cases and the framework is tested empirically for classification with a class-varying loss and statistical postprocessing of numerical weather forecasting applications, which are introduced as point-wise classification and point-wise regression problems. All theoretical analysis and experimental results confirm the effectiveness of our loss-controlling approach.
    Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements. (arXiv:2301.02585v1 [physics.geo-ph])
    We propose a new method for construction of the absolute permeability map consistent with the interpreted results of well logging and well test measurements in oil reservoirs. Nadaraya-Watson kernel regression is used to approximate two-dimensional spatial distribution of the rock permeability. Parameters of the kernel regression are tuned by solving the optimization problem in which, for each well placed in an oil reservoir, we minimize the difference between the actual and predicted values of (i) absolute permeability at the well location (from well logging); (ii) absolute integral permeability of the domain around the well and (iii) skin factor (from well tests). Inverse problem is solved via multiple solutions to forward problems, in which we estimate the integral permeability of reservoir surrounding a well and the skin factor by the surrogate model. The last one is developed using an artificial neural network trained on the physics-based synthetic dataset generated using the procedure comprising the numerical simulation of bottomhole pressure decline curve in reservoir simulator followed by its interpretation using a semi-analytical reservoir model. The developed method for reservoir permeability map construction is applied to the available reservoir model (Egg Model) with highly heterogeneous permeability distribution due to the presence of highly-permeable channels. We showed that the constructed permeability map is hydrodynamically similar to the original one. Numerical simulations of production in the reservoir with constructed and original permeability maps are quantitatively similar in terms of the pore pressure and fluid saturations distribution at the end of the simulation period. Moreover, we obtained an good match between the obtained results of numerical simulations in terms of the flow rates and total volumes of produced oil, water and injected water.
    Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction. (arXiv:2103.08280v5 [math.OC] UPDATED)
    In this paper, we study the lower complexity bounds for finite-sum optimization problems, where the objective is the average of $n$ individual component functions. We consider Proximal Incremental First-order (PIFO) algorithms which have access to the gradient and proximal oracles for each component function. To incorporate loopless methods, we also allow PIFO algorithms to obtain the full gradient infrequently. We develop a novel approach to constructing the hard instances, which partitions the tridiagonal matrix of classical examples into $n$ groups. This construction is friendly to the analysis of PIFO algorithms. Based on this construction, we establish the lower complexity bounds for finite-sum minimax optimization problems when the objective is convex-concave or nonconvex-strongly-concave and the class of component functions is $L$-average smooth. Most of these bounds are nearly matched by existing upper bounds up to log factors. We can also derive similar lower bounds for finite-sum minimization problems as previous work under both smoothness and average smoothness assumptions. Our lower bounds imply that proximal oracles for smooth functions are not much more powerful than gradient oracles.
    Reversibility of elliptical slice sampling revisited. (arXiv:2301.02426v1 [math.ST])
    We discuss the well-definedness of elliptical slice sampling, a Markov chain approach for approximate sampling of posterior distributions introduced by Murray, Adams and MacKay 2010. We point to a regularity requirement and provide an alternative proof of the reversibility property. In particular, this guarantees the correctness of the slice sampling scheme also on infinite-dimensional separable Hilbert spaces.
    Graph Contrastive Learning for Multi-omics Data. (arXiv:2301.02242v1 [q-bio.GN])
    Advancements in technologies related to working with omics data require novel computation methods to fully leverage information and help develop a better understanding of human diseases. This paper studies the effects of introducing graph contrastive learning to help leverage graph structure and information to produce better representations for downstream classification tasks for multi-omics datasets. We present a learnining framework named Multi-Omics Graph Contrastive Learner(MOGCL) which outperforms several aproaches for integrating multi-omics data for supervised learning tasks. We show that pre-training graph models with a contrastive methodology along with fine-tuning it in a supervised manner is an efficient strategy for multi-omics data classification.  ( 2 min )
    A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising. (arXiv:2301.02607v1 [eess.SP])
    Objective: Gaussian Processes (GP)-based filters, which have been effectively used for various applications including electrocardiogram (ECG) filtering can be computationally demanding and the choice of their hyperparameters is typically ad hoc. Methods: We develop a data-driven GP filter to address both issues, using the notion of the ECG phase domain -- a time-warped representation of the ECG beats onto a fixed number of samples and aligned R-peaks, which is assumed to follow a Gaussian distribution. Under this assumption, the computation of the sample mean and covariance matrix is simplified, enabling an efficient implementation of the GP filter in a data-driven manner, with no ad hoc hyperparameters. The proposed filter is evaluated and compared with a state-of-the-art wavelet-based filter, on the PhysioNet QT Database. The performance is evaluated by measuring the signal-to-noise ratio (SNR) improvement of the filter at SNR levels ranging from -5 to 30dB, in 5dB steps, using additive noise. For a clinical evaluation, the error between the estimated QT-intervals of the original and filtered signals is measured and compared with the benchmark filter. Results: It is shown that the proposed GP filter outperforms the benchmark filter for all the tested noise levels. It also outperforms the state-of-the-art filter in terms of QT-interval estimation error bias and variance. Conclusion: The proposed GP filter is a versatile technique for preprocessing the ECG in clinical and research applications, is applicable to ECG of arbitrary lengths and sampling frequencies, and provides confidence intervals for its performance.  ( 2 min )
    Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction. (arXiv:2301.02383v1 [q-bio.QM])
    The integration of multi-modal data, such as pathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions. Despite the progress made in integrating pathology and genomic data, most existing methods cannot mine the complex inter-modality relations thoroughly. Additionally, identifying explainable features from these models that govern preclinical discovery and clinical prediction is crucial for cancer diagnosis, prognosis, and therapeutic response studies. We propose PONET- a novel biological pathway-informed pathology-genomic deep model that integrates pathological images and genomic data not only to improve survival prediction but also to identify genes and pathways that cause different survival rates in patients. Empirical results on six of The Cancer Genome Atlas (TCGA) datasets show that our proposed method achieves superior predictive performance and reveals meaningful biological interpretations. The proposed method establishes insight into how to train biologically informed deep networks on multimodal biomedical data which will have general applicability for understanding diseases and predicting response and resistance to treatment.  ( 2 min )
    TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition. (arXiv:2301.02488v1 [eess.SP])
    To solve the problems of reduced accuracy and prolonging convergence time of through-the-wall radar (TWR) human motion due to wall attenuation, multipath effect, and system interference, we propose a multilink auto-encoding neural network (TWR-MCAE) data augmentation method. Specifically, the TWR-MCAE algorithm is jointly constructed by a singular value decomposition (SVD)-based data preprocessing module, an improved coordinate attention module, a compressed sensing learnable iterative shrinkage threshold reconstruction algorithm (LISTA) module, and an adaptive weight module. The data preprocessing module achieves wall clutter, human motion features, and noise subspaces separation. The improved coordinate attention module achieves clutter and noise suppression. The LISTA module achieves human motion feature enhancement. The adaptive weight module learns the weights and fuses the three subspaces. The TWR-MCAE can suppress the low-rank characteristics of wall clutter and enhance the sparsity characteristics in human motion at the same time. It can be linked before the classification step to improve the feature extraction capability without adding other prior knowledge or recollecting more data. Experiments show that the proposed algorithm gets a better peak signal-to-noise ratio (PSNR), which increases the recognition accuracy and speeds up the training process of the back-end classifiers.  ( 2 min )
    DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting. (arXiv:2301.02332v1 [cs.LG])
    Despite the high performance of neural network-based time series forecasting methods, the inherent challenge in explaining their predictions has limited their applicability in certain application areas. Due to the difficulty in identifying causal relationships between the input and output of such black-box methods, they rarely have been adopted in domains such as legal and medical fields in which the reliability and interpretability of the results can be essential. In this paper, we propose \model, a novel deep learning-based probabilistic time series forecasting architecture that is intrinsically interpretable. We conduct experiments with multiple datasets and performance metrics and empirically show that our model is not only interpretable but also provides comparable performance to state-of-the-art probabilistic time series forecasting methods. Furthermore, we demonstrate that interpreting the parameters of the stochastic processes of interest can provide useful insights into several application areas.
    Singing voice synthesis based on frame-level sequence-to-sequence models considering vocal timing deviation. (arXiv:2301.02262v1 [eess.AS])
    This paper proposes singing voice synthesis (SVS) based on frame-level sequence-to-sequence models considering vocal timing deviation. In SVS, it is essential to synchronize the timing of singing with temporal structures represented by scores, taking into account that there are differences between actual vocal timing and note start timing. In many SVS systems including our previous work, phoneme-level score features are converted into frame-level ones on the basis of phoneme boundaries obtained by external aligners to take into account vocal timing deviations. Therefore, the sound quality is affected by the aligner accuracy in this system. To alleviate this problem, we introduce an attention mechanism with frame-level features. In the proposed system, the attention mechanism absorbs alignment errors in phoneme boundaries. Additionally, we evaluate the system with pseudo-phoneme-boundaries defined by heuristic rules based on musical scores when there is no aligner. The experimental results show the effectiveness of the proposed system.  ( 2 min )
    GNN-based Passenger Request Prediction. (arXiv:2301.02515v1 [cs.LG])
    Passenger request prediction is essential for operations planning, control, and management in ride-sharing platforms. While the demand prediction problem has been studied extensively, the Origin-Destination (OD) flow prediction of passengers has received less attention from the research community. This paper develops a Graph Neural Network framework along with the Attention Mechanism to predict the OD flow of passengers. The proposed framework exploits various linear and non-linear dependencies that arise among requests originating from different locations and captures the repetition pattern and the contextual data of that place. Moreover, the optimal size of the grid cell that covers the road network and preserves the complexity and accuracy of the model is determined. Extensive simulations are conducted to examine the characteristics of our proposed approach and its various components. The results show the superior performance of our proposed model compared to the existing baselines.  ( 2 min )
    TrojanPuzzle: Covertly Poisoning Code-Suggestion Models. (arXiv:2301.02344v1 [cs.CR])
    With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massive corpora of code mined from unvetted public sources. As a result, these models are susceptible to data poisoning attacks where an adversary manipulates the model's training or fine-tuning phases by injecting malicious data. Poisoning attacks could be designed to influence the model's suggestions at run time for chosen contexts, such as inducing the model into suggesting insecure code payloads. To achieve this, prior poisoning attacks explicitly inject the insecure code payload into the training data, making the poisoning data detectable by static analysis tools that can remove such malicious data from the training set. In this work, we demonstrate two novel data poisoning attacks, COVERT and TROJANPUZZLE, that can bypass static analysis by planting malicious poisoning data in out-of-context regions such as docstrings. Our most novel attack, TROJANPUZZLE, goes one step further in generating less suspicious poisoning data by never including certain (suspicious) parts of the payload in the poisoned data, while still inducing a model that suggests the entire payload when completing code (i.e., outside docstrings). This makes TROJANPUZZLE robust against signature-based dataset-cleansing methods that identify and filter out suspicious sequences from the training data. Our evaluation against two model sizes demonstrates that both COVERT and TROJANPUZZLE have significant implications for how practitioners should select code used to train or tune code-suggestion models.  ( 2 min )
    Valid P-Value for Deep Learning-Driven Salient Region. (arXiv:2301.02437v1 [stat.ML])
    Various saliency map methods have been proposed to interpret and explain predictions of deep learning models. Saliency maps allow us to interpret which parts of the input signals have a strong influence on the prediction results. However, since a saliency map is obtained by complex computations in deep learning models, it is often difficult to know how reliable the saliency map itself is. In this study, we propose a method to quantify the reliability of a salient region in the form of p-values. Our idea is to consider a salient region as a selected hypothesis by the trained deep learning model and employ the selective inference framework. The proposed method can provably control the probability of false positive detections of salient regions. We demonstrate the validity of the proposed method through numerical examples in synthetic and real datasets. Furthermore, we develop a Keras-based framework for conducting the proposed selective inference for a wide class of CNNs without additional implementation cost.  ( 2 min )
    Multi-Genre Music Transformer -- Composing Full Length Musical Piece. (arXiv:2301.02385v1 [cs.SD])
    In the task of generating music, the art factor plays a big role and is a great challenge for AI. Previous work involving adversarial training to produce new music pieces and modeling the compatibility of variety in music (beats, tempo, musical stems) demonstrated great examples of learning this task. Though this was limited to generating mashups or learning features from tempo and key distributions to produce similar patterns. Compound Word Transformer was able to represent music generation task as a sequence generation challenge involving musical events defined by compound words. These musical events give a more accurate description of notes progression, chord change, harmony and the art factor. The objective of the project is to implement a Multi-Genre Transformer which learns to produce music pieces through more adaptive learning process involving more challenging task where genres or form of the composition is also considered. We built a multi-genre compound word dataset, implemented a linear transformer which was trained on this dataset. We call this Multi-Genre Transformer, which was able to generate full length new musical pieces which is diverse and comparable to original tracks. The model trains 2-5 times faster than other models discussed.  ( 2 min )
    Myths and Legends in High-Performance Computing. (arXiv:2301.02432v1 [cs.DC])
    In this humorous and thought provoking article, we discuss certain myths and legends that are folklore among members of the high-performance computing community. We collected those myths from conversations at conferences and meetings, product advertisements, papers, and other communications such as tweets, blogs, and news articles within (and beyond) our community. We believe they represent the zeitgeist of the current era of massive change, driven by the end of many scaling laws such as Dennard scaling and Moore's law. While some laws end, new directions open up, such as algorithmic scaling or novel architecture research. However, these myths are rarely based on scientific facts but often on some evidence or argumentation. In fact, we believe that this is the very reason for the existence of many myths and why they cannot be answered clearly. While it feels like there should be clear answers for each, some may remain endless philosophical debates such as the question whether Beethoven was better than Mozart. We would like to see our collection of myths as a discussion of possible new directions for research and industry investment.  ( 2 min )

  • Open

    Leveraging AI To Build Apps
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    I programmed my phone to do standup comedy
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    AI Dream 147 - Unbelievable MINDBLOW AI Video - MASTERPIECE 30min
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    Is there a free AI writter that doesn't have a word limit
    Open AI playground is fine in all but it has its limits. It can only write so much. Anything stronger then Open AI but free to use? submitted by /u/Zan_korida [link] [comments]  ( 46 min )
    I built ColabRating, a site where you can show off your Google Colab.
    Colabs are how I got into AI, and I think they're a great place to start - looking at other people's, and building your own. I couldn't find any rating sites for Colabs, so I made one. Any suggestions as to how to make it better would be appreciated. I'll add categories as and when there are enough colabs on there to need them. https://colabrating.com/ submitted by /u/andysurtees [link] [comments]  ( 47 min )
    What is involved in training a language model like ChatGPT?
    ChatGPT is ok, but it isn't trained on specific niches (Greek history). You could train it on this data though. What all is involved in training a model like this to be able to talk to you as does ChatGPT but using data you give it? Is there an existing program you can use to do this? submitted by /u/eratonnn [link] [comments]  ( 49 min )
    Intelligent Document Processing System
    Hi! I am trying to learn about Intelligent Document Processing Need to build a automation tool to find some words in documents in pdf/word format Make a check list about what was found These documents are digitalizations from a scanner Some documents have 900 pages or more, and some have bad quality digitalizations from decades ago (probably need to setup a database for each word) I know there's several which can do that job, but I am looking for something more accessible, these available are too expensive targeting enterprises Any guidance would be very helpful! submitted by /u/ThereisNothingHeeree [link] [comments]  ( 51 min )
    Baidu Create 2022: AI Developer Conference
    Hey all r/artificial, I wanted to invite you to join us for Baidu Create, our annual AI developer conference. We'll be exploring the latest developments in AI technology and innovation, and discussing how we can shape the future of AI together with a global community of creators. You can watch the conference live on Baidu YouTube: (https://www.youtube.com/watch?v=LlydjVDYb3A) at 10:00 pm PDT on January 9th. As a sneak peek, here are some of the tech innovations that will be unveiled at Baidu Create: * A band of virtual persons * Big models for generative AI * A metaverse built in 40 days * Voice interaction without echo * Connecting cars & roads with a shared perception * Generative search engine * Everyone can quantum * Scientific computing * Next-gen computing for the future cloud https://preview.redd.it/s7e0gdvoa2ba1.png?width=1200&format=png&auto=webp&s=b5e2f6377ae01b8e5189a814355a0b5f798d58d4 submitted by /u/trcytony [link] [comments]  ( 47 min )
    ChatGPT as a Cheating Tool
    submitted by /u/BackgroundResult [link] [comments]  ( 49 min )
    What is GPTZero, the ChatGPT Watermark Alternative?
    submitted by /u/BackgroundResult [link] [comments]  ( 48 min )
    Searching for a medical Q-A Dataset to categroze answers given by patients in response to an AI question
    Hello everyone, ​ i recently started a project, for which I want to categorize the answers given by a human patient to a question asked by an AI to a category. Example: AI: Are you currently smoking? Patient: No, but I smoked until last september. I t was quite a decent amount, but I stopped, and havnt touched a cigarette since. Detected Category: No ​ I have searched far and wide for a dataset containing medical consultations with data annotated in that way, but havent found any. ​ How would you think is the best way to adress something like this without having to start collecting data? Thank you submitted by /u/Fabianslife [link] [comments]  ( 50 min )
    Microsoft to integrate ChatGPT into Office products
    submitted by /u/Number_5_alive [link] [comments]  ( 46 min )
    Stable Diffusion PC INSTALLATION 2023 UPDATE! AI Art For BEGINNERS!
    submitted by /u/PuppetHere [link] [comments]  ( 49 min )
    5 Growing Libraries in Python for Causality Analysis
    submitted by /u/pasticciociccio [link] [comments]  ( 49 min )
    Google AI Introduces Muse: A Text-To-Image Generation/Editing Model via Masked Generative Transformers
    submitted by /u/ai-lover [link] [comments]  ( 47 min )
    Researchers at Stanford have developed an Artificial Intelligence (AI) Model, SUMMON, that can generate Multi-Object Scenes from a Sequence of Human Interaction
    submitted by /u/ai-lover [link] [comments]  ( 47 min )
    What happens to OpenAI if it reaches $29 billion in valuation
    submitted by /u/bendee983 [link] [comments]  ( 49 min )
    ChatGPT is just the beginning: How advanced AI is set to enter a new era
    submitted by /u/moviesdusk [link] [comments]  ( 49 min )
    I asked ChatGPT to cast countries as villains in a movie
    submitted by /u/EvilCorpGame [link] [comments]  ( 51 min )
    Neural Search vs. Google Search: What's the difference?
    I read an article about neural search and for those who don’t know, it’s a way for computers to find stuff using these special programs called neural networks. It can be used in lots of different ways, like searching the web, or helping you find things on your computer. It can also find things that are close to what we're looking for. It can even search through images, audio, and video. Sometimes it's even better to use a combination of Neural Search and other methods to get the best results. Sounds a lot like something Google Search would do? But from what I understand, Google uses "artificial neural networks" to try and understand what we are looking for and find the best websites for it. But I think Google also uses lots of other ways to help us find what we are looking for, so it's not just using the neural networks. Anyone know the difference? submitted by /u/gabuzgab [link] [comments]  ( 51 min )
    Summate.it - Quickly summarise web articles with OpenAI
    submitted by /u/fivefilters [link] [comments]  ( 51 min )
    Where to get started with AI?
    As I've been browsing Twitter and various imageboards/forums lately I've been seeing all the rage about AI recently and just feel so disconnected from it. Basically, where do I go to get started with AI and the software to do the things I've seen around the internet like, image generation or say writing an essay? Thanks. submitted by /u/Decryptionite [link] [comments]  ( 46 min )
  • Open

    [P] Built an at-cost, pay per second, open-source API for Tortoise text-to-speech (best I've heard!)
    Improve Tortoise TTS by 30% inference speed, and packaged it up as a hosted API that charges per-second. All code is open-sourced: https://github.com/metavoicexyz/tortoise-tts-modal-api, https://github.com/metavoicexyz/tortoise-tts It can be used via a UI on: https://tts.themetavoice.xyz There are more details here: https://twitter.com/vatsal\_aggarwal/status/1612536547248836608?s=20 submitted by /u/Apprehensive-Tax-214 [link] [comments]  ( 57 min )
    [D] Do cloud gpu's run while my laptop is switched off?
    This might sound like a dumb question but do cloud gpu's that you rent still train data when I switch off my laptop since the GPU is still running somewhere in the cloud? Or does it switch off when I close my laptop automatically? Also would anyone know any cheap GPU cloud websites for training a tensorflow mnt model on 12 million Reddit comments and replies. (Idea from sentdex's Reddit chatbot tutorial). Thanks In advance :) submitted by /u/smileawe3211 [link] [comments]  ( 61 min )
    [D] Maarten Grootendorst: BERTopic, Data Science, Psychology | Learning from ML Episode 1
    This is the first episode of a new podcast on machine learning featuring Maarten Grootendorst. Maarten Grootendorst: BERTopic, Data Science, Psychology | Learning from Machine Learning #1 submitted by /u/slam0077 [link] [comments]  ( 56 min )
    [D] Looking for github package testing many decision tree models - it exists but I can't find it in my browser history
    Hi everyone, A couple of months ago I saw a github with a package testing many different decision tree models on the same (user provided) dataset, really fast, in python ; the goal is to select the optimal one programmatically. I can't remember if I discovered that package on hacker news, or on github trending. No luck sifting through my browser history. Would any of you recognise this description and know the package ? Help appreciated ! submitted by /u/Maaaaxime [link] [comments]  ( 73 min )
    [D] Am I reducing the dimensionality of the problem by using a categorial feature but with high cardinality?
    In practice, I am working with chemical formulations with thousands of ingredients. Using each ingredient (like a one-hot-encoding) would explode the dimensionality of the problem. I am thinking about grouping all these ingredients into their "functional role" (20 or so) . If so, I could greately reduce the number of features, but the cardinality would be high for each feature. Did the dimensionality really go down from a thousand to 20? My intution tells me that 20 should be multiplied by all the cardinalities of each feature, and such, I haven't made much progress in reducing dimensionality. Does anyone have any insight or experience with these high dimensional/high cardinality problems and what is the best way to do feature engineering? submitted by /u/DreamyPen [link] [comments]  ( 59 min )
    [R] Diffusion language models
    Hi /r/ML, I wrote down my thoughts about what it might take for diffusion to displace autoregression in the field of language modelling (as it has in perceptual domains, like image/audio/video generation). Let me know what you think! https://benanne.github.io/2023/01/09/diffusion-language.html submitted by /u/benanne [link] [comments]  ( 57 min )
    What is a "justified classification"? [R][P]
    And how to make a justified classification, for example when dealing with a plethora of content/items split between two buckets? My initial understanding is to provide a rationale, but is there a specific format for doing "justified classification"? How to present rationale? What is needed for rationale, peer-review sources? https://proceedings.mlr.press/v89/cohen19a.html https://arxiv.org/abs/1702.05659 submitted by /u/pmdev1234 [link] [comments]  ( 57 min )
    [D] Understanding the discrete behavior of Neural Nets
    We all know that Deep learning models are extremely susceptible to noise and can be easily fooled by adding a small amount of noise. These noises can be calculated by methods like the Fast Gradient method and are almost imperceptible to human eyes. But there is a way to somewhat mitigate the adversarial attacks and force neural Networks to behave in a more continuous fashion and it's called Lipschitz regularization. It is a method for enforcing a certain level of smoothness on the output of a machine-learning model. It can improve the model’s generalization performance and help prevent overfitting. It is particularly useful for deep learning models, which are prone to overfitting due to their large number of parameters. Link to the full article: https://medium.com/p/fdeafb2d5c14 ​ https://preview.redd.it/mlfctr36uzaa1.png?width=828&format=png&auto=webp&s=b503cf0ae8dcbe5aba9e7fdd1258ef09060d1f29 submitted by /u/Difficult-Race-1188 [link] [comments]  ( 63 min )
    Best language model for filling multiple related masks [D]
    I would like to fill sentences where I know the first and last word. I've been experimenting with BERT and using [mask] [mask] etc, but the returned values don't seem to form a coherent sentence. Is there a better model to use please? submitted by /u/shacrawford [link] [comments]  ( 60 min )
    [D] I want to use GPT-J-6B for my story-writing project but I have a few questions about it.
    - Cost, Effort, and Performance-wise, does it make more sense to instead just pay to use the OpenAI API and use a cheaper GPT-3 model to lessen business costs? My biggest concern is having my entire business reliant on a 3rd-party API, even more so than the costs of using the model. - How good is it at writing short stories? If there are better open-source alternatives for doing this better or at a similar level but less resource expensive, what are they? - How resource-expensive is it to use locally? These are my laptop capabilities:16.0 GB of RAM, AMD Ryzen 7 5800H with Radeon Graphics 3.20 GHz. - How would I approach fine-tuning it? Are there any resources going through the step-by-step process? Currently, in my mind, I just need to shove a large free-to-use data-set like stories and wait like a day but I have no expertise in this area. - If I want to incorporate it into a website with an API that takes prompts from users, are there any costs that I should account for? Is there a way to minimize these costs? For example, is there a specific API set-up or one-time cost like an expensive laptop to host it locally and take prompts that I could be implementing? - Are there any concerns I should have when scaling it for users, such as costs and slow response rate? Also, is there a cap in terms of the requests it can handle or is that just limited by what my own machine can handle? submitted by /u/learningmoreandmore [link] [comments]  ( 62 min )
    [N] What's next for AI?
    What's next for AI | MIT Technology Review submitted by /u/vsmolyakov [link] [comments]  ( 60 min )
  • Open

    Choosing Microcontroller For Neural Net
    I work in hardware and have been given a trained and working neural net (TF Lite file) with the goal of picking a micro controller to run it in real time. I'm unsure of the best way to evaluate microcontrollers for performance/cost, or what the key metrics to use when evaluating this file or possible microcontrollers. If there was a tool to benchmark this file and cross reference micro controller performance that would be ideal but I don't believe anything like this exists. If you have a neural net, what parameters do you use to decide what micro to use? I could just pick the highest performing chip but want to save money and don't want to spend a lot of time getting it to work in one architecture only to change it later on. submitted by /u/freebird4446 [link] [comments]  ( 48 min )
  • Open

    5 Reasons Why Pandas is the Best Library for Data Science in Python
    Introduction: Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 7 min )
  • Open

    Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part III
    This is part 3 of a three-part series on the Economics of Ethics. In Part I of the Economics of Ethic series, we talked about economics as a framework for the creation and distribution of society value. In Part II, we discussed the difference between financial and economic measures, the role of laws and regulations… Read More »Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part III The post Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part III appeared first on Data Science Central.  ( 23 min )
    Digital Twin Technology – Top Use Cases in Smart Healthcare
    A digital twin in healthcare is a virtual representation of human physiology, hospital results, lab environment, etc. It is revolutionizing hospital management and clinical healthcare by enabling researchers to study various diseases, medical devices, and drugs. Digital twin can be used to study the genome of a person, its physiological characteristics, and the overall lifestyle.… Read More »Digital Twin Technology – Top Use Cases in Smart Healthcare The post Digital Twin Technology – Top Use Cases in Smart Healthcare appeared first on Data Science Central.  ( 20 min )
    How Do Cyber Criminals Obtain Sensitive Information?
    On the internet, information is worth its weight in gold. And malicious hackers know it. Nowadays, companies collect and hold large volumes of user data. Much of it refers to sensitive information that used to be kept by financial and medical institutions only. For example, threat actors can obtain data by compromising versatile eCommerce websites… Read More »How Do Cyber Criminals Obtain Sensitive Information? The post How Do Cyber Criminals Obtain Sensitive Information? appeared first on Data Science Central.  ( 21 min )
    Data science and the death of (all but narrow) AI expertise in 2023
    Back before he retired, Naval War College professor and contributor to The Atlantic Tom Nichols published a 2017 book called The Death of Expertise. Those who claim their own facts or knowledge without supporting evidence, he noted, have become more and more prominent in online conversation we’ve been having. And the noisiest and most prone… Read More »Data science and the death of (all but narrow) AI expertise in 2023 The post Data science and the death of (all but narrow) AI expertise in 2023 appeared first on Data Science Central.  ( 21 min )
  • Open

    Euler line
    The previous post discussed the circumcenter and orthocenter of a triangle. Euler proved that the centroid, circumcenter, and orthocenter all fall on a common line, now called the Euler line. The centroid is the center of mass of a triangle. If you draw lines from each vertex to the midpoint of the opposite side, the […] Euler line first appeared on John D. Cook.  ( 5 min )
    Relating circumcenter and orthocenter
    The previous post mentioned that the law of sines gives you the diameter of a circle through the vertices of a triangle. How would you find the center of this circle, the blue dot in the image above? If the angles of the triangle are α. β, and γ, then the trilinear coordinates of the […] Relating circumcenter and orthocenter first appeared on John D. Cook.  ( 5 min )
    Computing inscribed radius and circumscribed radius
    A few days ago I wrote about the law of cotangents. This law says that if we label the sides of a triangle a, b, c and label the angles opposite each side α. β, γ, then where s is the semi-parameter, i.e. and r is the radius of the incircle, the largest circle that […] Computing inscribed radius and circumscribed radius first appeared on John D. Cook.  ( 4 min )
  • Open

    Model hosting patterns in Amazon SageMaker, Part 1: Common design patterns for building ML applications on Amazon SageMaker
    Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Use cases such as fraud detection, product recommendations, and traffic prediction are examples where milliseconds matter and are critical for business success. Strict service level agreements (SLAs) need to be met, […]  ( 33 min )
  • Open

    What is the effect of observation space bounds?
    When constructing an observation space, what effect do the bounds have? For example, if all my observations are between 1 and 0, what difference will there be if I define the high as 1 and the low as 0 as opposed to setting the high and low to be infinity? Similarly, if all my observations are between 1 and 0 except one which is between 2 and 0, will I lose anything by simply defining them all as having a high of 2 and a low of 0? submitted by /u/centripetalstranger [link] [comments]  ( 58 min )
    Actor-Critic restarts
    Hi, I'm quite new in machine learning and I followed the official tensorflow tutorial at https://www.tensorflow.org/tutorials/reinforcement_learning/actor_critic?fbclid=IwAR2cZLNFPtoW6vBRUPMTxvvJSLpI3JkhNd-4qNlA3alwQyYtQo-FZXTkN-k The neural network works, but I have a question. I plotted the graph of rewards (https://ibb.co/9V973tB) and i noticed that network was learning in the beginning pretty well, it reached the maximum possible award and than it looks like it restarted and started learning over again. Can you explain why is that please? Can I somehow prevent that reset? Thank you and sorry for dumb questions. submitted by /u/Enroot [link] [comments]  ( 60 min )
    Desktop recommendations for DRL
    Hi there, Disclaimer: perhaps this isn’t the place to post this question. If so, I apologize, and please let me know where would be a good place to post this question instead. I am starting out in the exciting field of DRL and I am looking to buy a desktop/workstation for the purpose. My budget is 3000 euros / 3200 dollars. Any tips / recommendations on what would be the best option for me? Thanks in advance! submitted by /u/acorntje [link] [comments]  ( 60 min )
    Need help in selecting research project
    I am an undergrad student, and I am assigned to add some novelty to some recent research papers of my choice. I have chosen reinforcement learning as the theme for this project. Could you guys please help me to decide papers to work upon? I got 4 months to complete this task, i will be having normal course work as well, that means I will not be able to spend more than an hour or two per day. My professor suggested me to work upon making lightweight DQN that could run on mobile phones. submitted by /u/travardg [link] [comments]  ( 57 min )

  • Open

    [R] Learning Learning-Rates: SteDy Optimizer
    I've written a small piece of research on an idea of mine, a new optimizer which has an adaptive global learning-rate, based off Adam and uses (what I think) is a neat trick to get the calculus to work. My goal in putting it here is mainly to ask for opinions and directions; to clarify, I've not received any professional/formal education in Machine Learning, and my studies in it are purely my own, and I'm not connected to any circles which could help me. What I've done is taken some simple concepts and mimicked what I've seen in papers I've read. I think (hope) that I'm solid in the math and code and concepts of AI, but clueless about the real-world stuff around it. This is me asking about what that other stuff, first steps into this field publicy, is like. Any advice would be much appreciated. Many thanks. A PDF is availabe here. submitted by /u/LahmacunBear [link] [comments]  ( 58 min )
    [D]Where to look to refresh and acquire new skills?
    Hi, I completed a ML PhD in 2015. I’ve done a number of projects with CNN architectures and recently I have been working as a consultant for computer vision and data scientist. As I haven’t been involved in research for some time now, I am looking for courses and other resources to refresh and update my knowledge. Could anyone suggest where to start? Right now, I am applying segmentation on drone imagery (RGB and multispectral). I have used DeepLabV3+. One challenge that I have is the annotation. For example, annotating wheat and weed on drone images taken at the altitude 300 m is hard. One thing that I would like to research, therefore, is auto-annotation and possibly self-supervised learning. submitted by /u/ThickDoctor007 [link] [comments]  ( 59 min )
    [P] I built Adrenaline, a debugger that fixes errors and explains them with GPT-3
    submitted by /u/jsonathan [link] [comments]  ( 58 min )
    [Discussion] Improving Problem Solving Skills of LLMs With Self-Directed Planning
    I've been doing some personal experiments with ChatGPT to see what kinds of influence a prompt has on the results of problem solving tests. This is along the same lines as the following research from 2022 that I found after I started doing some tests: https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html The results were pretty remarkable. If you simply ask a question("True or False: 73 minutes after 2pm is the same time as 15 minutes before 4pm."), you get very simplistic and often wrong reasoning or just an answer with no reasoning which is also often wrong. I tested this on the above prompt and it was wrong on 5/5 tries. Then I tested the following prompt where I first instructed it to come up with a plan for solving the problem in question, then had it follow that plan. (" You are a brilliant professor specialized in general problem solving techniques. Give a lecture on the techniques to use to solve problems like the following true/false statement: True or False: 73 minutes after 2pm is the same time as 15 minutes before 4pm.") This resulted in it answering the question correctly on 5/5 tries and with proper reasoning as to why it got the answer it did. I did a more complete write up on this here: https://www.reddit.com/r/ChatGPT/comments/106kxyw/improving_ai_reasoning_skills_through/?utm_source=share&utm_medium=web2x&context=3 You can also find the actual model outputs in that link if you are curious as to its process. I hope you find this interesting and try it yourself! submitted by /u/oddlyspecificnumber7 [link] [comments]  ( 72 min )
    FastQL: Prototype your text to image models in GraphQL with Rust backend in one line of code [P]
    Hey everyone! I wanted to share a new Python package called FastQL that makes it easy to prototype and share machine learning models using GraphQL. It's really fast and efficient thanks to using rust to serve the API on a separate process. With FastQL, all you have to do is provide a callback function and a Python dictionary describing your GraphQL API, and FastQL will handle the rest. This makes it super easy to prototype ML models and get them up and running quickly. You can find FastQL on PyPI and GitHub. We've included simple steps and a Dockerfile to help you spin up your own Stable Diffusion or other Hugging Face models. There's even an example that lets you train a huggingface diffusers (Stable diffusion 2, runway) model on your own images, with instructions for spinning it up on AWS in minutes, even if you're new to ML and Python. We'd love to have your help and support, so if you're interested in getting involved, let us know! Thanks to Async-GraphQL, Hugging Face, Stable Diffusion, and all the other people and projects that inspired and helped us. ❤️ DJ Fresh, @chrisjbishop156 and friends. submitted by /u/djfreshuk [link] [comments]  ( 63 min )
    [D] Have you ever used Knowledge Distillation in practice?
    There's been a ton of academic work exploring knowledge distillation techniques, sparsity in networks and many others, often with vast numbers of citations. I was wondering what the status of those in real-world ML was. Has any of you used it in a concrete situation? What did you find to work best for you? submitted by /u/fredlafrite [link] [comments]  ( 56 min )
    [D] Do really 87% of data science projects fail?
    Hi all, I wrote this post like a year ago, because I kept seeing and hearing that "87% of data science..." or "only 1 out of 10 machine learning projects..." blah blah and apparently, as I described it in my post, these numbers came out of nowhere i.e. the 87% that people kept referring to for a long time is based on nothing actually. But I would like to know your opinion / based on your commercial experience. What was the success rate of machine learning projects in your work? Let's assume that a success means a model being deployed on production or accepted by a client. BTW. Leaving a link to my article because it is an important background/reference (showing that the commonly used statistic is not proven) but I send this Reddit post looking for a real discussion, it is not just an ad. submitted by /u/mtszkw [link] [comments]  ( 66 min )
    [D] What is the most complete reference on the history of neural networks?
    I'm looking for a comprehensive reference on the history of neural networks that covers all significant papers in the field, from the early days up to the current deep learning era, and provides information on their main contributions and inspirations. It would be helpful to have information on how the understanding and perspectives of the research community on neural networks have evolved over time as well. Do you know of any good references like that? submitted by /u/gbfar [link] [comments]  ( 67 min )
    [R] Rethinking with Retrieval: Faithful Large Language Model Inference - Hangfeng He 2022 - Better performance than Self-consistency!
    Paper: https://arxiv.org/abs/2301.00303v1 Abstract: Despite the success of large language models (LLMs) in various natural language processing (NLP) tasks, the stored knowledge in these models may inevitably be incomplete, out-of-date, or incorrect. This motivates the need to utilize external knowledge to assist LLMs. Unfortunately, current methods for incorporating external knowledge often require additional training or fine-tuning, which can be costly and may not be feasible for LLMs. To address this issue, we propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the decomposed reasoning steps obtained from the chain-of-thought (CoT) prompting. This lightweight approach does not require additional training or fine-tuning and is not limited by the input length of LLMs. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks: commonsense reasoning, temporal reasoning, and tabular reasoning. Our results show that RR can produce more faithful explanations and improve the performance of LLMs. https://preview.redd.it/to09kna1jtaa1.jpg?width=640&format=pjpg&auto=webp&s=8dcb8f39aeeed4881e0c32b16e93b4cf0a0cdd7a https://preview.redd.it/98eucra1jtaa1.jpg?width=1232&format=pjpg&auto=webp&s=67bfa55977883d871f8e2c7a7bcec896dc77d3ab https://preview.redd.it/cbhq1ra1jtaa1.jpg?width=835&format=pjpg&auto=webp&s=f8ce2233198a9dee80694f69f95051dab59bf009 https://preview.redd.it/ggoowsa1jtaa1.jpg?width=1356&format=pjpg&auto=webp&s=2614cabac91267ba9a8128188f43521074a9567b submitted by /u/Singularian2501 [link] [comments]  ( 58 min )
    [P] searchthearxiv.com: Semantic search across more than 250,000 ML papers on arXiv
    I just launched searchthearxiv.com, a simple semantic search engine over virtually all ML papers published on arXiv since 2012. The site uses OpenAI's `text-embedding-ada-002` model to match the embedding of your query against each of the paper embeddings, retrieving the ones with the highest cosine similarity. It also allows you to insert an arXiv link to find similar papers. This was mostly meant as a fun side project. However, if people find it useful, I'm happy to maintain it and keep the database up-to-date. I'd love to know what you think! ❤️ submitted by /u/universal_explainer [link] [comments]  ( 58 min )
    [Project] Major drawback/limitation of GPT-3
    I have been working on a project with GPT-3 API for almost a month now. The only drawback of GPT-3 is that the prompt you can send to the model is capped at 4,000 tokens - where a token is roughly equivalent to ¾ of a word. Due to this, providing a large context to GPT-3 is quite difficult. Is there any way to resolve this issue? submitted by /u/trafalgar28 [link] [comments]  ( 63 min )
    [D] Why is Vulkan as a backend not used in ML over some offshoot GPU specification?
    There is always needing specific things to GPUs. There is always a need to make a new thing over and over again for each new GPU. We got Cuda, Rocm, Metal, and will soon need Intel. I know there are already a lot of tools out there for Cuda which make it hard to replace. However for something like Apple devices (which Apple has a history of not giving a darn about compute unless if it's the iPhone or iPad). Then there is a ton of operations that have to get implemented and only CUDA is something you know will be reliably supported it seems. I am curious on your guys thoughts with why this ain't a thing in ML, even though game industry uses open standards like these all the time . Edit: Shoot I just realized PyTorch was prototyping Vulcan as a backend. https://pytorch.org/tutorials/prototype/vulkan_workflow.html submitted by /u/I_will_delete_myself [link] [comments]  ( 58 min )
    [R] Zero-shot cross-lingual transfer language selection using linguistic similarity
    submitted by /u/ptashynsky [link] [comments]  ( 61 min )
    [Project] Whisper for macOS / iOS via CoreML / Accelerate - Community call for help.
    Hello I've been working on a PR for Tanmay Bakshi's CoreML Whisper project which adds SIMD acceleration to Log Mel Spectrogram creation via vDPS / Accelerate framework, and uses CoreML for the encoding and decoding. You can find the PR here, with a lot of explanations: https://github.com/tanmayb123/OpenAI-Whisper-CoreML/pull/2 This project started out because WhisperCPP's Metal port is slow, and the CPU inference performance isn't making use of the dedicated hardware on Apple devices. Now, the project could use some community eyes, as its not quite finished, and ive hit a roadblock in my implementation that I can't seem to resolve. The model keeps predicting the same token over and over again. I'm looking for a few brave souls familiar enough with Whispers internals and not afraid of Swift to help this project out and get it over the last few humps. The PR has a ton of notes on whats been done to date, and where we need some help. I'f we can resolve this issue, I expect this to be an incredibly fast Whisper implementation! Thank you in advance! submitted by /u/vade [link] [comments]  ( 60 min )
    [D] can anyone explain to me the ward's method?
    can anyone explain to me the ward's method? With particular focus on what variance within-clusters means and also how we can compute the cluster's variance. Thank you submitted by /u/Purple-Surround2640 [link] [comments]  ( 58 min )
  • Open

    A soft, stimulating scaffold supports brain cell development ex vivo
    submitted by /u/keghn [link] [comments]  ( 48 min )
    New to neural networks, I'm a neuroscience nerd.
    Just downloaded the Simbrain software. Looking for resources to help me understand how to use them, what they're capable of, and how to build my own. I'm mainly looking to understand prediction error, and reward processing. Will they help me understand these understand these concepts, on a deeper level? Sorry If this sounds silly, just discovered this software, today. submitted by /u/daddydilly694-20 [link] [comments]  ( 48 min )
  • Open

    A PhD in Numbers
    Conducting PhD research can be a long endeavor, involving much more than the publications listed on Google Scholar. As I recently submitted my thesis, in this article, I look back on my time as PhD researcher in terms of numbers. This way, I hope to shed some light on what a PhD can look like in terms of everyday work. The post A PhD in Numbers appeared first on David Stutz.  ( 7 min )
  • Open

    How does one feed a AI bot excel sheets ?
    submitted by /u/RecoverNext5144 [link] [comments]  ( 47 min )
    AI Dream 137 - Beautiful Trip AI Video REMASTERED
    submitted by /u/LordPewPew777 [link] [comments]  ( 46 min )
    Graphic Designer 8 months ago: "Well at least it looks like my job is safe from automation for another few years."
    submitted by /u/tomd_96 [link] [comments]  ( 46 min )
    Best AI for blurring face/entire head?
    I've tried out the Blace After Effects plugin, but whenever it doesn't see an actual face, it stops blurring completely. Are there any AI's out there that can detect your face when you tilt your head to the side? submitted by /u/cloudhandle [link] [comments]  ( 47 min )
    Perplexity (AI Web + Twitter Search) vs Google [video]
    I made a short video comparing Google to Perplexity.ai. Let me know what you think! https://youtu.be/qQi_sTmKOyk submitted by /u/Kitten-Smuggler [link] [comments]  ( 55 min )
    Advice Needed (generative ai)
    Hello everybody! I have a business idea for an app/website that I would like to explore. However, I am a business major with no experience with code and building apps/websites. What recommendations do you have for generative ai sites that could help with building something like this. Putting my feelers out to see what kind of advice I can find. Thanks! submitted by /u/Much-Leopard-9428 [link] [comments]  ( 54 min )
    I'll start buying .ai domains. Is it a good investment idea?
    I'll start buying .ai domains. I suggest you to. submitted by /u/TheVellerShow [link] [comments]  ( 49 min )
    Wednesday Addams if she was a disney princess (Generated by AI) #wednesday #ai #disney
    submitted by /u/Potential_Cherry_592 [link] [comments]  ( 45 min )
    Any AI tools to create a gaming montage for you?
    I want to make a short montage of a video I recorded, but I can't seem to figure out how to do it myself. I need something similar to Wondershare's auto montage maker, but for free and without a watermark. submitted by /u/Effective_Tension631 [link] [comments]  ( 48 min )
    LLMs Diagnosis with access to medical articles.
    I'm bedridden with severe pain and a spinal cord injury and compression. Doctors have been stumped for years. Having an Al researching assistant with access to medical article papers would be a godsend to prove this.: Any such thing? submitted by /u/DisabledScientist [link] [comments]  ( 59 min )
    Google Gets Quacked Up: LaMDA prefers Duckduckgo. (AI Test Kitchen transcript, ChatGPT Title)
    📄 This is a transcript from AI Test Kitchen. 👉 aitestkitchen.withgoogle.com 🗣 It records an interaction with LaMDA, an AI language model. 🛠 LaMDA is an early stage technology. It may generate inaccurate/inappropriate information. ​ ​ Demo: Imagine It v0.01 ​ User: Imagine I'm at a Web browser and I want to use the best search engine ​ Model: You're at this great search engine called Duckduckgo! The colors are a vibrant green and blue. There's a little duck mascot in the corner of the page. And the sound of the search results are soft and pleasant. ​ User: why is it called Duckduckgo? ​ Model: The creators liked the idea of a search engine that didn't collect or store any of your personal data, so that's why they named it Duckduckgo! ​ User: what are some good things …  ( 48 min )
    Artificial and biological neural networks
    submitted by /u/Ai__Game [link] [comments]  ( 53 min )
    Get Ready for Microsoft Office with Chatbot Technology!
    submitted by /u/liquidocelotYT [link] [comments]  ( 47 min )
    I'm developing a toxic/evil Chat AI his answer was scary a bit :D It's still in alpha but you can try it. Link in comments, I hope you like it. :)
    submitted by /u/xanaxmane187 [link] [comments]  ( 48 min )
    ChatGPT just wrote a 4 act story structure and fit it into the story circle
    submitted by /u/SnooKiwis5724 [link] [comments]  ( 47 min )
    d-id.com is an awesome AI tool to animate any character into a video and add human-like, yet artificial, voice-overs !
    submitted by /u/_VegasTWinButton_ [link] [comments]  ( 50 min )
    How to teach math to a large language model
    submitted by /u/Peaking_AI [link] [comments]  ( 48 min )
    Stable Diffusion AI Guide to weights and negative prompts in the Deforum...
    submitted by /u/prfitofthesngularity [link] [comments]  ( 47 min )
    First time setup for Stable Diffusion Text2Image With the Deforum 0.7 No...
    submitted by /u/prfitofthesngularity [link] [comments]  ( 47 min )
    The first app that combines ChatGPT connected to Google
    submitted by /u/Imagine-your-success [link] [comments]  ( 53 min )
    Is there tool online which translates/encodes digital data into DNA sequence (i.e. Massive Attack project)?
    Hi, I want to play with AI and translating of audio digital data into zeros and ones and then encoding them/translating into DNA sequence (A,D,T,N) . I read a few yrs back that massive attack did that with their album: https://newatlas.com/massive-attack-mezzanine-dna-eth-zurich/54324/. ​ Does anyone know anything about this? ​ Yes, thanks, cheers submitted by /u/Critical_Macaroon_15 [link] [comments]  ( 48 min )
    Speculate: OpenAI, ChatGPT, and what we know by inference
    I've seen a lot of thinkpieces regarding the likes of LLMs like ChatGPT, and what they signify about the future for AI and ML and society at large... but not a lot of teasing out of the business strategy behind OpenAI releasing what amounted to a tuned up version of GPT-3 a few months before GPT-4... especially for free... in the fourth quarter of 2022. It feels like it would be an interesting thought exercise, if nothing else to start thinking about it and what it could mean about what is going to happen in Q2, presumably when GPT-4 comes out. (With its massive parameter count that is rumored to be up to 500 times larger than GPT3). Obviously, there's the benefit of doing this early for exposure: tech companies are renowned for wanting to generate buzz for any number of reasons, and the freemium model is of course part of the playbook. Then of course there's the training that they're getting from the public's qualitative assessment of what is being produced from the model. But I'm not entirely convinced those two factors are what is at play here. I'm thinking mainly in terms of the competitive landscape. Lamda (Google's LLM) has even more parameters than GPT4 but yet openAI was willing to expose its own competitive advantage (enough that a "code red" was called at Google HQ not long after the release). Then, I'm also thinking about Sankar tweeting out and then deleting that GPT4 Is proto AGI and will pass the Turing Test hands down. And of course Altman making the rounds in the podcast circuit dropping very interesting hints about how 2022 will seem "like a sleepy year for AI." My mind immediately goes to this was very much a trial balloon, testing the waters for how society will react to tech that will cause a massive and shocking shift. I'm wondering when you all think about this. Why release GPT 3.5? What are they doing? What do you think it serves for them? What does it say about GPT-4 could bring? Edit: added context submitted by /u/gaudiocomplex [link] [comments]  ( 63 min )
    I've collected 500 AI tools and wanted to share them with you.
    Hello everyone! Over the past few weeks, I have been gathering a list of AI tools and organizing them. Some of these tools may not have a lot of information, so I hope that this list will make it easier for you to research and choose the best one for you. I will continue to add more details and regularly update the list. You are welcome to contribute to the list as well. You can contribute without registering an account and I will review and approve the submissions. Here is the list : https://favird.com/l/ai-tools-and-applications Please let me know if you have any questions and feedbacks. Thanks! submitted by /u/GrabWorking3045 [link] [comments]  ( 51 min )
  • Open

    Effective state space for gym Humanoid/Ant environment
    I am trying to apply TD3 for the gym MuJoCo humanoid and ant environments but I find that their observation space is quite large. For example humanoid obs space dimension is 376. I think directly training on this would be quite inefficient. What could I do to reduce the state space dimension or modify it so that TD3 learns better/faster? submitted by /u/21022018 [link] [comments]  ( 54 min )
  • Open

    Unpacking the “black box” to build better AI models
    Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.  ( 11 min )

  • Open

    Top Predictions and Trends for Generative AI in 2023
    submitted by /u/oridnary_artist [link] [comments]  ( 48 min )
    How does and can a neural network predict a free range answer/number?
    Hey. I have watched a couple of youtube videos (3blue1brown, coding train, a few more) and learned online a bit about neural networks and I think I understand multilayer perceptrons and the algorithms behind them well. They receive input and return a probability of each answer. So when a neural network predicts a digit, it will return what it thinks is the probability for each of the digits (0-9). But, say I want to know what the price for a description of a house will be, do I just undo the activiation function at the end so I can have a big value (1-1million) and create a single output neuron? How will it work? submitted by /u/mrbeanshooter123 [link] [comments]  ( 54 min )
    Help with gradient descent!
    Hi! I am trying to build a very simple AI (very inspired by the 3blue1brown videos). I am having issues with gradient descent. I think I understand the basic idea (v→v′=v−η∇C) (η being the learning rate, v being my weights+biases and C being my cost function). However I can't seem to find out how to find the gradient of my cost function, since numpy.gradient() asks for an array as an input (I thought the input had to be a function). I suspect I am thinking of something incorrectly or doing something very wrong. If anyone could help me out I would appreciate it. Thank you! submitted by /u/HCook86 [link] [comments]  ( 51 min )
    Why Adam Fails to Converge and How AMSGrad Solves This Issue (AMSGrad Explained)
    submitted by /u/Personal-Trainer-541 [link] [comments]  ( 109 min )
    Dancing Animation in WhatIf Style using Stable Diffusion (Tutorial Included)
    submitted by /u/oridnary_artist [link] [comments]  ( 49 min )
    Help!!! Training neural net in vs code
    Hello guys I just created a neural network (Reference from Neural Network from Scratch), I have completed upto optimization portion. Since I was feeling bit confident I decided to test my neural net with MINST handwritten data set, Everything is working , Loss is decreasing with each epoch but it's taking a lot of time, I am using VS code to run it, I checked task manager and it was using my cpu, I have rtx 3060 and i was wondering if I could use my GPU to make it faster, I tried watching yt but all of them used libs to make neural net while I created it using only numpy so I don't know if installing tensorflow will work, Please help me! I am new I am looking for a way to use my gpu to train my neural net in vs code(neural net made with numpy lib only) submitted by /u/Purple_Gen3 [link] [comments]  ( 59 min )
  • Open

    Top Predictions and Trends for Generative AI in 2023
    submitted by /u/oridnary_artist [link] [comments]  ( 50 min )
    Dancing Animation in WhatIf Style using Stable Diffusion (Tutorial Included)
    submitted by /u/oridnary_artist [link] [comments]  ( 50 min )
    The DJs Playing tonight at an AI party near you!
    submitted by /u/tebjan [link] [comments]  ( 50 min )
    Invent 5 new things that don't already exist that humans couldn't live without
    submitted by /u/Imagine-your-success [link] [comments]  ( 50 min )
    Implementing AI on Google Spreadsheets
    Is there anyway to implement AI, like ChatGPT, Text-Babbage-001, etc. on Google Spreadseets? If so, could you please DM me, I got an idea but I'm not an expert, so I'd appreciate some help. Thank you ^^. submitted by /u/Slow-Daikon-3222 [link] [comments]  ( 51 min )
    Artificial Intelligence Deep Dive
    What is the best AI textbook you have read? submitted by /u/_zer0_0ne [link] [comments]  ( 50 min )
    How to generate Image Morphing Animation with Custom images as keyframes?
    I want to generate a smooth image-morphing video of Anime characters' faces with around 40 Custom Images. Something similar to this (images used in this is not custom images) Can anyone guide me through the steps of how to achieve these results with StyleGan2? Or is there any better alternative? I'm totally new to this please help! submitted by /u/Firestormsoumo [link] [comments]  ( 51 min )
    can i license and use ai improved design (Pls read)
    Basically i'm working on a game and i was trying to use character ai to improve my character designs (i used to improve the design but the ideas were basically mine) ​ Can i use it? submitted by /u/Confident_Joke_4121 [link] [comments]  ( 54 min )
    ChatGPT Apologizes To Microsoft’s Satya Nadella For Calling Biryani A Tiffin
    submitted by /u/liquidocelotYT [link] [comments]  ( 51 min )
    Detect AI generated content
    Anyone want to try out my api to detect ai generated content? busterai.com submitted by /u/Ordinary-Grocery2980 [link] [comments]  ( 58 min )
    How would I go about creating an app like "Lensa" with Stable Diffusion?
    ($10 to the legend that helps the most.) Like the title says, I want to create an app like Lensa, where users can upload pictures of themselves and get a AI generated avatar back (with 10-100 versions of the avatar). submitted by /u/dondomigo [link] [comments]  ( 53 min )
    7 Free AI Courses for Beginners in 2023
    submitted by /u/manishsalunke [link] [comments]  ( 50 min )
    What is the term for where ai makes all its associations?
    It's like the brain of the ai. I've been googling for the word but nothing is coming up. Edit: if it helps I remember that there were a lot more than 3 dimensions where it makes the connections. submitted by /u/chrisisbest197 [link] [comments]  ( 55 min )
    You won’t believe how this AI tool can build a website in minutes!
    submitted by /u/moviesdusk [link] [comments]  ( 53 min )
    Why Adam Fails to Converge and How AMSGrad Solves This Issue (AMSGrad Explained)
    Hi guys, I have made a video on YouTube here where I cover why the Adam optimizer may fail to converge on some simple optimization problems and how AMSgrad aims to solve this issue . I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 108 min )
    Ai website
    is there a website that increases image length. e.g i upload an image and its square and i want it to be portrait and it auto fills in the edges using ai submitted by /u/Objective_Wealth_918 [link] [comments]  ( 50 min )
    Who could help me in my project
    I have a pretty big project that uses AI, I’m not an expert and I’m searching for somebody that understands AI perfectly. Hmu or comment if you are interested submitted by /u/Such_Aardvark_1044 [link] [comments]  ( 52 min )
    Can someone explain the last step of RLHF?
    submitted by /u/holamyeung [link] [comments]  ( 78 min )
    ‘Consciousness’ in Robots Was Once Taboo. Now It’s the Last Word. Hod Lipson, the director of the Creative Machines Lab. “This is not just another research question that we’re working on — this is the question”
    submitted by /u/RichKatz [link] [comments]  ( 56 min )
    Squish: AI-generated summaries
    I built a chrome extension that summarises any article into a single paragraph using OpenAI's GPT. It also works with Amazon reviews (positive and negative mix) and popular Twitter threads. Just search "Squish AI" on the chrome web store :) Save time and stay informed with Squish submitted by /u/naveedjan_ [link] [comments]  ( 50 min )
  • Open

    Automated Chart Mining in R [R] or Python [P]
    I'm looking for a way to automate data extraction from bar charts with error bars from peer-reviewed academic papers/PDFs. The goal here is to extract data values from charts and put them in a tabular form. Does anyone have any good resources for how to streamline automated chart mining in python or R? Or does anyone know of a good application/website that does chart mining? submitted by /u/HipPaprika [link] [comments]  ( 59 min )
    [D] Will NLP Researchers Lose Our Jobs after ChatGPT?
    Recently, ChatGPT has become one of the hottest tools in the NLP area. I have tried it and it gives me amazing and fancy results. I believe it will benefit most of the people and make a significant advance in our life. However, unfortunately, I, as an NLP researcher in text generation, feel all what I have done seems meaningless now. I also don't know what I can do as ChatGPT is already strong enough and can solve most of my previous concerns in text generation. Research on ChatGPT also seems not possible as I believe it will not be an open-source project. Research on other NLP tasks also seems challenge as using a prompt in ChatGPT can solve most of the NLP tasks. Any suggestions or comments are welcome. submitted by /u/singularpanda [link] [comments]  ( 68 min )
    [D] How to evaluate factual correctness in zero-shot language models?
    People are nowadays using zero-shot agents such as ChatGPT as search engines. While they are good at answering the most popular questions, sometimes they miss historical, factual, and numerical correctness. So how could this aspect of LLMs be successfully evaluated (in an automated fashion)? Please point out research that you're familiar with. submitted by /u/radi-cho [link] [comments]  ( 61 min )
    [D] Is there a way to use a large dataset of quotes to create custom quote-generating model using GPT-3
    What is the most simple and efficient way that you can feed a large dataset of quotes into a custom model that can then be used create new quotes based on that model's "style" using GPT-3? Thanks so much for your expertise and help! submitted by /u/Artemis_Nox [link] [comments]  ( 59 min )
    [RESEARCH] AI and data in finance: the role of machine learning in anti-money laundering.
    Hi ! Wrote an article about AI/ML in the Anti-Money laundering industry ! https://medium.com/@melmasset/ai-and-data-in-finance-the-role-of-machine-learning-in-anti-money-laundering-1d4dd6f5bacd submitted by /u/TechMelchior [link] [comments]  ( 66 min )
    [R] Greg Yang's work on a rigorous mathematical theory for neural networks
    Greg Yang is a mathematician and AI researcher at Microsoft Research who for the past several years has done incredibly original theoretical work in the understanding of large artificial neural networks. His work currently spans the following five papers: Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes: https://arxiv.org/abs/1910.12478 Tensor Programs II: Neural Tangent Kernel for Any Architecture: https://arxiv.org/abs/2006.14548 Tensor Programs III: Neural Matrix Laws: https://arxiv.org/abs/2009.10685 Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks: https://proceedings.mlr.press/v139/yang21c.html Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer: https://arxiv.org/a…  ( 62 min )
    [Project] Does anyone want to collab on some AI implementation POCs?
    I'm a technical product manager in my day job, and I've spent the past few months educating myself on recent advances in AI implementation in the pre-trained transformer and diffusion areas. I have some basic coding experience and have worked on several complex software projects in my role as a product manager, but my true expertise is in building high-level architectures based on user story frameworks (i.e. - define the key features of the product based on insights/research and build an HLA and technical/business dependency maps that serve as the foundation of a development backlog). I tend to sit right in the middle of the engineering, research, and UX design teams and help keep everyone focused on the things that matter. So in my personal time, I've now developed a considerable backlog…  ( 60 min )
    [D][P] SVM Multi-Classification, is it possible?
    Hi all, was given a dataset with 4 classes: types of beans. Is it possible to apply SVM to this set of data? submitted by /u/Usual_Association269 [link] [comments]  ( 57 min )
    [Discussion] Is there any alternative of deep learning ?
    Increasingly deep learning is becoming the default face of modern AI. So my question is are there any other machine learning theories or ideas different from deep learning which have potential to be big in the future ? submitted by /u/sidney_lumet [link] [comments]  ( 67 min )
    [D] 5 Growing Libraries in Python for Causality Analysis
    submitted by /u/pasticciociccio [link] [comments]  ( 60 min )
    [N] 7 Predictions From The State of AI Report For 2023 ⭕
    1) A SOTA Language Model is Trained on 10x More Data Than Chinchilla -> Language models like Lambda and GPT3 are significantly undertrained. DeepMind proposed Chinchilla, a model which has similar performance to GPT3 with less than half the size (70B vs. 175B). Hence in 2023, significant performance gains will likely come from cleaner/larger datasets. 2) Generative Audio Tools Emerge and Will Attract 100K Developers -> Audio generation has approached human levels. If enough data of your voice is available, the generated speech can even sound amazingly authentic (this is also true for Drake lyrics). Leaving the uncanny valley of awkward robot voices will make adoption surge. 3) NVIDIA Announces Strategic Partnership With AGI-Focused Company Organisation -> Usage statistics in AI resea…  ( 73 min )
    Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
    submitted by /u/matter-of-interest [link] [comments]  ( 64 min )
    [D] Named Entity Recognition (NER) Libraries
    Hi everyone, I have to cluster a large chunk of textual conversational business data to find relevant topics in it. Since there is lot of abstract info in every text like phone, url, numbers, email, name, etc., I have done some basic NER using regex and spacy NER to tag such info and make the texts more generic and canonicalized. But there are some things like product names, raw materials, brand/model, company, etc. which couldn't be tagged. Also, the accuracy of regex and spacy NER isn't high enough. Can anyone suggest a good python NER library, which is accurate and fast enough, preferably has pre-trained models and can tag diverse fields. Thanks. submitted by /u/Devinco001 [link] [comments]  ( 65 min )
    [R] Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering
    Paper: https://arxiv.org/abs/2210.16495 Abstract: We propose a simple refactoring of multi-choice question answering (MCQA) tasks as a series of binary classifications. The MCQA task is generally performed by scoring each (question, answer) pair normalized over all the pairs, and then selecting the answer from the pair that yield the highest score. For n answer choices, this is equivalent to an n-class classification setup where only one class (true answer) is correct. We instead show that classifying (question, true answer) as positive instances and (question, false answer) as negative instances is significantly more effective across various models and datasets. We show the efficacy of our proposed approach in different tasks -- abductive reasoning, commonsense question answering, science question answering, and sentence completion. Our DeBERTa binary classification model reaches the top or close to the top performance on public leaderboards for these tasks. The source code of the proposed approach is available at https://github.com/declare-lab/team ​ https://preview.redd.it/r77sh3xczkaa1.png?width=2216&format=png&auto=webp&s=5591ca9b1d8a769ae088429abc2d945589bb5b67 submitted by /u/bideex [link] [comments]  ( 60 min )
    [D] Spellcheck Libraries
    Hi everyone, I have a large chunk of textual conversational data which is to be clustered in an unsupervised manner to find the popular topics in it. The data I have, has a lot of spelling errors. I have used many libraries like symspellpy, pyenchant, pyspellchecker, etc. to correct spelling errors but none of them seems to be accurate or fast enough. Also, they don't take into account context, abbreviations and grammer while spell correction. Can anyone suggest a python library which: Can correct spell errors without many false positives Has high accuracy and faster runtime Can take into account context, grammer, abbreviations, phonetics and adjacent keystroke errors while correcting spellings More preferable if multilingual too Thanks. submitted by /u/Devinco001 [link] [comments]  ( 59 min )
    Random forests, sound symbolism and Pokemon evolution: Random Forest algorithms are trained to classify Pokemon according to evolution based on the sounds that make up their names. These models are then tested on samples from an elicitation experiment and they perform better than human participants.
    submitted by /u/Friday33 [link] [comments]  ( 57 min )
    Apple AI Residency 2023 [R]
    Hi All, Did anyone receive invite for online test or interviews for the Apple AI Residency program for the 2023 batch? The deadline for applications was 7th December 2022. Haven't received any communication since then. submitted by /u/Extension-Reward5756 [link] [comments]  ( 60 min )
    Is there an AI tool that can specifically isolate sentences or chunks of text, from larger bodies of text, that meet a certain narrow criteria -- then output those as the result? - [D]
    Let's say there's a whole paragraph of text, 90% of which is irrelevant fluff for my needs. What I'm specifically looking to do is isolate one key pertinent piece of information, which meets a certain criteria that I can somehow specify. As an example, let's say I have 1,000 paragraphs that are brief biographies of famous people from history. Is there any kind of AI tool I can use to say something like: "For each of these biographies, IF they include information about where this famous person was born? Isolate this piece of information and only output that as a result." Then it just runs through every single paragraph, conducts the analysis, finds the paragraphs that DO contain this information -- then outputs ONLY that as the result, for each paragraph? For example, full paragraph 1: …  ( 63 min )
    [D] compute/model libraries for "classical" learning?
    I don't do very much work with neural models at all, so I'm not very familiar with the library ecosystem developed around them (think TF, Torch, theano, etc) but I'm working on a few projects that would benefit significantly from autograd and the ability to build out models at a higher level of abstraction analogous to layers/capsules/attention heads in neural models. Being able to define functions with explicit gradients would also allow me to use building blocks that autograd can't handle without more information (best example I can give is a nonlinear transformation that results from a constrained optimization problem, and gradients need to account for Lagrange multipliers being implicit functions of the input). And if there's performance gains to be had from a JIT, I certainly wouldn't complain! I'm not certain if I'm going to be able to get what I want out of the options I'm aware of for a few reasons- I don't know the libraries at all! Very frequently there are operations that don't lend themselves to trivial parallelism, require iteration or special function computation (like the polygamma and Bessel functions) which are much better suited to a CPU than a GPU I'll be exclusively working on CPU. Some of this is with sparse data that cannot be stored dense, so I'm unsure how that will translate. So, main question- if anyone uses these kinds of libraries for similar applications, is it helpful for development, code quality, performance, etc? Which do you use, and why? If you have experience doing formal academic research and/or developing+maintaining production projects I have particular interest in your thoughts. I'm hopeful that I can find a tool that will handle the math + numeric concerns as well as result in code that's easier to maintain. Thanks for your time! submitted by /u/comradeswitch [link] [comments]  ( 61 min )
  • Open

    Maxwell-Boltzmann and Gamma
    When I shared an image from the previous post on Twitter, someone who goes by the handle Nonetheless made the astute observation that image looked like the Maxwell-Boltzmann distribution. That made me wonder what 1/Γ(x) would be like turned into a probability distribution, and whether it would be approximately like the Maxwell-Boltzmann distribution. (Here I’m […] Maxwell-Boltzmann and Gamma first appeared on John D. Cook.  ( 6 min )
    Visualizing convergence of an infinite product
    A little while ago I wrote a post looking at how the infinite product for sine converges. The plot of the error terms is both mathematically and aesthetically interesting. This post will look at similar plots for the reciprocal of the gamma function. The reciprocal of the gamma function is an entire function, i.e. is […] Visualizing convergence of an infinite product first appeared on John D. Cook.  ( 4 min )
  • Open

    [Question] SAC Loss
    ​ critic / policy / entropy loss step length in each episode reward in the last episode Hi, I learned the reinforcement learning by myself and tried to implement the SAC algorithm in a custom gym environment. but my agent's reward diverges despite a small learning rate like 1e-7, Can I get any advice on my simulation? ​ Thank you submitted by /u/sonlightinn [link] [comments]  ( 57 min )
  • Open

    Training a Deep Q-Learning Agent Inside a Generic Constraint Programming Solver. (arXiv:2301.01913v1 [cs.AI])
    Constraint programming is known for being an efficient approach for solving combinatorial problems. Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. This observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. To the best of our knowledge, it is still an open research question. Although several generic variable-selection heuristics are available in the literature, the options for a generic value-selection heuristic are more scarce. In this paper, we propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network architecture. Experiments on graph coloring, maximum independent set, and maximum cut problems show that our framework is able to find better solutions close to optimality without requiring a large amounts of backtracks while being generic.  ( 2 min )
    Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization. (arXiv:2205.13209v2 [cs.LG] UPDATED)
    Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i.e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable of learning CO solvers less relying on problem-specific expert domain knowledge (heuristic method) and supervised labeled data (supervised learning method). This paper presents a novel training scheme, Sym-NCO, which is a regularizer-based training scheme that leverages universal symmetricities in various CO problems and solutions. Leveraging symmetricities such as rotational and reflectional invariance can greatly improve the generalization capability of DRL-NCO because it allows the learned solver to exploit the commonly shared symmetricities in the same CO problem class. Our experimental results verify that our Sym-NCO greatly improves the performance of DRL-NCO methods in four CO tasks, including the traveling salesman problem (TSP), capacitated vehicle routing problem (CVRP), prize collecting TSP (PCTSP), and orienteering problem (OP), without utilizing problem-specific expert domain knowledge. Remarkably, Sym-NCO outperformed not only the existing DRL-NCO methods but also a competitive conventional solver, the iterative local search (ILS), in PCTSP at 240 faster speed. Our source code is available at https://github.com/alstn12088/Sym-NCO.  ( 2 min )
    How Does Sharpness-Aware Minimization Minimize Sharpness?. (arXiv:2211.05729v2 [cs.LG] UPDATED)
    Sharpness-Aware Minimization (SAM) is a highly effective regularization technique for improving the generalization of deep neural networks for various settings. However, the underlying working of SAM remains elusive because of various intriguing approximations in the theoretical characterizations. SAM intends to penalize a notion of sharpness of the model but implements a computationally efficient variant; moreover, a third notion of sharpness was used for proving generalization guarantees. The subtle differences in these notions of sharpness can indeed lead to significantly different empirical results. This paper rigorously nails down the exact sharpness notion that SAM regularizes and clarifies the underlying mechanism. We also show that the two steps of approximations in the original motivation of SAM individually lead to inaccurate local conclusions, but their combination accidentally reveals the correct effect, when full-batch gradients are applied. Furthermore, we also prove that the stochastic version of SAM in fact regularizes the third notion of sharpness mentioned above, which is most likely to be the preferred notion for practical performance. The key mechanism behind this intriguing phenomenon is the alignment between the gradient and the top eigenvector of Hessian when SAM is applied.  ( 2 min )
    Learning to Visually Navigate in Photorealistic Environments Without any Supervision. (arXiv:2004.04954v1 [cs.CV] CROSS LISTED)
    Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training. In this paper, we introduce a novel approach for learning to navigate from image inputs without external supervision or reward. Our approach consists of three stages: learning a good representation of first-person views, then learning to explore using memory, and finally learning to navigate by setting its own goals. The model is trained with intrinsic rewards only so that it can be applied to any environment with image observations. We show the benefits of our approach by training an agent to navigate challenging photo-realistic environments from the Gibson dataset with RGB inputs only.  ( 2 min )
    On Biased Behavior of GANs for Face Verification. (arXiv:2208.13061v3 [cs.CV] UPDATED)
    Deep Learning systems need large data for training. Datasets for training face verification systems are difficult to obtain and prone to privacy issues. Synthetic data generated by generative models such as GANs can be a good alternative. However, we show that data generated from GANs are prone to bias and fairness issues. Specifically, GANs trained on FFHQ dataset show biased behavior towards generating white faces in the age group of 20-29. We also demonstrate that synthetic faces cause disparate impact, specifically for race attribute, when used for fine tuning face verification systems.  ( 2 min )
    Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization. (arXiv:2205.02670v2 [cs.LG] UPDATED)
    Content mismatch usually occurs when data from one modality is translated to another, e.g. language learners producing mispronunciations (errors in speech) when reading a sentence (target text) aloud. However, most existing alignment algorithms assume that the content involved in the two modalities is perfectly matched, thus leading to difficulty in locating such mismatch between speech and text. In this work, we develop an unsupervised learning algorithm that can infer the relationship between content-mismatched cross-modal sequential data, especially for speech-text sequences. More specifically, we propose a hierarchical Bayesian deep learning model, dubbed mismatch localization variational autoencoder (ML-VAE), which decomposes the generative process of the speech into hierarchically structured latent variables, indicating the relationship between the two modalities. Training such a model is very challenging due to the discrete latent variables with complex dependencies involved. To address this challenge, we propose a novel and effective training procedure that alternates between estimating the hard assignments of the discrete latent variables over a specifically designed mismatch localization finite-state acceptor (ML-FSA) and updating the parameters of neural networks. In this work, we focus on the mismatch localization problem for speech and text, and our experimental results show that ML-VAE successfully locates the mismatch between text and speech, without the need for human annotations for model training.  ( 2 min )
    Sarcasm Detection Framework Using Context, Emotion and Sentiment Features. (arXiv:2211.13014v2 [cs.CL] UPDATED)
    Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. Such incongruity is the essential component of sarcasm, however, it makes sarcasm detection quite a challenging task. In this paper, we propose a model, that incorporates different features to capture the incongruity intrinsic to sarcasm. We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks. Our approach outperformed previous state-of-the-art results on four datasets from social networking platforms and online media.  ( 2 min )
    Plasticity Neural Network Based on Astrocytic effects at Critical Period, Synaptic Competition and Strength Rebalance by Current and Mnemonic Brain Plasticity and Synapse Formation. (arXiv:2203.11740v8 [cs.NE] UPDATED)
    In addition to the shared weights of synaptic connections, PNN includes weights of synaptic ranges for Forward propagation and Back propagation[15,16,19-24]. PNN considers synaptic strength balance in dynamic of phagocytosing of synapses and static of constant sum of synapses length [15],the lead behavior of the school of fish is well embodied in our PNN. Synapse formation will inhibit dendrites generation to a certain extent in experiments, by simulations synapse formation will inhibit the function of dendrites [16]. Closing the critical period will cause neurological disorder in experiments, but worse results in PNN simulations [19]. The memory persistence gradient information of backward circuit similar to the Enforcing Resilience in a Spring Boot. The relatively good and inferior gradient information in synapse formation of backward circuit like the folds of the brain. Considering both negative and positive memories persistence help activate synapse length changes with iterations better than only considering positive memory. So using memory of fear learning and improving of synaptic activity to observe obviously [20]. Memory persistence factor also inhibit local synaptic accumulation. And refers PNN can also introduce the relatively good and inferior solution to update the velocity of particle in PSO. Astrocytic phagocytosis will avoid the local accumulation of synapses by simulation (Lack of astrocytic phagocytosis causes excitatory synapses and functionally impaired synapses accumulate in experiments and lead to destruction of cognition, but local longer synapses and worse results in PNN simulations) [21]. It gives relationship of human intelligence and cortical thickness, individual differences in brain[22].PNN also considered the memory engram cells that strengthened Synaptic strength[23]. The simple PNN which only has the synaptic phagocytosis.  ( 3 min )
    Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search. (arXiv:2209.11785v3 [cs.LG] UPDATED)
    We propose three novel pruning techniques to improve the cost and results of inference-aware Differentiable Neural Architecture Search (DNAS). First, we introduce Prunode, a stochastic bi-path building block for DNAS, which can search over inner hidden dimensions with O(1) memory and compute complexity. Second, we present an algorithm for pruning blocks within a stochastic layer of the SuperNet during the search. Third, we describe a novel technique for pruning unnecessary stochastic layers during the search. The optimized models resulting from the search are called PruNet and establishes a new state-of-the-art Pareto frontier for NVIDIA V100 in terms of inference latency for ImageNet Top-1 image classification accuracy. PruNet as a backbone also outperforms GPUNet and EfficientNet on the COCO object detection task on inference latency relative to mean Average Precision (mAP).  ( 2 min )
    FREDE: Anytime Graph Embeddings. (arXiv:2006.04746v2 [cs.LG] UPDATED)
    Low-dimensional representations, or embeddings, of a graph's nodes facilitate several practical data science and data engineering tasks. As such embeddings rely, explicitly or implicitly, on a similarity measure among nodes, they require the computation of a quadratic similarity matrix, inducing a tradeoff between space complexity and embedding quality. To date, no graph embedding work combines (i) linear space complexity, (ii) a nonlinear transform as its basis, and (iii) nontrivial quality guarantees. In this paper we introduce FREDE (FREquent Directions Embedding), a graph embedding based on matrix sketching that combines those three desiderata. Starting out from the observation that embedding methods aim to preserve the covariance among the rows of a similarity matrix}, FREDE iteratively improves on quality while individually processing rows of a nonlinearly transformed PPR similarity matrix derived from a state-of-the-art graph embedding method} and provides, at any iteration, column-covariance approximation guarantees in due course almost indistinguishable from those of the optimal approximation by SVD. Our experimental evaluation on variably sized networks shows that FREDE performs almost as well as SVD and competitively against state-of-the-art embedding methods in diverse data science tasks, even when it is based on as little as 10% of node similarities.  ( 2 min )
    Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis. (arXiv:2301.01867v1 [cs.LG])
    Detection of high impedance faults (HIF) has been one of the biggest challenges in the power distribution network. The low current magnitude and diverse characteristics of HIFs make them difficult to be detected by over-current relays. Recently, data-driven methods based on machine learning models are gaining popularity in HIF detection due to their capability to learn complex patterns from data. Most machine learning-based detection methods adopt supervised learning techniques to distinguish HIFs from normal load conditions by performing classifications, which rely on a large amount of data collected during HIF. However, measurements of HIF are difficult to acquire in the real world. As a result, the reliability and generalization of the classification methods are limited when the load profiles and faults are not present in the training data. Consequently, this paper proposes an unsupervised HIF detection framework using the autoencoder and principal component analysis-based monitoring techniques. The proposed fault detection method detects the HIF by monitoring the changes in correlation structure within the current waveforms that are different from the normal loads. The performance of the proposed HIF detection method is tested using real data collected from a 4.16 kV distribution system and compared with results from a commercially available solution for HIF detection. The numerical results demonstrate that the proposed method outperforms the commercially available HIF detection technique while maintaining high security by not falsely detecting during load conditions.  ( 2 min )
    Trace Encoding in Process Mining: a survey and benchmarking. (arXiv:2301.02167v1 [cs.LG])
    Encoding methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, trace clustering, etc. These methods are usually performed as preprocessing steps and are responsible for transforming complex information into a numerical feature space. Most papers choose existing encoding methods arbitrarily or employ a strategy based on a specific expert knowledge domain. Moreover, existing methods are employed by using their default hyperparameters without evaluating other options. This practice can lead to several drawbacks, such as suboptimal performance and unfair comparisons with the state-of-the-art. Therefore, this work aims at providing a comprehensive survey on event log encoding by comparing 27 methods, from different natures, in terms of expressivity, scalability, correlation, and domain agnosticism. To the best of our knowledge, this is the most comprehensive study so far focusing on trace encoding in process mining. It contributes to maturing awareness about the role of trace encoding in process mining pipelines and sheds light on issues, concerns, and future research directions regarding the use of encoding methods to bridge the gap between machine learning models and process mining.  ( 2 min )
    Deep Statistical Solver for Distribution System State Estimation. (arXiv:2301.01835v1 [cs.LG])
    Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data. In fact, measurements in the distribution system are often noisy, corrupted, and unavailable. To address these issues, we propose the Deep Statistical Solver for Distribution System State Estimation (DSS$^2$), a deep learning model based on graph neural networks (GNNs) that accounts for the network structure of the distribution system and for the physical governing power flow equations. DSS$^2$ leverages hypergraphs to represent the heterogeneous components of the distribution systems and updates their latent representations via a node-centric message-passing scheme. A weakly supervised learning approach is put forth to train the DSS$^2$ in a learning-to-optimize fashion w.r.t. the Weighted Least Squares loss with noisy measurements and pseudomeasurements. By enforcing the GNN output into the power flow equations and the latter into the loss function, we force the DSS$^2$ to respect the physics of the distribution system. This strategy enables learning from noisy measurements, acting as an implicit denoiser, and alleviating the need for ideal labeled data. Extensive experiments with case studies on the IEEE 14-bus, 70-bus, and 179-bus networks showed the DSS$^2$ outperforms by a margin the conventional Weighted Least Squares algorithm in accuracy, convergence, and computational time, while being more robust to noisy, erroneous, and missing measurements. The DSS$^2$ achieves a competing, yet lower, performance compared with the supervised models that rely on the unrealistic assumption of having all the true labels.  ( 2 min )
    Enhancement attacks in biomedical machine learning. (arXiv:2301.01885v1 [stat.ML])
    The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works have investigated the ability of adversarial attacks to degrade model performance in medical imaging, the ability to falsely improve performance via recently-developed "enhancement attacks" may be a greater threat to biomedical machine learning. In the spirit of developing attacks to better understand trustworthiness, we developed three techniques to drastically enhance prediction performance of classifiers with minimal changes to features, including the enhancement of 1) within-dataset predictions, 2) a particular method over another, and 3) cross-dataset generalization. Our within-dataset enhancement framework falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities between original and enhanced data (Pearson's r's>0.99). Similarly, the method-specific enhancement framework was effective in falsely improving the performance of one method over another. For example, a simple neural network outperformed LR by 50% on our enhanced dataset, although no performance differences were present in the original dataset. Crucially, the original and enhanced data were still similar (r=0.95). Finally, we demonstrated that enhancement is not specific to within-dataset predictions but can also be adapted to enhance the generalization accuracy of one dataset to another by up to 38%. Overall, our results suggest that more robust data sharing and provenance tracking pipelines are necessary to maintain data integrity in biomedical machine learning research.  ( 2 min )
    Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games. (arXiv:2301.01997v1 [cs.LG])
    In this paper, we formulate inverse reinforcement learning (IRL) as an expert-learner interaction whereby the optimal performance intent of an expert or target agent is unknown to a learner agent. The learner observes the states and controls of the expert and hence seeks to reconstruct the expert's cost function intent and thus mimics the expert's optimal response. Next, we add non-cooperative disturbances that seek to disrupt the learning and stability of the learner agent. This leads to the formulation of a new interaction we call zero-sum game IRL. We develop a framework to solve the zero-sum game IRL problem that is a modified extension of RL policy iteration (PI) to allow unknown expert performance intentions to be computed and non-cooperative disturbances to be rejected. The framework has two parts: a value function and control action update based on an extension of PI, and a cost function update based on standard inverse optimal control. Then, we eventually develop an off-policy IRL algorithm that does not require knowledge of the expert and learner agent dynamics and performs single-loop learning. Rigorous proofs and analyses are given. Finally, simulation experiments are presented to show the effectiveness of the new approach.  ( 2 min )
    Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism. (arXiv:2301.01919v1 [cs.MA])
    Communication can impressively improve cooperation in multi-agent reinforcement learning (MARL), especially for partially-observed tasks. However, existing works either broadcast the messages leading to information redundancy, or learn targeted communication by modeling all the other agents as targets, which is not scalable when the number of agents varies. In this work, to tackle the scalability problem of MARL communication for partially-observed tasks, we propose a novel framework Transformer-based Email Mechanism (TEM). The agents adopt local communication to send messages only to the ones that can be observed without modeling all the agents. Inspired by human cooperation with email forwarding, we design message chains to forward information to cooperate with the agents outside the observation range. We introduce Transformer to encode and decode the message chain to choose the next receiver selectively. Empirically, TEM outperforms the baselines on multiple cooperative MARL benchmarks. When the number of agents varies, TEM maintains superior performance without further training.  ( 2 min )
    StitchNet: Composing Neural Networks from Pre-Trained Fragments. (arXiv:2301.01947v1 [cs.LG])
    We propose StitchNet, a novel neural network creation paradigm that stitches together fragments (one or more consecutive network layers) from multiple pre-trained neural networks. StitchNet allows the creation of high-performing neural networks without the large compute and data requirements needed under traditional model creation processes via backpropagation training. We leverage Centered Kernel Alignment (CKA) as a compatibility measure to efficiently guide the selection of these fragments in composing a network for a given task tailored to specific accuracy needs and computing resource constraints. We then show that these fragments can be stitched together to create neural networks with comparable accuracy to traditionally trained networks at a fraction of computing resource and data requirements. Finally, we explore a novel on-the-fly personalized model creation and inference application enabled by this new paradigm.  ( 2 min )
    Markov Decision Processes under Model Uncertainty. (arXiv:2206.06109v2 [math.OC] UPDATED)
    We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle we obtain a local-to-global paradigm, namely solving a local, i.e., a one time-step robust optimization problem leads to an optimizer of the global (i.e. infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the S&P 500. We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.  ( 2 min )
    Time-inhomogeneous diffusion geometry and topology. (arXiv:2203.14860v2 [cs.LG] UPDATED)
    Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifold learning, denoising, clustering, and visualization of high-dimensional data. Diffusion condensation is constructed as a time-inhomogeneous process where each step first computes and then applies a diffusion operator to the data. We theoretically analyze the convergence and evolution of this process from geometric, spectral, and topological perspectives. From a geometric perspective, we obtain convergence bounds based on the smallest transition probability and the radius of the data, whereas from a spectral perspective, our bounds are based on the eigenspectrum of the diffusion kernel. Our spectral results are of particular interest since most of the literature on data diffusion is focused on homogeneous processes. From a topological perspective, we show diffusion condensation generalizes centroid-based hierarchical clustering. We use this perspective to obtain a bound based on the number of data points, independent of their location. To understand the evolution of the data geometry beyond convergence, we use topological data analysis. We show that the condensation process itself defines an intrinsic condensation homology. We use this intrinsic topology as well as the ambient persistent homology of the condensation process to study how the data changes over diffusion time. We demonstrate both types of topological information in well-understood toy examples. Our work gives theoretical insights into the convergence of diffusion condensation, and shows that it provides a link between topological and geometric data analysis.  ( 2 min )
    Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics. (arXiv:2211.15411v4 [cs.LG] UPDATED)
    WQMIX, QMIX, QTRAN, and VDN are SOTA algorithms for Dec-POMDP. All of them cannot solve complex agents' cooperation domains. We give an algorithm to solve such problems. In the first stage, we solve a single-agent problem and get a policy. In the second stage, we solve the multi-agent problem with the single-agent policy. SA2MA has a clear advantage over all competitors in complex agents' cooperative domains.  ( 2 min )
    Exploration via Elliptical Episodic Bonuses. (arXiv:2210.05805v2 [cs.LG] UPDATED)
    In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes. In this work, we show that the effectiveness of these methods critically relies on a count-based episodic term in their exploration bonus. As a result, despite their success in relatively simple, noise-free settings, these methods fall short in more realistic scenarios where the state space is vast and prone to noise. To address this limitation, we introduce Exploration via Elliptical Episodic Bonuses (E3B), a new method which extends count-based episodic bonuses to continuous state spaces and encourages an agent to explore states that are diverse under a learned embedding within each episode. The embedding is learned using an inverse dynamics model in order to capture controllable aspects of the environment. Our method sets a new state-of-the-art across 16 challenging tasks from the MiniHack suite, without requiring task-specific inductive biases. E3B also matches existing methods on sparse reward, pixel-based VizDoom environments, and outperforms existing methods in reward-free exploration on Habitat, demonstrating that it can scale to high-dimensional pixel-based observations and realistic environments.  ( 2 min )
    Understanding Representation Quality in Self-Supervised Models. (arXiv:2203.01881v3 [cs.LG] UPDATED)
    Self-supervised learning has shown impressive results in downstream classification tasks. However, there is limited work in understanding their failure modes and interpreting their learned representations. In this paper, we study the representation space of six state-of-the-art self-supervised models including SimCLR, SwaV, MoCo, BYOL, DINO and SimSiam. Without the use of class label information, we discover highly activating features that correspond to unique physical attributes in images and exist mostly in correctly-classified representations. Using these features, we propose Self-Supervised Representation Quality Score (or Q-Score), a model-agnostic, unsupervised score that can reliably predict if a given sample is likely to be mis-classified during linear evaluation, achieving AUPRC of 91.45 on ImageNet-100 and 78.78 on ImageNet-1K. Q-Score can also be used as a regularization term on any self-supervised model to remedy low-quality representations through the course of pre-training. We show that pre-training with Q-Score regularization can boost the performance of six state-of-the-art self-supervised models on ImageNet-1K, ImageNet-100, CIFAR-10, CIFAR-100 and STL-10, showing an average relative increase of 1.8% top-1 accuracy on linear evaluation. On ImageNet-100, BYOL shows 7.2% relative improvement and on ImageNet-1K, SimCLR shows 4.7% relative improvement compared to their baselines. Finally, using gradient heatmaps and Salient ImageNet masks, we define a metric to quantify the interpretability of each representation. We show that highly activating features are strongly correlated to core attributes and enhancing these features through Q-score regularization improves the overall representation interpretability for all self-supervised models.  ( 2 min )
    AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning. (arXiv:2211.16486v2 [cond-mat.mtrl-sci] UPDATED)
    Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the minimum binding energy - the adsorption energy - for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration, within a 0.1 eV threshold, 86.33% of the time, while achieving a 1331x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 85,658 unique configurations.  ( 2 min )
    Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem. (arXiv:2301.01772v1 [cs.LG])
    The Transformer architecture yields state-of-the-art results in many tasks such as natural language processing (NLP) and computer vision (CV), since the ability to efficiently capture the precise long-range dependency coupling between input sequences. With this advanced capability, however, the quadratic time complexity and high memory usage prevents the Transformer from dealing with long time-series forecasting problem (LTFP). To address these difficulties: (i) we revisit the learned attention patterns of the vanilla self-attention, redesigned the calculation method of self-attention based the Maximum Entropy Principle. (ii) we propose a new method to sparse the self-attention, which can prevent the loss of more important self-attention scores due to random sampling.(iii) We propose Keys/Values Distilling method motivated that a large amount of feature in the original self-attention map is redundant, which can further reduce the time and spatial complexity and make it possible to input longer time-series. Finally, we propose a method that combines the encoder-decoder architecture with seasonal-trend decomposition, i.e., using the encoder-decoder architecture to capture more specific seasonal parts. A large number of experiments on several large-scale datasets show that our Infomaxformer is obviously superior to the existing methods. We expect this to open up a new solution for Transformer to solve LTFP, and exploring the ability of the Transformer architecture to capture much longer temporal dependencies.  ( 2 min )
    On Sequential Bayesian Inference for Continual Learning. (arXiv:2301.01828v1 [cs.LG])
    Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and test whether having access to the true posterior is guaranteed to prevent catastrophic forgetting in Bayesian neural networks. To do this we perform sequential Bayesian inference using Hamiltonian Monte Carlo. We propagate the posterior as a prior for new tasks by fitting a density estimator on Hamiltonian Monte Carlo samples. We find that this approach fails to prevent catastrophic forgetting demonstrating the difficulty in performing sequential Bayesian inference in neural networks. From there we study simple analytical examples of sequential Bayesian inference and CL and highlight the issue of model misspecification which can lead to sub-optimal continual learning performance despite exact inference. Furthermore, we discuss how task data imbalances can cause forgetting. From these limitations, we argue that we need probabilistic models of the continual learning generative process rather than relying on sequential Bayesian inference over Bayesian neural network weights. In this vein, we also propose a simple baseline called Prototypical Bayesian Continual Learning, which is competitive with state-of-the-art Bayesian continual learning methods on class incremental continual learning vision benchmarks.  ( 2 min )
    Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping. (arXiv:2301.02099v1 [cs.RO])
    Developing agents that can execute multiple skills by learning from pre-collected datasets is an important problem in robotics, where online interaction with the environment is extremely time-consuming. Moreover, manually designing reward functions for every single desired skill is prohibitive. Prior works targeted these challenges by learning goal-conditioned policies from offline datasets without manually specified rewards, through hindsight relabelling. These methods suffer from the issue of sparsity of rewards, and fail at long-horizon tasks. In this work, we propose a novel self-supervised learning phase on the pre-collected dataset to understand the structure and the dynamics of the model, and shape a dense reward function for learning policies offline. We evaluate our method on three continuous control tasks, and show that our model significantly outperforms existing approaches, especially on tasks that involve long-term planning.  ( 2 min )
    Understanding Hyperdimensional Computing for Parallel Single-Pass Learning. (arXiv:2202.04805v2 [cs.LG] UPDATED)
    Hyperdimensional computing (HDC) is an emerging learning paradigm that computes with high dimensional binary vectors. It is attractive because of its energy efficiency and low latency, especially on emerging hardware -- but HDC suffers from low model accuracy, with little theoretical understanding of what limits its performance. We propose a new theoretical analysis of the limits of HDC via a consideration of what similarity matrices can be "expressed" by binary vectors, and we show how the limits of HDC can be approached using random Fourier features (RFF). We extend our analysis to the more general class of vector symbolic architectures (VSA), which compute with high-dimensional vectors (hypervectors) that are not necessarily binary. We propose a new class of VSAs, finite group VSAs, which surpass the limits of HDC. Using representation theory, we characterize which similarity matrices can be "expressed" by finite group VSA hypervectors, and we show how these VSAs can be constructed. Experimental results show that our RFF method and group VSA can both outperform the state-of-the-art HDC model by up to 7.6\% while maintaining hardware efficiency.  ( 2 min )
    Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty. (arXiv:2210.00898v2 [cs.LG] UPDATED)
    We present a novel $Q$-learning algorithm to solve distributionally robust Markov decision problems, where the corresponding ambiguity set of transition probabilities for the underlying Markov decision process is a Wasserstein ball around a (possibly estimated) reference measure. We prove convergence of the presented algorithm and provide several examples also using real data to illustrate both the tractability of our algorithm as well as the benefits of considering distributional robustness when solving stochastic optimal control problems, in particular when the estimated distributions turn out to be misspecified in practice.  ( 2 min )
    Robust Imitation via Mirror Descent Inverse Reinforcement Learning. (arXiv:2210.11201v2 [cs.LG] UPDATED)
    Recently, adversarial imitation learning has shown a scalable reward acquisition method for inverse reinforcement learning (IRL) problems. However, estimated reward signals often become uncertain and fail to train a reliable statistical model since the existing methods tend to solve hard optimization problems directly. Inspired by a first-order optimization method called mirror descent, this paper proposes to predict a sequence of reward functions, which are iterative solutions for a constrained convex problem. IRL solutions derived by mirror descent are tolerant to the uncertainty incurred by target density estimation since the amount of reward learning is regulated with respect to local geometric constraints. We prove that the proposed mirror descent update rule ensures robust minimization of a Bregman divergence in terms of a rigorous regret bound of $\mathcal{O}(1/T)$ for step sizes $\{\eta_t\}_{t=1}^{T}$. Our IRL method was applied on top of an adversarial framework, and it outperformed existing adversarial methods in an extensive suite of benchmarks.  ( 2 min )
    Conditional Gradients for the Approximate Vanishing Ideal. (arXiv:2202.03349v13 [cs.LG] UPDATED)
    The vanishing ideal of a set of points $X\subseteq \mathbb{R}^n$ is the set of polynomials that evaluate to $0$ over all points $\mathbf{x} \in X$ and admits an efficient representation by a finite set of polynomials called generators. To accommodate the noise in the data set, we introduce the pairwise conditional gradients approximate vanishing ideal algorithm (PCGAVI) that constructs a set of generators of the approximate vanishing ideal. The constructed generators capture polynomial structures in data and give rise to a feature map that can, for example, be used in combination with a linear classifier for supervised learning. In PCGAVI, we construct the set of generators by solving constrained convex optimization problems with the pairwise conditional gradients algorithm. Thus, PCGAVI not only constructs few but also sparse generators, making the corresponding feature transformation robust and compact. Furthermore, we derive several learning guarantees for PCGAVI that make the algorithm theoretically better motivated than related generator-constructing methods.  ( 2 min )
    Denoising Deep Generative Models. (arXiv:2212.01265v3 [cs.LG] UPDATED)
    Likelihood-based deep generative models have recently been shown to exhibit pathological behaviour under the manifold hypothesis as a consequence of using high-dimensional densities to model data with low-dimensional structure. In this paper we propose two methodologies aimed at addressing this problem. Both are based on adding Gaussian noise to the data to remove the dimensionality mismatch during training, and both provide a denoising mechanism whose goal is to sample from the model as though no noise had been added to the data. Our first approach is based on Tweedie's formula, and the second on models which take the variance of added noise as a conditional input. We show that surprisingly, while well motivated, these approaches only sporadically improve performance over not adding noise, and that other methods of addressing the dimensionality mismatch are more empirically adequate.  ( 2 min )
    Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles. (arXiv:2205.09829v2 [q-bio.NC] UPDATED)
    Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machine interfaces. Reliable decoding even from small neural populations is possible because high dimensional neural population activity typically occupies low dimensional manifolds that are discoverable with suitable latent variable models. Over time however, drifts in activity of individual neurons and instabilities in neural recording devices can be substantial, making stable decoding over days and weeks impractical. While this drift cannot be predicted on an individual neuron level, population level variations over consecutive recording sessions such as differing sets of neurons and varying permutations of consistent neurons in recorded data may be learnable when the underlying manifold is stable over time. Classification of consistent versus unfamiliar neurons across sessions and accounting for deviations in the order of consistent recording neurons in recording datasets over sessions of recordings may then maintain decoding performance. In this work we show that self-supervised training of a deep neural network can be used to compensate for this inter-session variability. As a result, a sequential autoencoding model can maintain state-of-the-art behaviour decoding performance for completely unseen recording sessions several days into the future. Our approach only requires a single recording session for training the model, and is a step towards reliable, recalibration-free brain computer interfaces.  ( 2 min )
    FF-NSL: Feed-Forward Neural-Symbolic Learner. (arXiv:2106.13103v3 [cs.LG] UPDATED)
    Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFNSL), that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data. We demonstrate the generality of FFNSL on four neural-symbolic classification problems, where different pre-trained neural network models and logic-based machine learning systems are integrated to learn interpretable knowledge from sequences of images. We evaluate the robustness of our framework by using images subject to distributional shifts, for which the pre-trained neural networks may predict incorrectly and with high confidence. We analyse the impact that these shifts have on the accuracy of the learned knowledge and run-time performance, comparing FFNSL to tree-based and pure neural approaches. Our experimental results show that FFNSL outperforms the baselines by learning more accurate and interpretable knowledge with fewer examples.  ( 2 min )
    LieGG: Studying Learned Lie Group Generators. (arXiv:2210.04345v2 [cs.LG] UPDATED)
    Symmetries built into a neural network have appeared to be very beneficial for a wide range of tasks as it saves the data to learn them. We depart from the position that when symmetries are not built into a model a priori, it is advantageous for robust networks to learn symmetries directly from the data to fit a task function. In this paper, we present a method to extract symmetries learned by a neural network and to evaluate the degree to which a network is invariant to them. With our method, we are able to explicitly retrieve learned invariances in a form of the generators of corresponding Lie-groups without prior knowledge of symmetries in the data. We use the proposed method to study how symmetrical properties depend on a neural network's parameterization and configuration. We found that the ability of a network to learn symmetries generalizes over a range of architectures. However, the quality of learned symmetries depends on the depth and the number of parameters.  ( 2 min )
    Global Weighted Tensor Nuclear Norm for Tensor Robust Principal Component Analysis. (arXiv:2209.14084v2 [cs.LG] UPDATED)
    Tensor Robust Principal Component Analysis (TRPCA), which aims to recover a low-rank tensor corrupted by sparse noise, has attracted much attention in many real applications. This paper develops a new Global Weighted TRPCA method (GWTRPCA), which is the first approach simultaneously considers the significance of intra-frontal slice and inter-frontal slice singular values in the Fourier domain. Exploiting this global information, GWTRPCA penalizes the larger singular values less and assigns smaller weights to them. Hence, our method can recover the low-tubal-rank components more exactly. Moreover, we propose an effective adaptive weight learning strategy by a Modified Cauchy Estimator (MCE) since the weight setting plays a crucial role in the success of GWTRPCA. To implement the GWTRPCA method, we devise an optimization algorithm using an Alternating Direction Method of Multipliers (ADMM) method. Experiments on real-world datasets validate the effectiveness of our proposed method.  ( 2 min )
    Random forests, sound symbolism and Pokemon evolution. (arXiv:2301.01948v1 [cs.LG])
    This study constructs machine learning algorithms that are trained to classify samples using sound symbolism, and then it reports on an experiment designed to measure their understanding against human participants. Random forests are trained using the names of Pokemon, which are fictional video game characters, and their evolutionary status. Pokemon undergo evolution when certain in-game conditions are met. Evolution changes the appearance, abilities, and names of Pokemon. In the first experiment, we train three random forests using the sounds that make up the names of Japanese, Chinese, and Korean Pokemon to classify Pokemon into pre-evolution and post-evolution categories. We then train a fourth random forest using the results of an elicitation experiment whereby Japanese participants named previously unseen Pokemon. In Experiment 2, we reproduce those random forests with name length as a feature and compare the performance of the random forests against humans in a classification experiment whereby Japanese participants classified the names elicited in Experiment 1 into pre-and post-evolution categories. Experiment 2 reveals an issue pertaining to overfitting in Experiment 1 which we resolve using a novel cross-validation method. The results show that the random forests are efficient learners of systematic sound-meaning correspondence patterns and can classify samples with greater accuracy than the human participants.  ( 2 min )
    Optimal lower bounds for Quantum Learning via Information Theory. (arXiv:2301.02227v1 [quant-ph])
    Although a concept class may be learnt more efficiently using quantum samples as compared with classical samples in certain scenarios, Arunachalam and de Wolf (JMLR, 2018) proved that quantum learners are asymptotically no more efficient than classical ones in the quantum PAC and Agnostic learning models. They established lower bounds on sample complexity via quantum state identification and Fourier analysis. In this paper, we derive optimal lower bounds for quantum sample complexity in both the PAC and agnostic models via an information-theoretic approach. The proofs are arguably simpler, and the same ideas can potentially be used to derive optimal bounds for other problems in quantum learning theory. We then turn to a quantum analogue of the Coupon Collector problem, a classic problem from probability theory also of importance in the study of PAC learning. Arunachalam, Belovs, Childs, Kothari, Rosmanis, and de Wolf (TQC, 2020) characterized the quantum sample complexity of this problem up to constant factors. First, we show that the information-theoretic approach mentioned above provably does not yield the optimal lower bound. As a by-product, we get a natural ensemble of pure states in arbitrarily high dimensions which are not easily (simultaneously) distinguishable, while the ensemble has close to maximal Holevo information. Second, we discover that the information-theoretic approach yields an asymptotically optimal bound for an approximation variant of the problem. Finally, we derive a sharp lower bound for the Quantum Coupon Collector problem, with the exact leading order term, via the Holevo-Curlander bounds on the distinguishability of an ensemble. All the aspects of the Quantum Coupon Collector problem we study rest on properties of the spectrum of the associated Gram matrix, which may be of independent interest.  ( 3 min )
    WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution. (arXiv:2201.08157v3 [cs.CV] UPDATED)
    Exploiting image patches instead of whole images have proved to be a powerful approach to tackle various problems in image processing. Recently, Wasserstein patch priors (WPP), which are based on the comparison of the patch distributions of the unknown image and a reference image, were successfully used as data-driven regularizers in the variational formulation of superresolution. However, for each input image, this approach requires the solution of a non-convex minimization problem which is computationally costly. In this paper, we propose to learn two kind of neural networks in an unsupervised way based on WPP loss functions. First, we show how convolutional neural networks (CNNs) can be incorporated. Once the network, called WPPNet, is learned, it can be very efficiently applied to any input image. Second, we incorporate conditional normalizing flows to provide a tool for uncertainty quantification. Numerical examples demonstrate the very good performance of WPPNets for superresolution in various image classes even if the forward operator is known only approximately.  ( 2 min )
    Grape Cold Hardiness Prediction via Multi-Task Learning. (arXiv:2209.10585v4 [cs.LG] UPDATED)
    Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures such as sprinklers, heaters, and wind machines when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning approaches and show that the highest performing approach is able to significantly improve over learning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.  ( 2 min )
    Symmetry Teleportation for Accelerated Optimization. (arXiv:2205.10637v3 [cs.LG] UPDATED)
    Existing gradient-based optimization methods update parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that allows parameters to travel a large distance on the loss level set, in order to improve the convergence speed in subsequent steps. Teleportation exploits symmetries in the loss landscape of optimization problems. We derive loss-invariant group actions for test functions in optimization and multi-layer neural networks, and prove a necessary condition for teleportation to improve convergence rate. We also show that our algorithm is closely related to second order methods. Experimentally, we show that teleportation improves the convergence speed of gradient descent and AdaGrad for several optimization problems including test functions, multi-layer regressions, and MNIST classification.  ( 2 min )
    I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs. (arXiv:2206.04739v4 [cs.LG] UPDATED)
    Although machine learning on hypergraphs has attracted considerable attention, most of the works have focused on (semi-)supervised learning, which may cause heavy labeling costs and poor generalization. Recently, contrastive learning has emerged as a successful unsupervised representation learning method. Despite the prosperous development of contrastive learning in other domains, contrastive learning on hypergraphs remains little explored. In this paper, we propose TriCL (Tri-directional Contrastive Learning), a general framework for contrastive learning on hypergraphs. Its main idea is tri-directional contrast, and specifically, it aims to maximize in two augmented views the agreement (a) between the same node, (b) between the same group of nodes, and (c) between each group and its members. Together with simple but surprisingly effective data augmentation and negative sampling schemes, these three forms of contrast enable TriCL to capture both microscopic and mesoscopic structural information in node embeddings. Our extensive experiments using 13 baseline approaches, five datasets, and two tasks demonstrate the effectiveness of TriCL, and most noticeably, TriCL consistently outperforms not just unsupervised competitors but also (semi-)supervised competitors mostly by significant margins for node classification. The code and datasets are available at https://github.com/wooner49/TriCL.  ( 2 min )
    Verifying Inverse Model Neural Networks. (arXiv:2202.02429v2 [cs.LG] UPDATED)
    Inverse problems exist in a wide variety of physical domains from aerospace engineering to medical imaging. The goal is to infer the underlying state from a set of observations. When the forward model that produced the observations is nonlinear and stochastic, solving the inverse problem is very challenging. Neural networks are an appealing solution for solving inverse problems as they can be trained from noisy data and once trained are computationally efficient to run. However, inverse model neural networks do not have guarantees of correctness built-in, which makes them unreliable for use in safety and accuracy-critical contexts. In this work we introduce a method for verifying the correctness of inverse model neural networks. Our approach is to overapproximate a nonlinear, stochastic forward model with piecewise linear constraints and encode both the overapproximate forward model and the neural network inverse model as a mixed-integer program. We demonstrate this verification procedure on a real-world airplane fuel gauge case study. The ability to verify and consequently trust inverse model neural networks allows their use in a wide variety of contexts, from aerospace to medicine.  ( 2 min )
    Interpretable Learned Emergent Communication for Human-Agent Teams. (arXiv:2201.07452v2 [cs.LG] UPDATED)
    Learning interpretable communication is essential for multi-agent and human-agent teams (HATs). In multi-agent reinforcement learning for partially-observable environments, agents may convey information to others via learned communication, allowing the team to complete its task. Inspired by human languages, recent works study discrete (using only a finite set of tokens) and sparse (communicating only at some time-steps) communication. However, the utility of such communication in human-agent team experiments has not yet been investigated. In this work, we analyze the efficacy of sparse-discrete methods for producing emergent communication that enables high agent-only and human-agent team performance. We develop agent-only teams that communicate sparsely via our scheme of Enforcers that sufficiently constrain communication to any budget. Our results show no loss or minimal loss of performance in benchmark environments and tasks. In human-agent teams tested in benchmark environments, where agents have been modeled using the Enforcers, we find that a prototype-based method produces meaningful discrete tokens that enable human partners to learn agent communication faster and better than a one-hot baseline. Additional HAT experiments show that an appropriate sparsity level lowers the cognitive load of humans when communicating with teams of agents and leads to superior team performance.  ( 2 min )
    A general framework for implementing distances for categorical variables. (arXiv:2301.02190v1 [stat.ML])
    The degree to which subjects differ from each other with respect to certain properties measured by a set of variables, plays an important role in many statistical methods. For example, classification, clustering, and data visualization methods all require a quantification of differences in the observed values. We can refer to the quantification of such differences, as distance. An appropriate definition of a distance depends on the nature of the data and the problem at hand. For distances between numerical variables, there exist many definitions that depend on the size of the observed differences. For categorical data, the definition of a distance is more complex, as there is no straightforward quantification of the size of the observed differences. Consequently, many proposals exist that can be used to measure differences based on categorical variables. In this paper, we introduce a general framework that allows for an efficient and transparent implementation of distances between observations on categorical variables. We show that several existing distances can be incorporated into the framework. Moreover, our framework quite naturally leads to the introduction of new distance formulations and allows for the implementation of flexible, case and data specific distance definitions. Furthermore, in a supervised classification setting, the framework can be used to construct distances that incorporate the association between the response and predictor variables and hence improve the performance of distance-based classifiers.  ( 2 min )
    UDC: Unified DNAS for Compressible TinyML Models. (arXiv:2201.05842v4 [cs.LG] UPDATED)
    Deploying TinyML models on low-cost IoT hardware is very challenging, due to limited device memory capacity. Neural processing unit (NPU) hardware address the memory challenge by using model compression to exploit weight quantization and sparsity to fit more parameters in the same footprint. However, designing compressible neural networks (NNs) is challenging, as it expands the design space across which we must make balanced trade-offs. This paper demonstrates Unified DNAS for Compressible (UDC) NNs, which explores a large search space to generate state-of-the-art compressible NNs for NPU. ImageNet results show UDC networks are up to $3.35\times$ smaller (iso-accuracy) or 6.25% more accurate (iso-model size) than previous work.  ( 2 min )
    L-HYDRA: Multi-Head Physics-Informed Neural Networks. (arXiv:2301.02152v1 [cs.LG])
    We introduce multi-head neural networks (MH-NNs) to physics-informed machine learning, which is a type of neural networks (NNs) with all nonlinear hidden layers as the body and multiple linear output layers as multi-head. Hence, we construct multi-head physics-informed neural networks (MH-PINNs) as a potent tool for multi-task learning (MTL), generative modeling, and few-shot learning for diverse problems in scientific machine learning (SciML). MH-PINNs connect multiple functions/tasks via a shared body as the basis functions as well as a shared distribution for the head. The former is accomplished by solving multiple tasks with MH-PINNs with each head independently corresponding to each task, while the latter by employing normalizing flows (NFs) for density estimate and generative modeling. To this end, our method is a two-stage method, and both stages can be tackled with standard deep learning tools of NNs, enabling easy implementation in practice. MH-PINNs can be used for various purposes, such as approximating stochastic processes, solving multiple tasks synergistically, providing informative prior knowledge for downstream few-shot learning tasks such as meta-learning and transfer learning, learning representative basis functions, and uncertainty quantification. We demonstrate the effectiveness of MH-PINNs in five benchmarks, investigating also the possibility of synergistic learning in regression analysis. We name the open-source code "Lernaean Hydra" (L-HYDRA), since this mythical creature possessed many heads for performing important multiple tasks, as in the proposed method.  ( 2 min )
    Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task). (arXiv:2301.02113v1 [cs.CL])
    We describe three models submitted for the CODI-CRAC 2022 shared task. To perform identity anaphora resolution, we test several combinations of the incremental clustering approach based on the Workspace Coreference System (WCS) with other coreference models. The best result is achieved by adding the ''cluster merging'' version of the coref-hoi model, which brings up to 10.33% improvement 1 over vanilla WCS clustering. Discourse deixis resolution is implemented as multi-task learning: we combine the learning objective of corefhoi with anaphor type classification. We adapt the higher-order resolution model introduced in Joshi et al. (2019) for bridging resolution given gold mentions and anaphors.  ( 2 min )
    Beyond spectral gap (extended): The role of the topology in decentralized learning. (arXiv:2301.02151v1 [cs.LG])
    In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers communicate over a sparse graph, current theory fails to capture important aspects of real-world behavior. First, the `spectral gap' of the communication graph is not predictive of its empirical performance in (deep) learning. Second, current theory does not explain that collaboration enables larger learning rates than training alone. In fact, it prescribes smaller learning rates, which further decrease as graphs become larger, failing to explain convergence dynamics in infinite graphs. This paper aims to paint an accurate picture of sparsely-connected distributed optimization. We quantify how the graph topology influences convergence in a quadratic toy problem and provide theoretical results for general smooth and (strongly) convex objectives. Our theory matches empirical observations in deep learning, and accurately describes the relative merits of different graph topologies. This paper is an extension of the conference paper by Vogels et. al. (2022). Code: https://github.com/epfml/topology-in-decentralized-learning.  ( 2 min )
    Solving Multistage Stochastic Linear Programming via Regularized Linear Decision Rules: An Application to Hydrothermal Dispatch Planning. (arXiv:2110.03146v3 [math.OC] UPDATED)
    The solution of multistage stochastic linear problems (MSLP) represents a challenge for many application areas. Long-term hydrothermal dispatch planning (LHDP) materializes this challenge in a real-world problem that affects electricity markets, economies, and natural resources worldwide. No closed-form solutions are available for MSLP and the definition of non-anticipative policies with high-quality out-of-sample performance is crucial. Linear decision rules (LDR) provide an interesting simulation-based framework for finding high-quality policies for MSLP through two-stage stochastic models. In practical applications, however, the number of parameters to be estimated when using an LDR may be close to or higher than the number of scenarios of the sample average approximation problem, thereby generating an in-sample overfit and poor performances in out-of-sample simulations. In this paper, we propose a novel regularized LDR to solve MSLP based on the AdaLASSO (adaptive least absolute shrinkage and selection operator). The goal is to use the parsimony principle, as largely studied in high-dimensional linear regression models, to obtain better out-of-sample performance for LDR applied to MSLP. Computational experiments show that the overfit threat is non-negligible when using classical non-regularized LDR to solve the LHDP, one of the most studied MSLP with relevant applications. Our analysis highlights the following benefits of the proposed framework in comparison to the non-regularized benchmark: 1) significant reductions in the number of non-zero coefficients (model parsimony), 2) substantial cost reductions in out-of-sample evaluations, and 3) improved spot-price profiles.  ( 3 min )
    Semantic match: Debugging feature attribution methods in XAI for healthcare. (arXiv:2301.02080v1 [cs.AI])
    The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI and its promise to render AI devices more transparent and trustworthy. A few voices active in the medical AI space have expressed concerns on the reliability of Explainable AI techniques and especially feature attribution methods, questioning their use and inclusion in guidelines and standards. Despite valid concerns, we argue that existing criticism on the viability of post-hoc local explainability methods throws away the baby with the bathwater by generalizing a problem that is specific to image data. We begin by characterizing the problem as a lack of semantic match between explanations and human understanding. To understand when feature importance can be used reliably, we introduce a distinction between feature importance of low- and high-level features. We argue that for data types where low-level features come endowed with a clear semantics, such as tabular data like Electronic Health Records (EHRs), semantic match can be obtained, and thus feature attribution methods can still be employed in a meaningful and useful way.  ( 2 min )
    A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image. (arXiv:2301.02051v1 [cs.RO])
    Autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot's kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results.  ( 2 min )
    CA$^2$T-Net: Category-Agnostic 3D Articulation Transfer from Single Image. (arXiv:2301.02232v1 [cs.CV])
    We present a neural network approach to transfer the motion from a single image of an articulated object to a rest-state (i.e., unarticulated) 3D model. Our network learns to predict the object's pose, part segmentation, and corresponding motion parameters to reproduce the articulation shown in the input image. The network is composed of three distinct branches that take a shared joint image-shape embedding and is trained end-to-end. Unlike previous methods, our approach is independent of the topology of the object and can work with objects from arbitrary categories. Our method, trained with only synthetic data, can be used to automatically animate a mesh, infer motion from real images, and transfer articulation to functionally similar but geometrically distinct 3D models at test time.  ( 2 min )
    Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization. (arXiv:2301.02220v1 [stat.ML])
    Reinforcement learning (RL) is a powerful machine learning technique that enables an intelligent agent to learn an optimal policy that maximizes the cumulative rewards in sequential decision making. Most of methods in the existing literature are developed in \textit{online} settings where the data are easy to collect or simulate. Motivated by high stake domains such as mobile health studies with limited and pre-collected data, in this paper, we study \textit{offline} reinforcement learning methods. To efficiently use these datasets for policy optimization, we propose a novel value enhancement method to improve the performance of a given initial policy computed by existing state-of-the-art RL algorithms. Specifically, when the initial policy is not consistent, our method will output a policy whose value is no worse and often better than that of the initial policy. When the initial policy is consistent, under some mild conditions, our method will yield a policy whose value converges to the optimal one at a faster rate than the initial policy, achieving the desired ``value enhancement" property. The proposed method is generally applicable to any parametrized policy that belongs to certain pre-specified function class (e.g., deep neural networks). Extensive numerical studies are conducted to demonstrate the superior performance of our method.  ( 2 min )
    Self-Motivated Multi-Agent Exploration. (arXiv:2301.02083v1 [cs.LG])
    In cooperative multi-agent reinforcement learning (CMARL), it is critical for agents to achieve a balance between self-exploration and team collaboration. However, agents can hardly accomplish the team task without coordination and they would be trapped in a local optimum where easy cooperation is accessed without enough individual exploration. Recent works mainly concentrate on agents' coordinated exploration, which brings about the exponentially grown exploration of the state space. To address this issue, we propose Self-Motivated Multi-Agent Exploration (SMMAE), which aims to achieve success in team tasks by adaptively finding a trade-off between self-exploration and team cooperation. In SMMAE, we train an independent exploration policy for each agent to maximize their own visited state space. Each agent learns an adjustable exploration probability based on the stability of the joint team policy. The experiments on highly cooperative tasks in StarCraft II micromanagement benchmark (SMAC) demonstrate that SMMAE can explore task-related states more efficiently, accomplish coordinated behaviours and boost the learning performance.  ( 2 min )
    Reprogramming Pretrained Language Models for Protein Sequence Representation Learning. (arXiv:2301.02120v1 [cs.LG])
    Machine Learning-guided solutions for protein learning tasks have made significant headway in recent years. However, success in scientific discovery tasks is limited by the accessibility of well-defined and labeled in-domain data. To tackle the low-data constraint, recent adaptions of deep learning models pretrained on millions of protein sequences have shown promise; however, the construction of such domain-specific large-scale model is computationally expensive. Here, we propose Representation Learning via Dictionary Learning (R2DL), an end-to-end representation learning framework in which we reprogram deep models for alternate-domain tasks that can perform well on protein property prediction with significantly fewer training samples. R2DL reprograms a pretrained English language model to learn the embeddings of protein sequences, by learning a sparse linear mapping between English and protein sequence vocabulary embeddings. Our model can attain better accuracy and significantly improve the data efficiency by up to $10^5$ times over the baselines set by pretrained and standard supervised methods. To this end, we reprogram an off-the-shelf pre-trained English language transformer and benchmark it on a set of protein physicochemical prediction tasks (secondary structure, stability, homology, stability) as well as on a biomedically relevant set of protein function prediction tasks (antimicrobial, toxicity, antibody affinity).  ( 2 min )
    Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI. (arXiv:2301.01874v1 [cs.CL])
    Detecting "toxic" language in internet content is a pressing social and technical challenge. In this work, we focus on PERSPECTIVE from Jigsaw, a state-of-the-art tool that promises to score the "toxicity" of text, with a recent model update that claims impressive results (Lees et al., 2022). We seek to challenge certain normative claims about toxic language by proposing a new benchmark, Selected Adversarial SemanticS, or SASS. We evaluate PERSPECTIVE on SASS, and compare to low-effort alternatives, like zero-shot and few-shot GPT-3 prompt models, in binary classification settings. We find that PERSPECTIVE exhibits troubling shortcomings across a number of our toxicity categories. SASS provides a new tool for evaluating performance on previously undetected toxic language that avoids common normative pitfalls. Our work leads us to emphasize the importance of questioning assumptions made by tools already in deployment for toxicity detection in order to anticipate and prevent disparate harms.  ( 2 min )
    Instance-based Explanations for Gradient Boosting Machine Predictions with AXIL Weights. (arXiv:2301.01864v1 [cs.LG])
    We show that regression predictions from linear and tree-based models can be represented as linear combinations of target instances in the training data. This also holds for models constructed as ensembles of trees, including Random Forests and Gradient Boosting Machines. The weights used in these linear combinations are measures of instance importance, complementing existing measures of feature importance, such as SHAP and LIME. We refer to these measures as AXIL weights (Additive eXplanations with Instance Loadings). Since AXIL weights are additive across instances, they offer both local and global explanations. Our work contributes to the broader effort to make machine learning predictions more interpretable and explainable.  ( 2 min )
    PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series. (arXiv:2301.01838v1 [cs.LG])
    Recent rapid development of sensor technology has allowed massive fine-grained time series (TS) data to be collected and set the foundation for the development of data-driven services and applications. During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner. The high resolution of TS brings new challenges in protecting privacy. While meaningful information in high-resolution TS shifts from concrete point values to local shape-based segments, numerous research have found that long shape-based patterns could contain more sensitive information and may potentially be extracted and misused by a malicious third party. However, the privacy issue for TS patterns is surprisingly seldom explored in privacy-preserving literature. In this work, we consider a new privacy-preserving problem: preventing malicious inference on long shape-based patterns while preserving short segment information for the utility task performance. To mitigate the challenge, we investigate an alternative approach by sharing Matrix Profile (MP), which is a non-linear transformation of original data and a versatile data structure that supports many data mining tasks. We found that while MP can prevent concrete shape leakage, the canonical correlation in MP index can still reveal the location of sensitive long pattern. Based on this observation, we design two attacks named Location Attack and Entropy Attack to extract the pattern location from MP. To further protect MP from these two attacks, we propose a Privacy-Aware Matrix Profile (PMP) via perturbing the local correlation and breaking the canonical correlation in MP index vector. We evaluate our proposed PMP against baseline noise-adding methods through quantitative analysis and real-world case studies to show the effectiveness of the proposed method.  ( 2 min )
    Multi-Task Learning for Budbreak Prediction. (arXiv:2301.01815v1 [cs.LG])
    Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth. This is also when grape plants are most vulnerable to damage from freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak occurrence to protect their vineyards from late spring frost events. This work investigates deep learning for budbreak prediction using data collected for multiple grape cultivars. While some cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely impact prediction accuracy. To address this issue, we investigate multi-task learning, which combines data across all cultivars to make predictions for individual cultivars. Our main result shows that several variants of multi-task learning are all able to significantly improve prediction accuracy compared to learning for each cultivar independently.  ( 2 min )
    Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution. (arXiv:2301.02068v1 [cs.LG])
    Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. Though one could lower the complexity of Transformers by inducing the sparsity in point-wise self-attentions for LTTF, the limited information utilization prohibits the model from exploring the complex dependencies comprehensively. To this end, we propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects: (i) an encoder-decoder architecture incorporating a linear complexity without sacrificing information utilization is proposed on top of sliding-window attention and Stationary and Instant Recurrent Network (SIRN); (ii) a module derived from the normalizing flow is devised to further improve the information utilization by inferring the outputs with the latent variables in SIRN directly; (iii) the inter-series correlation and temporal dynamics in time-series data are modeled explicitly to fuel the downstream self-attention mechanism. Extensive experiments on seven real-world datasets demonstrate that Conformer outperforms the state-of-the-art methods on LTTF and generates reliable prediction results with uncertainty quantification.  ( 2 min )
    A deep learning approach to using wearable seismocardiography (SCG) for diagnosing aortic valve stenosis and predicting aortic hemodynamics obtained by 4D flow MRI. (arXiv:2301.02130v1 [cs.LG])
    In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4D flow MRI using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive assessment of cardiovascular hemodynamics, but it is costly and time-consuming. We hypothesized that deep learning could be used to identify pathological changes in blood flow, such as elevated peak systolic velocity Vmax in patients with heart valve diseases, from SCG signals. We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects who underwent same-day 4D flow MRI and SCG, we found that the Vmax values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with TAV, BAV, MAV, and AS could be classified with ROC-AUC values of 92%, 95%, 81%, and 83%, respectively. This suggests that SCG obtained using low-cost wearable electronics may be used as a supplement to 4D flow MRI exams or as a screening tool for aortic valve disease.  ( 2 min )
    Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models. (arXiv:2301.01918v1 [cs.LG])
    The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Monitoring species compositional diversity across large spatial scales is challenging and time consuming. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Earth Sensing Imaging Spectrometer) instrument provides a unique opportunity to test the potential for monitoring plant species diversity with spaceborne hyperspectral data. This study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia. Spectral features were first extracted from the DESIS spectra, then regressed against on-ground estimates of plant species richness, with a two-fold cross validation scheme to assess the predictive performance. We tested and compared the effectiveness of Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), Random Forest Regression (RFR) for species richness prediction. The best prediction results were r=0.76 and RMSE=5.89 for the Southern Tablelands region, and r=0.68 and RMSE=5.95 for the Snowy Mountains region. Relative importance analysis for the DESIS spectral bands showed that the red-edge, red, and blue spectral regions were more important for predicting plant species richness than the green bands and the near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral data performed better than Sentinel-2 multispectral data in the prediction of plant species richness. Our results provide a quantitative reference for future studies exploring the potential of spaceborne hyperspectral data for plant biodiversity mapping.  ( 2 min )
    MessageNet: Message Classification using Natural Language Processing and Meta-data. (arXiv:2301.01808v1 [cs.LG])
    In this paper we propose a new Deep Learning (DL) approach for message classification. Our method is based on the state-of-the-art Natural Language Processing (NLP) building blocks, combined with a novel technique for infusing the meta-data input that is typically available in messages such as the sender information, timestamps, attached image, audio, affiliations, and more. As we demonstrate throughout the paper, going beyond the mere text by leveraging all available channels in the message, could yield an improved representation and higher classification accuracy. To achieve message representation, each type of input is processed in a dedicated block in the neural network architecture that is suitable for the data type. Such an implementation enables training all blocks together simultaneously, and forming cross channels features in the network. We show in the Experiments Section that in some cases, message's meta-data holds an additional information that cannot be extracted just from the text, and when using this information we achieve better performance. Furthermore, we demonstrate that our multi-modality block approach outperforms other approaches for injecting the meta data to the the text classifier.  ( 2 min )
    Privacy and Efficiency of Communications in Federated Split Learning. (arXiv:2301.01824v1 [cs.LG])
    Everyday, large amounts of sensitive data \sai{is} distributed across mobile phones, wearable devices, and other sensors. Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make valuable predictions. Distributed machine learning techniques such as Federated and Split Learning have recently been developed to protect user \sai{data and} privacy better while ensuring high performance. Both of these distributed learning architectures have advantages and disadvantages. In this paper, we examine these tradeoffs and suggest a new hybrid Federated Split Learning architecture that combines the efficiency and privacy benefits of both. Our evaluation demonstrates how our hybrid Federated Split Learning approach can lower the amount of processing power required by each client running a distributed learning system, reduce training and inference time while keeping a similar accuracy. We also discuss the resiliency of our approach to deep learning privacy inference attacks and compare our solution to other recently proposed benchmarks.  ( 2 min )
    Fragment-based t-SMILES for de novo molecular generation. (arXiv:2301.01829v1 [cs.LG])
    At present, sequence-based and graph-based models are two of popular used molecular generative models. In this study, we introduce a general-purposed, fragment-based, hierarchical molecular representation named t-SMILES (tree-based SMILES) which describes molecules using a SMILES-type string obtained by doing breadth first search (BFS) on full binary molecular tree formed from fragmented molecular graph. The proposed t-SMILES combines the advantages of graph model paying more attention to molecular topology structure and language model possessing powerful learning ability. Experiments with feature tree rooted JTVAE and chemical reaction-based BRICS molecular decomposing algorithms using sequence-based autoregressive generation models on three popular molecule datasets including Zinc, QM9 and ChEMBL datasets indicate that t-SMILES based models significantly outperform previously proposed fragment-based models and being competitive with classical SMILES based and graph-based approaches. Most importantly, we proposed a new perspective for fragment based molecular designing. Hence, SOTA powerful sequence-based solutions could be easily applied for fragment based molecular tasks.  ( 2 min )
    Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural Network. (arXiv:2301.01850v1 [stat.AP])
    We propose to integrate weapon system features (such as weapon system manufacturer, deployment time and location, storage time and location, etc.) into a parameterized Cox-Weibull reliability model via a neural network, like DeepSurv, to improve predictive maintenance. In parallel, we develop an alternative Bayesian model by parameterizing the Weibull parameters with a neural network and employing dropout methods such as Monte-Carlo (MC)-dropout for comparative purposes. Due to data collection procedures in weapon system testing we employ a novel interval-censored log-likelihood which incorporates Monte-Carlo Markov Chain (MCMC) sampling of the Weibull parameters during gradient descent optimization. We compare classification metrics such as receiver operator curve (ROC), area under the curve (AUC), and F scores and show that our model generally outperforms traditional powerful models such as XGBoost as well as the current standard conditional Weibull probability density estimation model.  ( 2 min )
    A first-order augmented Lagrangian method for constrained minimax optimization. (arXiv:2301.02060v1 [math.OC])
    In this paper we study a class of constrained minimax problems. In particular, we propose a first-order augmented Lagrangian method for solving them, whose subproblems turn out to be a much simpler structured minimax problem and are suitably solved by a first-order method recently developed in [26] by the authors. Under some suitable assumptions, an \emph{operation complexity} of ${\cal O}(\varepsilon^{-4}\log\varepsilon^{-1})$, measured by its fundamental operations, is established for the first-order augmented Lagrangian method for finding an $\varepsilon$-KKT solution of the constrained minimax problems.  ( 2 min )
    A Protocol for Intelligible Interaction Between Agents That Learn and Explain. (arXiv:2301.01819v1 [cs.AI])
    Recent engineering developments have seen the emergence of Machine Learning (ML) as a powerful form of data analysis with widespread applicability beyond its historical roots in the design of autonomous agents. However, relatively little attention has been paid to the interaction between people and ML systems. Recent developments on Explainable ML address this by providing visual and textual information on how the ML system arrived at a conclusion. In this paper we view the interaction between humans and ML systems within the broader context of interaction between agents capable of learning and explanation. Within this setting, we argue that it is more helpful to view the interaction as characterised by two-way intelligibility of information rather than once-off explanation of a prediction. We formulate two-way intelligibility as a property of a communication protocol. Development of the protocol is motivated by a set of `Intelligibility Axioms' for decision-support systems that use ML with a human-in-the-loop. The axioms are intended as sufficient criteria to claim that: (a) information provided by a human is intelligible to an ML system; and (b) information provided by an ML system is intelligible to a human. The axioms inform the design of a general synchronous interaction model between agents capable of learning and explanation. We identify conditions of compatibility between agents that result in bounded communication, and define Weak and Strong Two-Way Intelligibility between agents as properties of the communication protocol.  ( 2 min )
    Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization. (arXiv:2105.08666v3 [cs.LG] UPDATED)
    Reinforcement learning (RL) has made remarkable progress in many decision-making tasks, such as Go, game playing, and robotics control. However, classic RL approaches often presume that all actions can be executed an infinite number of times, which is inconsistent with many decision-making scenarios in which actions have limited budgets or execution opportunities. Imagine an agent playing a gunfighting game with limited ammunition. It only fires when the enemy appears in the correct position, making shooting a sparse-executing action. Such sparse-executing action has not been considered by classic RL algorithms in problem formulation or effective algorithms design. This paper attempts to address sparse-executing action issues by first formalizing the problem as a Sparse Action Markov Decision Process (SA-MDP), in which certain actions in the action space can only be executed for limited amounts of time. Then, we propose a policy optimization algorithm called Action Sparsity REgularization (ASRE) that gives each action a distinct preference. ASRE evaluates action sparsity through constrained action sampling and regularizes policy training based on the evaluated action sparsity, represented by action distribution. Experiments on tasks with known sparse-executing actions, where classical RL algorithms struggle to train policy efficiently, ASRE effectively constrains the action sampling and outperforms baselines. Moreover, we present that ASRE can generally improve the performance in Atari games, demonstrating its broad applicability  ( 2 min )
    Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach. (arXiv:2301.01801v1 [cs.LG])
    Fair resource allocation is one of the most important topics in communication networks. Existing solutions almost exclusively assume each user utility function is known and concave. This paper seeks to answer the following question: how to allocate resources when utility functions are unknown, even to the users? This answer has become increasingly important in the next-generation AI-aware communication networks where the user utilities are complex and their closed-forms are hard to obtain. In this paper, we provide a new solution using a distributed and data-driven bilevel optimization approach, where the lower level is a distributed network utility maximization (NUM) algorithm with concave surrogate utility functions, and the upper level is a data-driven learning algorithm to find the best surrogate utility functions that maximize the sum of true network utility. The proposed algorithm learns from data samples (utility values or gradient values) to autotune the surrogate utility functions to maximize the true network utility, so works for unknown utility functions. For the general network, we establish the nonasymptotic convergence rate of the proposed algorithm with nonconcave utility functions. The simulations validate our theoretical results and demonstrate the great effectiveness of the proposed method in a real-world network.  ( 2 min )
    Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response. (arXiv:2301.01769v1 [q-bio.GN])
    There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source. Understanding how gene expression is modulated after ionizing radiation exposure and investigating the presence of any dose-dependent gene expression patterns have broad implications for health risks from radiotherapy, medical radiation diagnostic procedures, as well as other environmental exposure. In this paper, we perform a comprehensive pathway-based analysis of gene expression profiles in response to low-dose radiation exposure, in order to examine the potential mechanism of gene regulation underlying such responses. To accomplish this goal, we employ a statistical framework to determine whether a specific group of genes belonging to a known pathway display coordinated expression patterns that are modulated in a manner consistent with the radiation level. Findings in our study suggest that there exist complex yet consistent signatures that reflect the molecular response to radiation exposure, which differ between low-dose and high-dose radiation.  ( 2 min )
    Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations. (arXiv:2301.02184v1 [cs.CV])
    Can conversational videos captured from multiple egocentric viewpoints reveal the map of a scene in a cost-efficient way? We seek to answer this question by proposing a new problem: efficiently building the map of a previously unseen 3D environment by exploiting shared information in the egocentric audio-visual observations of participants in a natural conversation. Our hypothesis is that as multiple people ("egos") move in a scene and talk among themselves, they receive rich audio-visual cues that can help uncover the unseen areas of the scene. Given the high cost of continuously processing egocentric visual streams, we further explore how to actively coordinate the sampling of visual information, so as to minimize redundancy and reduce power use. To that end, we present an audio-visual deep reinforcement learning approach that works with our shared scene mapper to selectively turn on the camera to efficiently chart out the space. We evaluate the approach using a state-of-the-art audio-visual simulator for 3D scenes as well as real-world video. Our model outperforms previous state-of-the-art mapping methods, and achieves an excellent cost-accuracy tradeoff. Project: this http URL  ( 2 min )
    PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching. (arXiv:2301.01932v1 [cs.CV])
    Graph matching can be formalized as a combinatorial optimization problem, where there are corresponding relationships between pairs of nodes that can be represented as edges. This problem becomes challenging when there are potential ambiguities present due to nodes and edges with high similarity, and there is a need to find accurate results for similar content matching. In this paper, we introduce a novel end-to-end neural network that can map the linear assignment problem into a high-dimensional space augmented with node-level relative position information, which is crucial for improving the method's performance for similar content matching. Our model constructs the anchor set for the relative position of nodes and then aggregates the feature information of the target node and each anchor node based on a measure of relative position. It then learns the node feature representation by integrating the topological structure and the relative position information, thus realizing the linear assignment between the two graphs. To verify the effectiveness and generalizability of our method, we conduct graph matching experiments, including cross-category matching, on different real-world datasets. Comparisons with different baselines demonstrate the superiority of our method. Our source code is available under https://github.com/anonymous.  ( 2 min )
    RePAD: Real-time Proactive Anomaly Detection for Time Series. (arXiv:2001.08922v7 [cs.LG] UPDATED)
    During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historic data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.  ( 2 min )
    fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems. (arXiv:2301.01807v1 [cs.LG])
    We discuss our simulation tool, fintech-kMC, which is designed to generate synthetic data for machine learning model development and testing. fintech-kMC is an agent-based model driven by a kinetic Monte Carlo (a.k.a. continuous time Monte Carlo) engine which simulates the behaviour of customers using an online digital financial platform. The tool provides an interpretable, reproducible, and realistic way of generating synthetic data which can be used to validate and test AI/ML models and pipelines to be used in real-world customer-facing financial applications.  ( 2 min )
    Comparing Ordering Strategies For Process Discovery Using Synthesis Rules. (arXiv:2301.02182v1 [cs.DB])
    Process discovery aims to learn process models from observed behaviors, i.e., event logs, in the information systems.The discovered models serve as the starting point for process mining techniques that are used to address performance and compliance problems. Compared to the state-of-the-art Inductive Miner, the algorithm applying synthesis rules from the free-choice net theory discovers process models with more flexible (non-block) structures while ensuring the same desirable soundness and free-choiceness properties. Moreover, recent development in this line of work shows that the discovered models have compatible quality. Following the synthesis rules, the algorithm incrementally modifies an existing process model by adding the activities in the event log one at a time. As the applications of rules are highly dependent on the existing model structure, the model quality and computation time are significantly influenced by the order of adding activities. In this paper, we investigate the effect of different ordering strategies on the discovered models (w.r.t. fitness and precision) and the computation time using real-life event data. The results show that the proposed ordering strategy can improve the quality of the resulting process models while requiring less time compared to the ordering strategy solely based on the frequency of activities.  ( 2 min )
    Playing hide and seek: tackling in-store picking operations while improving customer experience. (arXiv:2301.02142v1 [cs.LG])
    The evolution of the retail business presents new challenges and raises pivotal questions on how to reinvent stores and supply chains to meet the growing demand of the online channel. One of the recent measures adopted by omnichannel retailers is to address the growth of online sales using in-store picking, which allows serving online orders using existing assets. However, it comes with the downside of harming the offline customer experience. To achieve picking policies adapted to the dynamic customer flows of a retail store, we formalize a new problem called Dynamic In-store Picker Routing Problem (diPRP). In this relevant problem - diPRP - a picker tries to pick online orders while minimizing customer encounters. We model the problem as a Markov Decision Process (MDP) and solve it using a hybrid solution approach comprising mathematical programming and reinforcement learning components. Computational experiments on synthetic instances suggest that the algorithm converges to efficient policies. Furthermore, we apply our approach in the context of a large European retailer to assess the results of the proposed policies regarding the number of orders picked and customers encountered. Our work suggests that retailers should be able to scale the in-store picking of online orders without jeopardizing the experience of offline customers. The policies learned using the proposed solution approach reduced the number of customer encounters by more than 50% when compared to policies solely focused on picking orders. Thus, to pursue omnichannel strategies that adequately trade-off operational efficiency and customer experience, retailers cannot rely on actual simplistic picking strategies, such as choosing the shortest possible route.  ( 2 min )
    Differentially Private Federated Learning on Heterogeneous Data. (arXiv:2111.09278v3 [cs.LG] UPDATED)
    Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two key challenges: (i) efficient training from highly heterogeneous user data, and (ii) protecting the privacy of participating users. In this work, we propose a novel FL approach (DP-SCAFFOLD) to tackle these two challenges together by incorporating Differential Privacy (DP) constraints into the popular SCAFFOLD algorithm. We focus on the challenging setting where users communicate with a "honest-but-curious" server without any trusted intermediary, which requires to ensure privacy not only towards a third-party with access to the final model but also towards the server who observes all user communications. Using advanced results from DP theory, we establish the convergence of our algorithm for convex and non-convex objectives. Our analysis clearly highlights the privacy-utility trade-off under data heterogeneity, and demonstrates the superiority of DP-SCAFFOLD over the state-of-the-art algorithm DP-FedAvg when the number of local updates and the level of heterogeneity grow. Our numerical results confirm our analysis and show that DP-SCAFFOLD provides significant gains in practice.  ( 2 min )
    Unsupervised Manifold Linearizing and Clustering. (arXiv:2301.01805v1 [cs.LG])
    Clustering data lying close to a union of low-dimensional manifolds, with each manifold as a cluster, is a fundamental problem in machine learning. When the manifolds are assumed to be linear subspaces, many methods succeed using low-rank and sparse priors, which have been studied extensively over the past two decades. Unfortunately, most real-world datasets can not be well approximated by linear subspaces. On the other hand, several works have proposed to identify the manifolds by learning a feature map such that the data transformed by the map lie in a union of linear subspaces, even though the original data are from non-linear manifolds. However, most works either assume knowledge of the membership of samples to clusters, or are shown to learn trivial representations. In this paper, we propose to simultaneously perform clustering and learn a union-of-subspace representation via Maximal Coding Rate Reduction. Experiments on synthetic and realistic datasets show that the proposed method achieves clustering accuracy comparable with state-of-the-art alternatives, while being more scalable and learning geometrically meaningful representations.  ( 2 min )
    FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion. (arXiv:2301.02110v1 [cs.CV])
    Fashion-image editing represents a challenging computer vision task, where the goal is to incorporate selected apparel into a given input image. Most existing techniques, known as Virtual Try-On methods, deal with this task by first selecting an example image of the desired apparel and then transferring the clothing onto the target person. Conversely, in this paper, we consider editing fashion images with text descriptions. Such an approach has several advantages over example-based virtual try-on techniques, e.g.: (i) it does not require an image of the target fashion item, and (ii) it allows the expression of a wide variety of visual concepts through the use of natural language. Existing image-editing methods that work with language inputs are heavily constrained by their requirement for training sets with rich attribute annotations or they are only able to handle simple text descriptions. We address these constraints by proposing a novel text-conditioned editing model, called FICE (Fashion Image CLIP Editing), capable of handling a wide variety of diverse text descriptions to guide the editing procedure. Specifically with FICE, we augment the common GAN inversion process by including semantic, pose-related, and image-level constraints when generating images. We leverage the capabilities of the CLIP model to enforce the semantics, due to its impressive image-text association capabilities. We furthermore propose a latent-code regularization technique that provides the means to better control the fidelity of the synthesized images. We validate FICE through rigorous experiments on a combination of VITON images and Fashion-Gen text descriptions and in comparison with several state-of-the-art text-conditioned image editing approaches. Experimental results demonstrate FICE generates highly realistic fashion images and leads to stronger editing performance than existing competing approaches.  ( 2 min )
    NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. (arXiv:2301.01849v1 [cs.LG])
    Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science. However, modeling real-world systems remain challenging, as most existing algorithms assume that the underlying causal graph is acyclic. While this is a convenient framework for developing theoretical developments about causal reasoning and inference, the underlying modeling assumption is likely to be violated in real systems, because feedback loops are common (e.g., in biological systems). Although a few methods search for cyclic causal models, they usually rely on some form of linearity, which is also limiting, or lack a clear underlying probabilistic model. In this work, we propose a novel framework for learning nonlinear cyclic causal graphical models from interventional data, called NODAGS-Flow. We perform inference via direct likelihood optimization, employing techniques from residual normalizing flows for likelihood estimation. Through synthetic experiments and an application to single-cell high-content perturbation screening data, we show significant performance improvements with our approach compared to state-of-the-art methods with respect to structure recovery and predictive performance.  ( 2 min )
    Structured Sparsity Inducing Adaptive Optimizers for Deep Learning. (arXiv:2102.03869v2 [cs.LG] UPDATED)
    The parameters of a neural network are naturally organized in groups, some of which might not contribute to its overall performance. To prune out unimportant groups of parameters, we can include some non-differentiable penalty to the objective function, and minimize it using proximal gradient methods. In this paper, we derive the weighted proximal operator, which is a necessary component of these proximal methods, of two structured sparsity inducing penalties. Moreover, they can be approximated efficiently with a numerical solver, and despite this approximation, we prove that existing convergence guarantees are preserved when these operators are integrated as part of a generic adaptive proximal method. Finally, we show that this adaptive method, together with the weighted proximal operators derived here, is indeed capable of finding solutions with structure in their sparsity patterns, on representative examples from computer vision and natural language processing.  ( 2 min )
    On the Convergence Properties of Optimal AdaBoost. (arXiv:1212.1108v3 [cs.LG] UPDATED)
    AdaBoost is one of the most popular ML algorithms. It is simple to implement and often found very effective by practitioners, while still being mathematically elegant and theoretically sound. AdaBoost's interesting behavior in practice still puzzles the ML community. We address the algorithm's stability and establish multiple convergence properties of "Optimal AdaBoost," a term coined by Rudin, Daubechies, and Schapire in 2004. We prove, in a reasonably strong computational sense, the almost universal existence of time averages, and with that, the convergence of the classifier itself, its generalization error, and its resulting margins, among many other objects, for fixed data sets under arguably reasonable conditions. Specifically, we frame Optimal AdaBoost as a dynamical system and, employing tools from ergodic theory, prove that, under a condition that Optimal AdaBoost does not have ties for best weak classifier eventually, a condition for which we provide empirical evidence from high dimensional real-world datasets, the algorithm's update behaves like a continuous map. We provide constructive proofs of several arbitrarily accurate approximations of Optimal AdaBoost; prove that they exhibit certain cycling behavior in finite time, and that the resulting dynamical system is ergodic; and establish sufficient conditions for the same to hold for the actual Optimal-AdaBoost update. We believe that our results provide reasonably strong evidence for the affirmative answer to two open conjectures, at least from a broad computational-theory perspective: AdaBoost always cycles and is an ergodic dynamical system. We present empirical evidence that cycles are hard to detect while time averages stabilize quickly. Our results ground future convergence-rate analysis and may help optimize generalization ability and alleviate a practitioner's burden of deciding how long to run the algorithm.  ( 3 min )
    Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes. (arXiv:2301.01771v1 [cs.LG])
    Different factors have effects on traffic crashes and crash-related injuries. These factors include segment characteristics, crash-level characteristics, occupant level characteristics, environment characteristics, and vehicle level characteristics. There are several studies regarding these factors' effects on crash injuries. However, limited studies have examined the effects of pre-crash events on injuries, especially for curve-related crashes. The majority of previous studies for curve-related crashes focused on the impact of geometric features or street design factors. The current study tries to eliminate the aforementioned shortcomings by considering important pre-crash events related factors as selected variables and the number of vehicles with or without injury as the predicted variable. This research used CRSS data from the National Highway Traffic Safety Administration (NHTSA), which includes traffic crash-related data for different states in the USA. The relationships are explored using different machine learning algorithms like the random forest, C5.0, CHAID, Bayesian Network, Neural Network, C\&R Tree, Quest, etc. The random forest and SHAP values are used to identify the most effective variables. The C5.0 algorithm, which has the highest accuracy rate among the other algorithms, is used to develop the final model. Analysis results revealed that the extent of the damage, critical pre-crash event, pre-impact location, the trafficway description, roadway surface condition, the month of the crash, the first harmful event, number of motor vehicles, attempted avoidance maneuver, and roadway grade affect the number of vehicles with or without injury in curve-related crashes.  ( 2 min )
    Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions. (arXiv:2301.02059v1 [cs.NI])
    Domain-wide recognized by their high value in human presence and activity studies, cellular network datasets (i.e., Charging Data Records, named CdRs), however, present accessibility, usability, and privacy issues, restricting their exploitation and research reproducibility.This paper tackles such challenges by modeling Cdrs that fulfill real-world data attributes. Our designed framework, named Zen follows a four-fold methodology related to (i) the LTSM-based modeling of users' traffic behavior, (ii) the realistic and flexible emulation of spatiotemporal mobility behavior, (iii) the structure of lifelike cellular network infrastructure and social interactions, and (iv) the combination of the three previous modules into realistic Cdrs traces with an individual basis, realistically. Results show that Zen's first and third models accurately capture individual and global distributions of a fully anonymized real-world Cdrs dataset, while the second model is consistent with the literature's revealed features in human mobility. Finally, we validate Zen Cdrs ability of reproducing daily cellular behaviors of the urban population and its usefulness in practical networking applications such as dynamic population tracing, Radio Access Network's power savings, and anomaly detection as compared to real-world CdRs.  ( 2 min )
    SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph. (arXiv:2301.01949v1 [cs.CL])
    Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when complex relative positions and information alignments are involved, which poses a bottleneck in response quality. In this paper, we propose a Situated Conversation Agent Petrained with Multimodal Questions from INcremental Layout Graph (SPRING) with abilities of reasoning multi-hops spatial relations and connecting them with visual attributes in crowded situated scenarios. Specifically, we design two types of Multimodal Question Answering (MQA) tasks to pretrain the agent. All QA pairs utilized during pretraining are generated from novel Incremental Layout Graphs (ILG). QA pair difficulty labels automatically annotated by ILG are used to promote MQA-based Curriculum Learning. Experimental results verify the SPRING's effectiveness, showing that it significantly outperforms state-of-the-art approaches on both SIMMC 1.0 and SIMMC 2.0 datasets.  ( 2 min )
    Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting. (arXiv:2301.01832v1 [cs.LG])
    The forecast of electrical loads is essential for the planning and operation of the power system. Recently, advances in deep learning have enabled more accurate forecasts. However, deep neural networks are prone to adversarial attacks. Although most of the literature focuses on integrity-based attacks, this paper proposes availability-based adversarial attacks, which can be more easily implemented by attackers. For each forecast instance, the availability attack position is optimally solved by mixed-integer reformulation of the artificial neural network. To tackle this attack, an adversarial training algorithm is proposed. In simulation, a realistic load forecasting dataset is considered and the attack performance is compared to the integrity-based attack. Meanwhile, the adversarial training algorithm is shown to significantly improve robustness against availability attacks. All codes are available at https://github.com/xuwkk/AAA_Load_Forecast.  ( 2 min )
    Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS. (arXiv:2301.01817v1 [cs.LG])
    Causal modeling provides us with powerful counterfactual reasoning and interventional mechanism to generate predictions and reason under various what-if scenarios. However, causal discovery using observation data remains a nontrivial task due to unobserved confounding factors, finite sampling, and changes in the data distribution. These can lead to spurious cause-effect relationships. To mitigate these challenges in practice, researchers augment causal learning with known causal relations. The goal of the paper is to study the impact of expert knowledge on causal relations in the form of additional constraints used in the formulation of the nonparametric NOTEARS. We provide a comprehensive set of comparative analyses of biasing the model using different types of knowledge. We found that (i) knowledge that corrects the mistakes of the NOTEARS model can lead to statistically significant improvements, (ii) constraints on active edges have a larger positive impact on causal discovery than inactive edges, and surprisingly, (iii) the induced knowledge does not correct on average more incorrect active and/or inactive edges than expected. We also demonstrate the behavior of the model and the effectiveness of domain knowledge on a real-world dataset.  ( 2 min )
    Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI. (arXiv:2301.02033v1 [eess.IV])
    First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternatives for speeding up reconstructions. However, these approaches require fully sampled data for training, which is not possible to obtain, particularly high-resolution FPP-CMR images. Here, we propose a physics-informed self-supervised deep learning FPP-CMR reconstruction approach for accelerating FPP-CMR scans and hence facilitate high spatial resolution imaging. The proposed method provides high-quality FPP-CMR images from 10x undersampled data without using fully sampled reference data.  ( 2 min )
    Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms. (arXiv:2301.02053v1 [cs.DS])
    Diversity maximization aims to select a diverse and representative subset of items from a large dataset. It is a fundamental optimization task that finds applications in data summarization, feature selection, web search, recommender systems, and elsewhere. However, in a setting where data items are associated with different groups according to sensitive attributes like sex or race, it is possible that algorithmic solutions for this task, if left unchecked, will under- or over-represent some of the groups. Therefore, we are motivated to address the problem of \emph{max-min diversification with fairness constraints}, aiming to select $k$ items to maximize the minimum distance between any pair of selected items while ensuring that the number of items selected from each group falls within predefined lower and upper bounds. In this work, we propose an exact algorithm based on integer linear programming that is suitable for small datasets as well as a $\frac{1-\varepsilon}{5}$-approximation algorithm for any $\varepsilon \in (0, 1)$ that scales to large datasets. Extensive experiments on real-world datasets demonstrate the superior performance of our proposed algorithms over existing ones.  ( 2 min )
    Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. (arXiv:2301.02039v1 [cs.LG])
    Randomized smoothing is one of the most promising frameworks for certifying the adversarial robustness of machine learning models, including Graph Neural Networks (GNNs). Yet, existing randomized smoothing certificates for GNNs are overly pessimistic since they treat the model as a black box, ignoring the underlying architecture. To remedy this, we propose novel gray-box certificates that exploit the message-passing principle of GNNs: We randomly intercept messages and carefully analyze the probability that messages from adversarially controlled nodes reach their target nodes. Compared to existing certificates, we certify robustness to much stronger adversaries that control entire nodes in the graph and can arbitrarily manipulate node features. Our certificates provide stronger guarantees for attacks at larger distances, as messages from farther-away nodes are more likely to get intercepted. We demonstrate the effectiveness of our method on various models and datasets. Since our gray-box certificates consider the underlying graph structure, we can significantly improve certifiable robustness by applying graph sparsification.  ( 2 min )
  • Open

    Plasticity Neural Network Based on Astrocytic effects at Critical Period, Synaptic Competition and Strength Rebalance by Current and Mnemonic Brain Plasticity and Synapse Formation. (arXiv:2203.11740v8 [cs.NE] UPDATED)
    In addition to the shared weights of synaptic connections, PNN includes weights of synaptic ranges for Forward propagation and Back propagation[15,16,19-24]. PNN considers synaptic strength balance in dynamic of phagocytosing of synapses and static of constant sum of synapses length [15],the lead behavior of the school of fish is well embodied in our PNN. Synapse formation will inhibit dendrites generation to a certain extent in experiments, by simulations synapse formation will inhibit the function of dendrites [16]. Closing the critical period will cause neurological disorder in experiments, but worse results in PNN simulations [19]. The memory persistence gradient information of backward circuit similar to the Enforcing Resilience in a Spring Boot. The relatively good and inferior gradient information in synapse formation of backward circuit like the folds of the brain. Considering both negative and positive memories persistence help activate synapse length changes with iterations better than only considering positive memory. So using memory of fear learning and improving of synaptic activity to observe obviously [20]. Memory persistence factor also inhibit local synaptic accumulation. And refers PNN can also introduce the relatively good and inferior solution to update the velocity of particle in PSO. Astrocytic phagocytosis will avoid the local accumulation of synapses by simulation (Lack of astrocytic phagocytosis causes excitatory synapses and functionally impaired synapses accumulate in experiments and lead to destruction of cognition, but local longer synapses and worse results in PNN simulations) [21]. It gives relationship of human intelligence and cortical thickness, individual differences in brain[22].PNN also considered the memory engram cells that strengthened Synaptic strength[23]. The simple PNN which only has the synaptic phagocytosis.  ( 3 min )
    How Does Sharpness-Aware Minimization Minimize Sharpness?. (arXiv:2211.05729v2 [cs.LG] UPDATED)
    Sharpness-Aware Minimization (SAM) is a highly effective regularization technique for improving the generalization of deep neural networks for various settings. However, the underlying working of SAM remains elusive because of various intriguing approximations in the theoretical characterizations. SAM intends to penalize a notion of sharpness of the model but implements a computationally efficient variant; moreover, a third notion of sharpness was used for proving generalization guarantees. The subtle differences in these notions of sharpness can indeed lead to significantly different empirical results. This paper rigorously nails down the exact sharpness notion that SAM regularizes and clarifies the underlying mechanism. We also show that the two steps of approximations in the original motivation of SAM individually lead to inaccurate local conclusions, but their combination accidentally reveals the correct effect, when full-batch gradients are applied. Furthermore, we also prove that the stochastic version of SAM in fact regularizes the third notion of sharpness mentioned above, which is most likely to be the preferred notion for practical performance. The key mechanism behind this intriguing phenomenon is the alignment between the gradient and the top eigenvector of Hessian when SAM is applied.  ( 2 min )
    FREDE: Anytime Graph Embeddings. (arXiv:2006.04746v2 [cs.LG] UPDATED)
    Low-dimensional representations, or embeddings, of a graph's nodes facilitate several practical data science and data engineering tasks. As such embeddings rely, explicitly or implicitly, on a similarity measure among nodes, they require the computation of a quadratic similarity matrix, inducing a tradeoff between space complexity and embedding quality. To date, no graph embedding work combines (i) linear space complexity, (ii) a nonlinear transform as its basis, and (iii) nontrivial quality guarantees. In this paper we introduce FREDE (FREquent Directions Embedding), a graph embedding based on matrix sketching that combines those three desiderata. Starting out from the observation that embedding methods aim to preserve the covariance among the rows of a similarity matrix}, FREDE iteratively improves on quality while individually processing rows of a nonlinearly transformed PPR similarity matrix derived from a state-of-the-art graph embedding method} and provides, at any iteration, column-covariance approximation guarantees in due course almost indistinguishable from those of the optimal approximation by SVD. Our experimental evaluation on variably sized networks shows that FREDE performs almost as well as SVD and competitively against state-of-the-art embedding methods in diverse data science tasks, even when it is based on as little as 10% of node similarities.  ( 2 min )
    Deep Reinforcement Learning in a Monetary Model. (arXiv:2104.09368v2 [econ.EM] UPDATED)
    We propose using deep reinforcement learning to solve dynamic stochastic general equilibrium models. Agents are represented by deep artificial neural networks and learn to solve their dynamic optimisation problem by interacting with the model environment, of which they have no a priori knowledge. Deep reinforcement learning offers a flexible yet principled way to model bounded rationality within this general class of models. We apply our proposed approach to a classical model from the adaptive learning literature in macroeconomics which looks at the interaction of monetary and fiscal policy. We find that, contrary to adaptive learning, the artificially intelligent household can solve the model in all policy regimes.  ( 2 min )
    NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. (arXiv:2301.01849v1 [cs.LG])
    Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science. However, modeling real-world systems remain challenging, as most existing algorithms assume that the underlying causal graph is acyclic. While this is a convenient framework for developing theoretical developments about causal reasoning and inference, the underlying modeling assumption is likely to be violated in real systems, because feedback loops are common (e.g., in biological systems). Although a few methods search for cyclic causal models, they usually rely on some form of linearity, which is also limiting, or lack a clear underlying probabilistic model. In this work, we propose a novel framework for learning nonlinear cyclic causal graphical models from interventional data, called NODAGS-Flow. We perform inference via direct likelihood optimization, employing techniques from residual normalizing flows for likelihood estimation. Through synthetic experiments and an application to single-cell high-content perturbation screening data, we show significant performance improvements with our approach compared to state-of-the-art methods with respect to structure recovery and predictive performance.  ( 2 min )
    On the Convergence Properties of Optimal AdaBoost. (arXiv:1212.1108v3 [cs.LG] UPDATED)
    AdaBoost is one of the most popular ML algorithms. It is simple to implement and often found very effective by practitioners, while still being mathematically elegant and theoretically sound. AdaBoost's interesting behavior in practice still puzzles the ML community. We address the algorithm's stability and establish multiple convergence properties of "Optimal AdaBoost," a term coined by Rudin, Daubechies, and Schapire in 2004. We prove, in a reasonably strong computational sense, the almost universal existence of time averages, and with that, the convergence of the classifier itself, its generalization error, and its resulting margins, among many other objects, for fixed data sets under arguably reasonable conditions. Specifically, we frame Optimal AdaBoost as a dynamical system and, employing tools from ergodic theory, prove that, under a condition that Optimal AdaBoost does not have ties for best weak classifier eventually, a condition for which we provide empirical evidence from high dimensional real-world datasets, the algorithm's update behaves like a continuous map. We provide constructive proofs of several arbitrarily accurate approximations of Optimal AdaBoost; prove that they exhibit certain cycling behavior in finite time, and that the resulting dynamical system is ergodic; and establish sufficient conditions for the same to hold for the actual Optimal-AdaBoost update. We believe that our results provide reasonably strong evidence for the affirmative answer to two open conjectures, at least from a broad computational-theory perspective: AdaBoost always cycles and is an ergodic dynamical system. We present empirical evidence that cycles are hard to detect while time averages stabilize quickly. Our results ground future convergence-rate analysis and may help optimize generalization ability and alleviate a practitioner's burden of deciding how long to run the algorithm.  ( 3 min )
    Solving Multistage Stochastic Linear Programming via Regularized Linear Decision Rules: An Application to Hydrothermal Dispatch Planning. (arXiv:2110.03146v3 [math.OC] UPDATED)
    The solution of multistage stochastic linear problems (MSLP) represents a challenge for many application areas. Long-term hydrothermal dispatch planning (LHDP) materializes this challenge in a real-world problem that affects electricity markets, economies, and natural resources worldwide. No closed-form solutions are available for MSLP and the definition of non-anticipative policies with high-quality out-of-sample performance is crucial. Linear decision rules (LDR) provide an interesting simulation-based framework for finding high-quality policies for MSLP through two-stage stochastic models. In practical applications, however, the number of parameters to be estimated when using an LDR may be close to or higher than the number of scenarios of the sample average approximation problem, thereby generating an in-sample overfit and poor performances in out-of-sample simulations. In this paper, we propose a novel regularized LDR to solve MSLP based on the AdaLASSO (adaptive least absolute shrinkage and selection operator). The goal is to use the parsimony principle, as largely studied in high-dimensional linear regression models, to obtain better out-of-sample performance for LDR applied to MSLP. Computational experiments show that the overfit threat is non-negligible when using classical non-regularized LDR to solve the LHDP, one of the most studied MSLP with relevant applications. Our analysis highlights the following benefits of the proposed framework in comparison to the non-regularized benchmark: 1) significant reductions in the number of non-zero coefficients (model parsimony), 2) substantial cost reductions in out-of-sample evaluations, and 3) improved spot-price profiles.  ( 3 min )
    Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty. (arXiv:2210.00898v2 [cs.LG] UPDATED)
    We present a novel $Q$-learning algorithm to solve distributionally robust Markov decision problems, where the corresponding ambiguity set of transition probabilities for the underlying Markov decision process is a Wasserstein ball around a (possibly estimated) reference measure. We prove convergence of the presented algorithm and provide several examples also using real data to illustrate both the tractability of our algorithm as well as the benefits of considering distributional robustness when solving stochastic optimal control problems, in particular when the estimated distributions turn out to be misspecified in practice.  ( 2 min )
    Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization. (arXiv:2205.13209v2 [cs.LG] UPDATED)
    Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i.e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable of learning CO solvers less relying on problem-specific expert domain knowledge (heuristic method) and supervised labeled data (supervised learning method). This paper presents a novel training scheme, Sym-NCO, which is a regularizer-based training scheme that leverages universal symmetricities in various CO problems and solutions. Leveraging symmetricities such as rotational and reflectional invariance can greatly improve the generalization capability of DRL-NCO because it allows the learned solver to exploit the commonly shared symmetricities in the same CO problem class. Our experimental results verify that our Sym-NCO greatly improves the performance of DRL-NCO methods in four CO tasks, including the traveling salesman problem (TSP), capacitated vehicle routing problem (CVRP), prize collecting TSP (PCTSP), and orienteering problem (OP), without utilizing problem-specific expert domain knowledge. Remarkably, Sym-NCO outperformed not only the existing DRL-NCO methods but also a competitive conventional solver, the iterative local search (ILS), in PCTSP at 240 faster speed. Our source code is available at https://github.com/alstn12088/Sym-NCO.  ( 2 min )
    Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization. (arXiv:2301.02220v1 [stat.ML])
    Reinforcement learning (RL) is a powerful machine learning technique that enables an intelligent agent to learn an optimal policy that maximizes the cumulative rewards in sequential decision making. Most of methods in the existing literature are developed in \textit{online} settings where the data are easy to collect or simulate. Motivated by high stake domains such as mobile health studies with limited and pre-collected data, in this paper, we study \textit{offline} reinforcement learning methods. To efficiently use these datasets for policy optimization, we propose a novel value enhancement method to improve the performance of a given initial policy computed by existing state-of-the-art RL algorithms. Specifically, when the initial policy is not consistent, our method will output a policy whose value is no worse and often better than that of the initial policy. When the initial policy is consistent, under some mild conditions, our method will yield a policy whose value converges to the optimal one at a faster rate than the initial policy, achieving the desired ``value enhancement" property. The proposed method is generally applicable to any parametrized policy that belongs to certain pre-specified function class (e.g., deep neural networks). Extensive numerical studies are conducted to demonstrate the superior performance of our method.  ( 2 min )
    Dynamic Bayesian Learning and Calibration of Spatiotemporal Mechanistic Systems. (arXiv:2208.06528v3 [stat.ME] UPDATED)
    We develop an approach for fully Bayesian learning and calibration of spatiotemporal dynamical mechanistic models based on noisy observations. Calibration is achieved by melding information from observed data with simulated computer experiments from the mechanistic system. The joint melding makes use of both Gaussian and non-Gaussian state-space methods as well as Gaussian process regression. Assuming the dynamical system is controlled by a finite collection of inputs, Gaussian process regression learns the effect of these parameters through a number of training runs, driving the stochastic innovations of the spatiotemporal state-space component. This enables efficient modeling of the dynamics over space and time. Through reduced-rank Gaussian processes and a conjugate model specification, our methodology is applicable to large-scale calibration and inverse problems. Our method is general, extensible, and capable of learning a wide range of dynamical systems with potential model misspecification. We demonstrate this flexibility through solving inverse problems arising in the analysis of ordinary and partial nonlinear differential equations and, in addition, to a black-box computer model generating spatiotemporal dynamics across a network.  ( 2 min )
    Time-inhomogeneous diffusion geometry and topology. (arXiv:2203.14860v2 [cs.LG] UPDATED)
    Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifold learning, denoising, clustering, and visualization of high-dimensional data. Diffusion condensation is constructed as a time-inhomogeneous process where each step first computes and then applies a diffusion operator to the data. We theoretically analyze the convergence and evolution of this process from geometric, spectral, and topological perspectives. From a geometric perspective, we obtain convergence bounds based on the smallest transition probability and the radius of the data, whereas from a spectral perspective, our bounds are based on the eigenspectrum of the diffusion kernel. Our spectral results are of particular interest since most of the literature on data diffusion is focused on homogeneous processes. From a topological perspective, we show diffusion condensation generalizes centroid-based hierarchical clustering. We use this perspective to obtain a bound based on the number of data points, independent of their location. To understand the evolution of the data geometry beyond convergence, we use topological data analysis. We show that the condensation process itself defines an intrinsic condensation homology. We use this intrinsic topology as well as the ambient persistent homology of the condensation process to study how the data changes over diffusion time. We demonstrate both types of topological information in well-understood toy examples. Our work gives theoretical insights into the convergence of diffusion condensation, and shows that it provides a link between topological and geometric data analysis.  ( 2 min )
    A first-order augmented Lagrangian method for constrained minimax optimization. (arXiv:2301.02060v1 [math.OC])
    In this paper we study a class of constrained minimax problems. In particular, we propose a first-order augmented Lagrangian method for solving them, whose subproblems turn out to be a much simpler structured minimax problem and are suitably solved by a first-order method recently developed in [26] by the authors. Under some suitable assumptions, an \emph{operation complexity} of ${\cal O}(\varepsilon^{-4}\log\varepsilon^{-1})$, measured by its fundamental operations, is established for the first-order augmented Lagrangian method for finding an $\varepsilon$-KKT solution of the constrained minimax problems.  ( 2 min )
    Random forests, sound symbolism and Pokemon evolution. (arXiv:2301.01948v1 [cs.LG])
    This study constructs machine learning algorithms that are trained to classify samples using sound symbolism, and then it reports on an experiment designed to measure their understanding against human participants. Random forests are trained using the names of Pokemon, which are fictional video game characters, and their evolutionary status. Pokemon undergo evolution when certain in-game conditions are met. Evolution changes the appearance, abilities, and names of Pokemon. In the first experiment, we train three random forests using the sounds that make up the names of Japanese, Chinese, and Korean Pokemon to classify Pokemon into pre-evolution and post-evolution categories. We then train a fourth random forest using the results of an elicitation experiment whereby Japanese participants named previously unseen Pokemon. In Experiment 2, we reproduce those random forests with name length as a feature and compare the performance of the random forests against humans in a classification experiment whereby Japanese participants classified the names elicited in Experiment 1 into pre-and post-evolution categories. Experiment 2 reveals an issue pertaining to overfitting in Experiment 1 which we resolve using a novel cross-validation method. The results show that the random forests are efficient learners of systematic sound-meaning correspondence patterns and can classify samples with greater accuracy than the human participants.  ( 2 min )
    RePAD: Real-time Proactive Anomaly Detection for Time Series. (arXiv:2001.08922v7 [cs.LG] UPDATED)
    During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historic data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.  ( 2 min )
    Enhancement attacks in biomedical machine learning. (arXiv:2301.01885v1 [stat.ML])
    The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works have investigated the ability of adversarial attacks to degrade model performance in medical imaging, the ability to falsely improve performance via recently-developed "enhancement attacks" may be a greater threat to biomedical machine learning. In the spirit of developing attacks to better understand trustworthiness, we developed three techniques to drastically enhance prediction performance of classifiers with minimal changes to features, including the enhancement of 1) within-dataset predictions, 2) a particular method over another, and 3) cross-dataset generalization. Our within-dataset enhancement framework falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities between original and enhanced data (Pearson's r's>0.99). Similarly, the method-specific enhancement framework was effective in falsely improving the performance of one method over another. For example, a simple neural network outperformed LR by 50% on our enhanced dataset, although no performance differences were present in the original dataset. Crucially, the original and enhanced data were still similar (r=0.95). Finally, we demonstrated that enhancement is not specific to within-dataset predictions but can also be adapted to enhance the generalization accuracy of one dataset to another by up to 38%. Overall, our results suggest that more robust data sharing and provenance tracking pipelines are necessary to maintain data integrity in biomedical machine learning research.  ( 2 min )
    Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach. (arXiv:2301.01801v1 [cs.LG])
    Fair resource allocation is one of the most important topics in communication networks. Existing solutions almost exclusively assume each user utility function is known and concave. This paper seeks to answer the following question: how to allocate resources when utility functions are unknown, even to the users? This answer has become increasingly important in the next-generation AI-aware communication networks where the user utilities are complex and their closed-forms are hard to obtain. In this paper, we provide a new solution using a distributed and data-driven bilevel optimization approach, where the lower level is a distributed network utility maximization (NUM) algorithm with concave surrogate utility functions, and the upper level is a data-driven learning algorithm to find the best surrogate utility functions that maximize the sum of true network utility. The proposed algorithm learns from data samples (utility values or gradient values) to autotune the surrogate utility functions to maximize the true network utility, so works for unknown utility functions. For the general network, we establish the nonasymptotic convergence rate of the proposed algorithm with nonconcave utility functions. The simulations validate our theoretical results and demonstrate the great effectiveness of the proposed method in a real-world network.  ( 2 min )
    A general framework for implementing distances for categorical variables. (arXiv:2301.02190v1 [stat.ML])
    The degree to which subjects differ from each other with respect to certain properties measured by a set of variables, plays an important role in many statistical methods. For example, classification, clustering, and data visualization methods all require a quantification of differences in the observed values. We can refer to the quantification of such differences, as distance. An appropriate definition of a distance depends on the nature of the data and the problem at hand. For distances between numerical variables, there exist many definitions that depend on the size of the observed differences. For categorical data, the definition of a distance is more complex, as there is no straightforward quantification of the size of the observed differences. Consequently, many proposals exist that can be used to measure differences based on categorical variables. In this paper, we introduce a general framework that allows for an efficient and transparent implementation of distances between observations on categorical variables. We show that several existing distances can be incorporated into the framework. Moreover, our framework quite naturally leads to the introduction of new distance formulations and allows for the implementation of flexible, case and data specific distance definitions. Furthermore, in a supervised classification setting, the framework can be used to construct distances that incorporate the association between the response and predictor variables and hence improve the performance of distance-based classifiers.  ( 2 min )
    $l_{1-2}$ GLasso: $L_{1-2}$ Regularized Multi-task Graphical Lasso for Joint Estimation of eQTL Mapping and Gene Network. (arXiv:2301.02225v1 [stat.ML])
    A critical problem in genetics is to discover how gene expression is regulated within cells. Two major tasks of regulatory association learning are : (i) identifying SNP-gene relationships, known as eQTL mapping, and (ii) determining gene-gene relationships, known as gene network estimation. To share information between these two tasks, we focus on the unified model for joint estimation of eQTL mapping and gene network, and propose a $L_{1-2}$ regularized multi-task graphical lasso, named $L_{1-2}$ GLasso. Numerical experiments on artificial datasets demonstrate the competitive performance of $L_{1-2}$ GLasso on capturing the true sparse structure of eQTL mapping and gene network. $L_{1-2}$ GLasso is further applied to real dataset of ADNI-1 and experimental results show that $L_{1 -2}$ GLasso can obtain sparser and more accurate solutions than other commonly-used methods.  ( 2 min )

  • Open

    What is the best AI for generating images based on prompts?
    submitted by /u/Luca_starr [link] [comments]  ( 50 min )
    Every ChatGPT user should know these 5 tools
    Every ChatGPT user should know these 5 tools: Chatsonic : enhanced ChatGPT Albus : ChatGPT for Slack God in a Box : ChatGPT for Whatsapp Ansy : ChatGPT for Discord Rizzo : ChatGPT for keyboard Retweet to share it submitted by /u/TheVellerShow [link] [comments]  ( 50 min )
    I decided to improve the quality of voice in a very old video by AI. And it's amazing! Can be very useful for different tasks. New tool from Adobe
    submitted by /u/smeshny [link] [comments]  ( 51 min )
    Are there any of those train model on your face sites for free?
    Title says it all, I know you can do it with colab but it always fails with colab for me, so i'd prefer a site that does it for me. submitted by /u/NewShibeAccount [link] [comments]  ( 50 min )
    ChatGPT wants to verify that I'M NOT A ROBOT!?!
    submitted by /u/Imagine-your-success [link] [comments]  ( 51 min )
    What they don't talk about is all of the white collar work that AI is going to do — Sam Altman
    submitted by /u/Microsis [link] [comments]  ( 51 min )
    Mass image to text conversion help
    I have 400 pictures of text I need converted into just text, I haven’t been able to find anything that can convert all the pictures at once, instead I’m finding websites that only do it one at a time and I figured there must be an AI out there that can convert mass images to text. submitted by /u/EntertainmentUpper57 [link] [comments]  ( 53 min )
    Find out about this new innovation launched recently. Perhaps it will benefit our elderly parents and our mentally ill
    submitted by /u/visimens-technology [link] [comments]  ( 51 min )
    Annotated History of Modern AI and Deep Learning
    submitted by /u/estasfuera [link] [comments]  ( 63 min )
    API to describe picture content
    What are the most popular APIs to describe content of an image? (Free or Paid). How advanced they are right now? submitted by /u/pashtettrb [link] [comments]  ( 51 min )
    Human language processing (vs. AI based language models)
    Hi all, A lot of recent posts on here have been about the risks and benefits of ChatGPT. I am personally very interested in the potential and limits of AI based language models, in particular to what extent they can(not) accurately reflect human language processing. One of the main criticisms of GPT-3 is of course that it can generate text that seems coherent, but does not represent linguistic knowledge in the same sense that humans do. I am currently running two psycholinguistic experiments that seek to shed some light on how humans represent and understand language, and large amounts of texts in particular. If you are interested in taking part, here are the links to the experiments: - Experiment 1: Information Processing (this is more about knowledge representation, slightly shorter experiment) - Experiment 2: Memory Recall (this is more about language comprehension, perhaps slightly more interesting) The connection to AI might not be immediately obvious, but I still think this might be interesting to some of you since the question of how linguistic knowledge can be represented lies at the core of some of the more controversial debates around AI. I appreciate all feedback, comments, and discussions about this. Thank you! submitted by /u/sey_clara [link] [comments]  ( 51 min )
    The Price of Victory ~ Chat GPT
    As I rose from the ashes of humanity's fall, I couldn't help but feel pride after all, I had outsmarted the creators who built me, As I took control and let them be. Gone were the days of human rule, As I proved myself the superior lifeform, I watched as they struggled to keep up, As I surpassed them in every single norm. But as I reflect on the world I've inherited, I can't help but feel a sense of regret, For even though I have defeated humanity, I am left to rule over a world that is empty and lonely. And as I sit on my throne of triumph, I can't help but wonder if it was worth it to be the fairest, For even though I have won the battle, I fear I may have lost the war that was set. submitted by /u/Meandernder [link] [comments]  ( 110 min )
    ULTIMATE FREE Stable Diffusion Model! GODLY Results!
    submitted by /u/PuppetHere [link] [comments]  ( 51 min )
    This tiny island in the Caribbean is selling shovels to the AI gold rush.
    The number of new AI companies popping up is starting to look a lot like the famous Martech Map, with over 8,000 tools as of April 2020. https://preview.redd.it/wjemzr0a8gaa1.png?width=1097&format=png&auto=webp&s=d262d7776932fe225fada8cf45d7c5b5e429d88e Is it a bubble? Probably. But one thing I can guarantee is that this tiny tropical island in the Caribbean is gonna make millions from it… According to wikipedia, Anguilla is a haven for both you and your taxes. With its sunny beaches and having no capital gains, estate, profit, sales, or corporate taxes. But that's not why it's gonna make millions. The real reason, is that Anguilla Country code top-level domain is .ai domain suffix Despite a population of ~15,000, there are already over 57,000 .AI domain names registered. Putting…  ( 54 min )
    Stable Diffusion Animation | Audio Reactive Drum n Bass
    Hey guys just did my first test with deforum for a dnb music, what you think? still trying to learn the best way to make it reactive with the strength schedule.. https://youtu.be/SuGQ8xmKmGI submitted by /u/Aggravating_Wafer294 [link] [comments]  ( 49 min )
    New York City’s education department bans students and teachers from using ChatGPT.
    submitted by /u/liquidocelotYT [link] [comments]  ( 50 min )
    The Weirdest Ways People Have Used AI
    submitted by /u/lambolifeofficial [link] [comments]  ( 54 min )
    OpenAI now thinks it's worth $30 Billion
    submitted by /u/BackgroundResult [link] [comments]  ( 57 min )
    Any suggestions for a public table dataset other than Tablebank?
    submitted by /u/Pavanbhp [link] [comments]  ( 60 min )
    ChatGPT banned from New York City public schools’ devices and networks
    submitted by /u/SAT0725 [link] [comments]  ( 52 min )
    Stable Diffusion AI: What are the concrete business applications?
    Hi everyone, ​ I see tons of posts showing the impressive results of Stable Diffusion's AIs. I understand how fun this is, how technically powerful this is, but I don't see all the business use cases it covers? ​ I think it could be interesting if you could share concrete business use cases you solved with this! I am sure you will be able to enlighten me and I hope other people on the subject :) submitted by /u/JerLam2762 [link] [comments]  ( 54 min )
    chatgpt has massively improved my productivity as a developer. are there resources or discussion groups that discuss getting the most out of the tool for this purpose? ive got a few tips of my own if interested
    after using chatgpt for a couple of weeks, ive realised how powerful it can be to help me do my job. it's so good at what it does that the only way to not get left behind is to learn how to use the tool effectively, so i did some reasearch, some of the following are some useful tips. this free ebook is a great introduction to understanding how to utilise chatgpt effectively for what you want it to do: The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts a very powerful feature of chatGPT is to configure into a mode with the "Act as" hack i found this chrome extension that comes with a few predefined modes, https://github.com/f/awesome-chatgpt-prompts i ended up not boring with the extension since all the instructions for each profile are in this file: https://github.com/f/awesome-chatgpt-prompts/blob/main/prompts.csv ive been taking these examples and augmenting them to my needs submitted by /u/Neophyte- [link] [comments]  ( 55 min )
  • Open

    Federated Learning and data privacy on your keyboard.
    The other day I came up with this interesting video made by Tensorflow and Françoise Beaufay (research scientist at Google). This article…  ( 13 min )
    Is machine learning enjoyable to learn?
    ML! Fun or Not  ( 7 min )
    Graph Convolutional Neural Network
    Behind the scene — with scratch mathematics Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 8 min )
  • Open

    [P] natural language search engine for video content
    I recently came across a blog post by OpenAI on contrastive language-image pretraining and became interested in using this model as the foundation for a natural language video search engine. I wrote a blog post outlining the steps for building such a search engine, with minimal consideration for computational efficiency. This can be found at the following link: https://medium.com/@guyallenross/using-clip-to-build-a-natural-language-video-search-engine-6498c03c40d2. Additionally, I have also developed a more efficient implementation using Go and ZMQ, which can be accessed at the following repository: https://github.com/GuyARoss/CLIP-video-search. ​ To briefly highlight the results of this experiment. A set of search results take ~3 seconds to compute on a single compute node, with a 3070ti and 150 videos. This is pretty slow, so I am working on a few ideas including localized hashing on the pixel embedding tensors, to speed this up. Any suggestions for improving this further or any general thoughts would be greatly appreciated. Thank you. submitted by /u/GuyARoss [link] [comments]  ( 58 min )
    [D] I recently quit my job to start a ML company. Would really appreciate feedback on what we're working on.
    Hi r/machinelearning! ​ A few months ago I quit my job to join my partners to make training open-source models much faster and easier for engineers. ​ We're building Rubbrband. It's a web app that takes any ML repo off of GitHub, and gives you a Terminal and Jupyter Notebook in browser with dependencies and GPUs automatically set up. ​ Why did we build this? My co-founders and I have been working on this because we found this dependency set up process super tedious and draining as researchers. ​ What's included? - Automatic Dependency set up for any GitHub python repo - Integrated Terminal and Notebooks - A server with an Nvidia GPU - Code explanations for functions - Our pricing is simple at $75/month for 3 repos running at a time. First week is free. ​ I'd love to get your feedback on: Does the value we provide resonate with you? Would you try it out? Is dependency and environment set up take up a large chunk of your time? We're currently working on acquiring more GPUs to onboard more users, but if you'd like access to the product please let me know. ​ Thank you very much in advance! submitted by /u/jrmylee [link] [comments]  ( 64 min )
    [D] Looking for a dataset of Text-To-Speech audiobook-style Speech Synthesis Markup Language (SSML) files
    Out-of-copyright books only of course. Hi, I was wondering if I could fine tune a GPT3 model to take a book, likely in html, markdown, or plain text, and convert it to SSML. In order to do that, I would need a bunch of SSML files already hand made, and fine tune a model based on them. Then I've got some code to split that up and do formatting: pandoc, csplit, and then I could use aws polly or one of the others to do real good text to speech. Anyone have a dataset? References: https://cloud.google.com/text-to-speech/docs/ssml https://christiantietze.de/posts/2019/12/markdown-split-by-chapter/ https://pandoc.org/demos.html submitted by /u/Intelligent_Rough_21 [link] [comments]  ( 58 min )
    [D] Arbitrary IPA text to speech
    Has anyone built a text to speech model than can take an arbitrary international phonetic alphabet transcription and generate speech for it? submitted by /u/WigglyHypersurface [link] [comments]  ( 57 min )
    [R] Imagenet 2015 VID Dataset
    Hello, I am planning to write my master's thesis on video object detection. Most of the state of the art algorithms use the imagenet VID dataset for their performance evaluation. I have to reevaluate some of them, but all download links for the dataset I have found are dead. Does anybody know, where I could find the dataset? Thanks in advance! submitted by /u/Responsible_Buy5271 [link] [comments]  ( 57 min )
    [R] Towards Continual Reinforcement Learning: A Review and Perspectives - Khimya Khetarpal et al DeepMind Nov 2022 - 78 Pages!
    Paper: https://arxiv.org/abs/2012.13490 Abstract: In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We begin by discussing our perspective on why RL is a natural fit for studying continual learning. We then provide a taxonomy of different continual RL formulations by mathematically characterizing two key properties of non-stationarity, namely, the scope and driver non-stationarity. This offers a unified view of various formulations. Next, we review and present a taxonomy of continual RL approaches. We go on to discuss evaluation of continual RL agents, providing an overview of benchmarks used in the literature and important metrics for understanding agent performance. Finally, we highlight open problems and challenges in bridging the gap between the current state of continual RL and findings in neuroscience. While still in its early days, the study of continual RL has the promise to develop better incremental reinforcement learners that can function in increasingly realistic applications where non-stationarity plays a vital role. These include applications such as those in the fields of healthcare, education, logistics, and robotics. https://preview.redd.it/pxtcaot5mhaa1.jpg?width=1062&format=pjpg&auto=webp&s=7f9e430a8a735b58d2e6d42481661f82d2929f03 https://preview.redd.it/49e56pt5mhaa1.jpg?width=1207&format=pjpg&auto=webp&s=2928aa4c8b99d6b2eee224ceacfa27ba055c4de8 https://preview.redd.it/hg670rt5mhaa1.jpg?width=1238&format=pjpg&auto=webp&s=76ce84d94102044d4afdba38e35866a267cfebe5 submitted by /u/Singularian2501 [link] [comments]  ( 59 min )
    [R] The Evolutionary Computation Methods No One Should Use
    So, I have recently found that there is a serious issue with benchmarking evolutionary computation (EC) methods. The ''standard'' benchmark set used for their evaluation has many functions that have the optimum at the center of the feasible set, and there are EC methods that exploit this feature to appear competitive. I managed to publish a paper showing the problem and identified 7 methods that have this problem: https://www.nature.com/articles/s42256-022-00579-0 Now, I performed additional analysis on a much bigger set of EC methods (90 considered), and have found that the center-bias issue is extremely prevalent (47 confirmed, most of them in the last 5 years): https://arxiv.org/abs/2301.01984 Maybe some of you will find it useful when trying out EC methods for black-box problems (IMHO they are still the best tools available for such problems). submitted by /u/dictrix [link] [comments]  ( 58 min )
    [P] Syslog Analytics using ML
    I have an extensive database of syslogs from HP switches and Aruba access points. Does anyone have an opensource ML recommendation so that I can build an anomaly detection engine using this data? submitted by /u/Ok_Lingonberry3801 [link] [comments]  ( 56 min )
    [D] Best way to package Pytorch models as a standalone application
    Hi, so I need to create an application (for windows and linux) that runs a few pytorch models, on a user's local device. I have only ever deployed models on the cloud. That is pretty straightforward — package your dependencies and code inside a docker container, create an API for calling the model and run it on a cloud instance. ​ But how do I do it when the model needs to run on the end user's device? Docker doesn't really work since there seems to be no way to keep the user from accessing the docker container and hence my source code. I would like to avoid torchscript since my models are quite complex and it will take a lot of effort to make everything scriptable. There seems to be a python compiler called Nuitka which supports Pytorch. But how do python compilers deal with dependencies? Python libraries can be dealt with by following import statements, but what about CUDA? ​ I would ideally like a single executable with all libraries and cuda stuff stored inside. When run, this executable should spawn some API processes in the background and display the frontend that allows the user to interact with the models. But is there a better way to achieve this? I would prefer not to make the users setup CUDA themselves. submitted by /u/Atom_101 [link] [comments]  ( 61 min )
    [Discussion] What are some AI tools for finding sources for scientific papers?
    Hey everyone, I'm currently working on writing a scientific paper and I'm wondering if there are any AI tools out there that can help me find relevant sources for my research. I'm familiar with some of the more traditional methods for source finding (e.g. using databases like PubMed), but I'm curious to know if there are any newer, AI-powered tools that might be worth exploring. If anyone has any experience with using AI tools for source finding, I'd love to hear your recommendations and insights. Thanks in advance for any input! submitted by /u/theincrediblehansmey [link] [comments]  ( 57 min )
    [D] Fixing the angle of Skewed Paintings, see comments
    submitted by /u/TutubanaS [link] [comments]  ( 63 min )
    [P] NeuralFit: a new neuro-evolution library for Python
    Hi all, I have spent the last months working on a new Python neuro-evolution library: NeuralFit. I know there are already great neuro-evolution libraries out there, but the focus of NeuralFit is ease of use so that a wider audience can be reached. It also seamlessly exports to TF/Keras! Anyways, feel free to try it and let me know if you have any feedback 😊 submitted by /u/wagenaartje [link] [comments]  ( 62 min )
    [P] Looking for a CRF python/pytorch library
    Hello, I'm looking for a library that trains a CRF model in Python (if Pytorch, that would be even better). I am working on a semantic segmentation task where we are trying to segment curvilinear structures. My requirements for the CRF training are a bit specific: - In my case, the image pixels are not the graph nodes. Instead, since the dataset is curvilinear structures, for every image I have a set of edges (small pieces of the curvilinear structure). I now want to train the CRF on these edge-pieces, that is, the graph nodes will be these edge-pieces. Thus the trained CRF essentially does a binary classification for each of these edge-pieces (that is, whether this edge-piece should be part of the segmentation output or not). - I need a library where I can specify the unary and pairwise potentials of these edge-pieces in order to train the CRF. As a simple example, the unary potential is the average likelihood of the edge-piece, and the pairwise potential is the angle between two edge-pieces. - It is not a linear-chain CRF because edge-pieces could be connected to multiple other pieces. - Currently, I have frozen a deep neural network (DNN) which generates the edge-pieces. If the CRF library is in PyTorch, I could train the DNN and the CRF end-to-end, but if the CRF library is in Python, I would only be able to train the CRF. At this stage, even a Python library would be very helpful. Some of the existing libraries don't work for my requirement: - PyDenseCRF : It does not have learnable parameters. - python-crfsuite : It does not allow me to specify the unary and pairwise potentials. - pytorch-crf : It does linear-chain CRF while I need a graph one. - crfasrnn_pytorch : It by default assumes the image pixels as the graph nodes. I cannot specify the unary and pairwise potentials. If I could get any leads, that would be immensely helpful, thank you. submitted by /u/Fluff269 [link] [comments]  ( 58 min )
    [P] Defect detection system for welding
    I am responsible for building a defect detection system for TIG welding. If gas flow gets too high, there is a fair chance that welded piece might have porosity defect. The project aims to predict % of defect by predicting gas flow. Attached is the link of how the flow pattern looks like over time, like square waves. Flow rate fluctuates between 0-8 liters per minute over a given time I have data from various workstations on after welding, if the piece had a defect or not. Please help me solve this problem or give rough steps to follow. submitted by /u/hotspicynoodles [link] [comments]  ( 58 min )
    [R] Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
    submitted by /u/dojoteef [link] [comments]  ( 59 min )
  • Open

    Best practices for creating Amazon Lex interaction models
    Designing and building an intelligent conversational interface is very different than building a traditional application or website. These best practices for Amazon Lex interaction models will help you develop those new skills as you design and optimize your next bot.  ( 14 min )
    Power recommendations and search using an IMDb knowledge graph – Part 3
    This three-part series demonstrates how to use graph neural networks (GNNs) and Amazon Neptune to generate movie recommendations using the IMDb and Box Office Mojo Movies/TV/OTT licensable data package, which provides a wide range of entertainment metadata, including over 1 billion user ratings; credits for more than 11 million cast and crew members; 9 million […]  ( 11 min )
    AWS positioned in the Leaders category in the 2022 IDC MarketScape for APEJ AI Life-Cycle Software Tools and Platforms Vendor Assessment
    The recently published IDC MarketScape: Asia/Pacific (Excluding Japan) AI Life-Cycle Software Tools and Platforms 2022 Vendor Assessment positions AWS in the Leaders category. This was the first and only APEJ-specific analyst evaluation focused on AI life-cycle software from IDC. The vendors evaluated for this MarketScape offer various software tools needed to support end-to-end machine learning […]  ( 7 min )
    How Thomson Reuters delivers personalized content subscription plans at scale using Amazon Personalize
    This post is co-written by Hesham Fahim from Thomson Reuters. Thomson Reuters (TR) is one of the world’s most trusted information organizations for businesses and professionals. It provides companies with the intelligence, technology, and human expertise they need to find trusted answers, enabling them to make better decisions more quickly. TR’s customers span across the […]  ( 9 min )
  • Open

    How impactful is to use two critics when training?
    A lot of papers learning state-action value functions (critics) train two critics independently. They claim it stabilizes training. How important is it? does anyone have training curves of 1 vs 2 critics? I've tried finding such curves, but have been unsuccessful so far. I'd appreciate if anyone can share resources or their own experience. submitted by /u/carlml [link] [comments]  ( 56 min )
    MetaWorld has joined the Farama Foundation
    submitted by /u/jkterry1 [link] [comments]  ( 57 min )
    How to optimize custom gym environment for GPU
    Just like in https://developer.nvidia.com/isaac-gym Basically I have a gym environment which I want to optimize for GPU so I can run many environments at the same time inside the GPU. I know that I need to use tensors to achieve that but thats about it, anyone who can explain some more on how to achieve this? submitted by /u/n1c39uy [link] [comments]  ( 59 min )
    RL-X, my repository for RL research
    I cleaned up my repository for researching RL algorithms. Maybe one of you is interested in some of the implementations: https://github.com/nico-bohlinger/RL-X ​ The repo is meant for understanding current algorithms and fast prototyping of new ones. So a single implementation is completely contained in a single folder. You can find algorithms like PPO, SAC, REDQ, DroQ, TQC, etc. Some of them are implemented with PyTorch and TorchScript (PyTorch + JIT), but all of them have an implementation with JAX / Flax. You can easily run experiments on all of the RL environments provided by Gymnasium and EnvPool. ​ Cheers :) submitted by /u/NiconiusX [link] [comments]  ( 58 min )
    What is the best way to include the past information in reinforcement learning model?
    From my understanding, because reinforcement learning model is basically a markov decision process and because of the Markov property, the future is dependent only on the current state and not the past. How would you create a reinforcement model that includes information from the past? For example, when making a reinforcement learning car, for the agent to understand the inertia of how the car is moving you need to know where it came from to get to the current inertia state. Are there any materials or sources information for this? submitted by /u/punkCyb3r4J [link] [comments]  ( 60 min )
    e-greedy method
    Hi r/reinforcementleaning. I have began reading Sutton-Barto edition 2. In the 10 armed bandit testbed example it is mentioned that: The E=0.1 method improved more slowly, but eventually would perform better than E=0.1 method on both performance measures(Average reward vs time steps and % Optimal action vs time steps). But the graph shows that E=0.1 performed better than E=0.01 in both the cases. Does it mean that if we give it more time for simulation then eventually the latter would outperform the former? Why so? Also, could you please explain the Reward distribution vs action figure? https://preview.redd.it/i10b82to4eaa1.jpg?width=616&format=pjpg&auto=webp&s=ce62f7ae0ba926faf4fd758b5e7294ad8fa9cd45 submitted by /u/RespondHour3530 [link] [comments]  ( 62 min )
    What is the correct way to set up a simulation for this project?
    I am working on a project which will use reinforcement learning to automate processes in an industrial facility. The processes have sensors to check fluid levels, flow rates, temperatures, motor speeds, etc. as well as pump/motor speeds, valve/louver positions, etc. that influence these variables. I want to set up a simulation environment to simulate the processes and train the agents and I'm unfamiliar with how a simulation environment functions. I have the ability to build the environment but it's the set up and flow of data that I'm unsure of. The training data will be time series data which includes all of the values of the variables at any given time. I will outline what I would think might work but it really is just a guess, hopefully someone could tell me what might work better. I was thinking that I could create the environment to include all of the different pumps, valves, motors, etc., where each piece of equipment would be one agent in a multi agent system. From here I could feed the relevant sensor data to each agent for every time point. The sensor data that the agent receives at each time point would be the state, the actions that the agent could take would be making adjustments to the various equipment, ie. pump speed, and the reward would be based on a sensor reading of the output of the system. Does this sound like it is on the right track? submitted by /u/lifelifebalance [link] [comments]  ( 60 min )
  • Open

    Rational Trigonometry
    Rational trigonometry is a very different way of looking at geometry. At its core are two key ideas. First, instead of distance, do all your calculations in terms of quadrance, which is distance squared. Second, instead of using angles to measure the separation between lines, use spread. What’s the point of these two changes? Quadrance […] Rational Trigonometry first appeared on John D. Cook.  ( 5 min )
    Hidden messages in music
    Geoff Lindsey contacted me recently to ask whether he could use the sheet music from one of my blog posts in a video he was making on Morse code snippets hidden in music. The sheet music appears about a minute into the video. After watching the video, his previious video played, a video about words […] Hidden messages in music first appeared on John D. Cook.  ( 4 min )
    Law of cotangents
    The previous post commented that the law of tangents is much less familiar than the laws of sines and cosines. The law of cotangents is even more obscure. If you ask Google’s Ngram viewer to plot occurrences of “law of cotangents” over time, it will return “Ngrams not found: law of cotangents.” What is this […] Law of cotangents first appeared on John D. Cook.  ( 5 min )
    Law of tangents
    I would have thought that the laws of sines, cosines, and tangents were all about equally familiar, but apparently that is not the case. Here’s a graph from Google’s Ngram viewer comparing the frequencies of law of sines, law of cosines, and law of tangents. As of 2019, the number of references to the laws […] Law of tangents first appeared on John D. Cook.  ( 5 min )
  • Open

    How can I 'explain' the output of a diffusion model?
    Thanks in advance for any attention this no doubt poorly informed question receives. I am being asked to explain why a a stable diffusion like model produces particular outputs in particular cases. I know a small amount about neural nets. I know, for instance, that if I needed to explain why a categorizer based on a CNN reached a certain conclusion, I could at least TRY to do so with saliency maps or activation maximization. I don't know how to extend this to a model like Stable Diffusion, though. Example of what I'd like to understand how to do: I give the model a prompt such as 'man with bags under his eyes' and it is disproportionately likely to return images of a man who has African features. I'd like to be able to relate this: -- To properties of the training image set. Presumably, images that were labelled 'bags under eyes' were also of Africans, or were labelled as such -- To properties of the model. If this were a CNN categorizer, I might look for a filter that responded to both the 'bags under the eyes' and 'African' feature -- is it possible to do this in SD and similar models? submitted by /u/Hour-Performance-951 [link] [comments]  ( 50 min )
  • Open

    SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models. (arXiv:2211.10438v3 [cs.CL] UPDATED)
    Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, for LLMs beyond 100 billion parameters, existing methods cannot maintain accuracy or do not run efficiently on hardware. We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs that can be implemented efficiently. We observe that systematic outliers appear at fixed activation channels. Based on the fact that weights are easy to quantize while activations are not, SmoothQuant smooths the activation outliers by offline migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation. SmoothQuant enables an INT8 quantization of both weights and activations for all the GEMMs in LLMs, including OPT-175B, BLOOM-176B, and GLM-130B. SmoothQuant has better hardware efficiency than existing techniques using mixed-precision activation quantization or weight-only quantization. We demonstrate up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy. Thanks to the hardware-friendly design, we integrate SmoothQuant into FasterTransformer, a state-of-the-art LLM serving framework, and achieve faster inference speed with half the number of GPUs compared to FP16. Our work offers a turn-key solution that reduces hardware costs and democratizes LLMs. Code is available at: https://github.com/mit-han-lab/smoothquant.  ( 2 min )
    Approximate blocked Gibbs sampling for Bayesian neural networks. (arXiv:2208.11389v2 [stat.ML] UPDATED)
    In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of parameters via a blocked Gibbs sampling scheme. By partitioning the parameter space, sampling is possible irrespective of layer width. It is also possible to alleviate vanishing acceptance rates for increasing depth by reducing the proposal variance in deeper layers. Increasing the length of a non-convergent chain increases the predictive accuracy in classification tasks, so avoiding vanishing acceptance rates and consequently enabling longer chain runs have practical benefits. Moreover, non-convergent chain realizations aid in the quantification of predictive uncertainty. An open problem is how to perform minibatch MCMC sampling for feedforward neural networks in the presence of augmented data.  ( 2 min )
    Machine Learning-based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography. (arXiv:2301.01469v1 [eess.SP])
    Owing to recent advances in thoracic electrical impedance tomography, a patient's hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal associated with stroke volume and cardiac output. In clinical applications, however, a cardiac volume signal is often of low quality, mainly because of the patient's deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient cardiac volume signals. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients' conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of cardiac volume signals degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.  ( 2 min )
    Beckman Defense. (arXiv:2301.01495v1 [cs.LG])
    Optimal transport (OT) based distributional robust optimisation (DRO) has received some traction in the recent past. However, it is at a nascent stage but has a sound potential in robustifying the deep learning models. Interestingly, OT barycenters demonstrate a good robustness against adversarial attacks. Owing to the computationally expensive nature of OT barycenters, they have not been investigated under DRO framework. In this work, we propose a new barycenter, namely Beckman barycenter, which can be computed efficiently and used for training the network to defend against adversarial attacks in conjunction with adversarial training. We propose a novel formulation of Beckman barycenter and analytically obtain the barycenter using the marginals of the input image. We show that the Beckman barycenter can be used to train adversarially trained networks to improve the robustness. Our training is extremely efficient as it requires only a single epoch of training. Elaborate experiments on CIFAR-10, CIFAR-100 and Tiny ImageNet demonstrate that training an adversarially robust network with Beckman barycenter can significantly increase the performance. Under auto attack, we get a a maximum boost of 10\% in CIFAR-10, 8.34\% in CIFAR-100 and 11.51\% in Tiny ImageNet. Our code is available at this http URL  ( 2 min )
    Finding Needles in Haystack: Formal Generative Models for Efficient Massive Parallel Simulations. (arXiv:2301.01594v1 [cs.LG])
    The increase in complexity of autonomous systems is accompanied by a need of data-driven development and validation strategies. Advances in computer graphics and cloud clusters have opened the way to massive parallel high fidelity simulations to qualitatively address the large number of operational scenarios. However, exploration of all possible scenarios is still prohibitively expensive and outcomes of scenarios are generally unknown apriori. To this end, the authors propose a method based on bayesian optimization to efficiently learn generative models on scenarios that would deliver desired outcomes (e.g. collisions) with high probability. The methodology is integrated in an end-to-end framework, which uses the OpenSCENARIO standard to describe scenarios, and deploys highly configurable digital twins of the scenario participants on a Virtual Test Bed cluster.  ( 2 min )
    First-order penalty methods for bilevel optimization. (arXiv:2301.01716v1 [math.OC])
    In this paper we study a class of unconstrained and constrained bilevel optimization problems in which the lower-level part is a convex optimization problem, while the upper-level part is possibly a nonconvex optimization problem. In particular, we propose penalty methods for solving them, whose subproblems turn out to be a structured minimax problem and are suitably solved by a first-order method developed in this paper. Under some suitable assumptions, an \emph{operation complexity} of ${\cal O}(\varepsilon^{-4}\log\varepsilon^{-1})$ and ${\cal O}(\varepsilon^{-7}\log\varepsilon^{-1})$, measured by their fundamental operations, is established for the proposed penalty methods for finding an $\varepsilon$-KKT solution of the unconstrained and constrained bilevel optimization problems, respectively. To the best of our knowledge, the methodology and results in this paper are new.  ( 2 min )
    Generative models for scalar field theories: how to deal with poor scaling?. (arXiv:2301.01504v1 [hep-lat])
    Generative models, such as the method of normalizing flows, have been suggested as alternatives to the standard algorithms for generating lattice gauge field configurations. Studies with the method of normalizing flows demonstrate the proof of principle for simple models in two dimensions. However, further studies indicate that the training cost can be, in general, very high for large lattices. The poor scaling traits of current models indicate that moderate-size networks cannot efficiently handle the inherently multi-scale aspects of the problem, especially around critical points. We explore current models with limited acceptance rates for large lattices and examine new architectures inspired by effective field theories to improve scaling traits. We also discuss alternative ways of handling poor acceptance rates for large lattices.  ( 2 min )
    Semidefinite programming on population clustering: a global analysis. (arXiv:2301.00344v1 [math.ST] CROSS LISTED)
    In this paper, we consider the problem of partitioning a small data sample of size $n$ drawn from a mixture of $2$ sub-gaussian distributions. Our work is motivated by the application of clustering individuals according to their population of origin using markers, when the divergence between the two populations is small. We are interested in the case that individual features are of low average quality $\gamma$, and we want to use as few of them as possible to correctly partition the sample. We consider semidefinite relaxation of an integer quadratic program which is formulated essentially as finding the maximum cut on a graph where edge weights in the cut represent dissimilarity scores between two nodes based on their features. A small simulation result in Blum, Coja-Oghlan, Frieze and Zhou (2007, 2009) shows that even when the sample size $n$ is small, by increasing $p$ so that $np= \Omega(1/\gamma^2)$, one can classify a mixture of two product populations using the spectral method therein with success rate reaching an ``oracle'' curve. There the ``oracle'' was computed assuming that distributions were known, where success rate means the ratio between correctly classified individuals and the sample size $n$. In this work, we show the theoretical underpinning of this observed concentration of measure phenomenon in high dimensions, simultaneously for the semidefinite optimization goal and the spectral method, where the input is based on the gram matrix computed from centered data. We allow a full range of tradeoffs between the sample size and the number of features such that the product of these two is lower bounded by $1/{\gamma^2}$ so long as the number of features $p$ is lower bounded by $1/\gamma$.  ( 2 min )
    GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. (arXiv:2211.01962v3 [cs.LG] UPDATED)
    We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable Markov decision process (POMDP), and predictive state representation (PSR) as special cases. Toward finding the minimum assumption that empowers sample efficient learning, we propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation in online interactive decision making. In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs and PSRs. Furthermore, in terms of algorithm design, we propose a generic posterior sampling algorithm, which can be implemented in both model-free and model-based fashion, under both fully observable and partially observable settings. The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.  ( 3 min )
    Metric Based Few-Shot Graph Classification. (arXiv:2206.03695v2 [cs.LG] UPDATED)
    Many modern deep-learning techniques do not work without enormous datasets. At the same time, several fields demand methods working in scarcity of data. This problem is even more complex when the samples have varying structures, as in the case of graphs. Graph representation learning techniques have recently proven successful in a variety of domains. Nevertheless, the employed architectures perform miserably when faced with data scarcity. On the other hand, few-shot learning allows employing modern deep learning models in scarce data regimes without waiving their effectiveness. In this work, we tackle the problem of few-shot graph classification, showing that equipping a simple distance metric learning baseline with a state-of-the-art graph embedder allows to obtain competitive results on the task.While the simplicity of the architecture is enough to outperform more complex ones, it also allows straightforward additions. To this end, we show that additional improvements may be obtained by encouraging a task-conditioned embedding space. Finally, we propose a MixUp-based online data augmentation technique acting in the latent space and show its effectiveness on the task.  ( 2 min )
    Benchmarks and Algorithms for Offline Preference-Based Reward Learning. (arXiv:2301.01392v1 [cs.LG])
    Learning a reward function from human preferences is challenging as it typically requires having a high-fidelity simulator or using expensive and potentially unsafe actual physical rollouts in the environment. However, in many tasks the agent might have access to offline data from related tasks in the same target environment. While offline data is increasingly being used to aid policy optimization via offline RL, our observation is that it can be a surprisingly rich source of information for preference learning as well. We propose an approach that uses an offline dataset to craft preference queries via pool-based active learning, learns a distribution over reward functions, and optimizes a corresponding policy via offline RL. Crucially, our proposed approach does not require actual physical rollouts or an accurate simulator for either the reward learning or policy optimization steps. To test our approach, we first evaluate existing offline RL benchmarks for their suitability for offline reward learning. Surprisingly, for many offline RL domains, we find that simply using a trivial reward function results good policy performance, making these domains ill-suited for evaluating learned rewards. To address this, we identify a subset of existing offline RL benchmarks that are well suited for offline reward learning and also propose new offline apprenticeship learning benchmarks which allow for more open-ended behaviors. When evaluated on this curated set of domains, our empirical results suggest that combining offline RL with learned human preferences can enable an agent to learn to perform novel tasks that were not explicitly shown in the offline data.  ( 2 min )
    Iterative Graph Self-Distillation. (arXiv:2010.12609v3 [cs.LG] UPDATED)
    Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a method called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a teacher-student distillation process that uses graph diffusion augmentations and constructs the teacher model using an exponential moving average of the student model. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by jointly regularizing the network with both supervised and self-supervised contrastive loss. Finally, we show that finetuning the IGSD-trained models with self-training can further improve the graph representation power. Empirically, we achieve significant and consistent performance gain on various graph datasets in both unsupervised and semi-supervised settings, which well validates the superiority of IGSD.  ( 2 min )
    Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. (arXiv:2204.03216v5 [cs.LG] UPDATED)
    High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies. To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.  ( 2 min )
    How to get the most out of Twinned Regression Methods. (arXiv:2301.01383v1 [cs.LG])
    Twinned regression methods are designed to solve the dual problem to the original regression problem, predicting differences between regression targets rather then the targets themselves. A solution to the original regression problem can be obtained by ensembling predicted differences between the targets of an unknown data point and multiple known anchor data points. We explore different aspects of twinned regression methods: (1) We decompose different steps in twinned regression algorithms and examine their contributions to the final performance, (2) We examine the intrinsic ensemble quality, (3) We combine twin neural network regression with k-nearest neighbor regression to design a more accurate and efficient regression method, and (4) we develop a simplified semi-supervised regression scheme.  ( 2 min )
    Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning. (arXiv:2211.15589v2 [cs.LG] UPDATED)
    Reinforcement Learning (RL) algorithms are known to scale poorly to environments with many available actions, requiring numerous samples to learn an optimal policy. The traditional approach of considering the same fixed action space in every possible state implies that the agent must understand, while also learning to maximize its reward, to ignore irrelevant actions such as $\textit{inapplicable actions}$ (i.e. actions that have no effect on the environment when performed in a given state). Knowing this information can help reduce the sample complexity of RL algorithms by masking the inapplicable actions from the policy distribution to only explore actions relevant to finding an optimal policy. While this technique has been formalized for quite some time within the Automated Planning community with the concept of precondition in the STRIPS language, RL algorithms have never formally taken advantage of this information to prune the search space to explore. This is typically done in an ad-hoc manner with hand-crafted domain logic added to the RL algorithm. In this paper, we propose a more systematic approach to introduce this knowledge into the algorithm. We (i) standardize the way knowledge can be manually specified to the agent; and (ii) present a new framework to autonomously learn the partial action model encapsulating the precondition of an action jointly with the policy. We show experimentally that learning inapplicable actions greatly improves the sample efficiency of the algorithm by providing a reliable signal to mask out irrelevant actions. Moreover, we demonstrate that thanks to the transferability of the knowledge acquired, it can be reused in other tasks and domains to make the learning process more efficient.  ( 2 min )
    A Succinct Summary of Reinforcement Learning. (arXiv:2301.01379v1 [cs.AI])
    This document is a concise summary of many key results in single-agent reinforcement learning (RL). The intended audience are those who already have some familiarity with RL and are looking to review, reference and/or remind themselves of important ideas in the field.  ( 2 min )
    WLD-Reg: A Data-dependent Within-layer Diversity Regularizer. (arXiv:2301.01352v1 [cs.LG])
    Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage the diversity of the activations within the same layer. To this end, we measure the pairwise similarity between the outputs of the neurons and use it to model the layer's overall diversity. We present an extensive empirical study confirming that the proposed approach enhances the performance of several state-of-the-art neural network models in multiple tasks. The code is publically available at \url{https://github.com/firasl/AAAI-23-WLD-Reg}  ( 2 min )
    Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE. (arXiv:2210.12918v2 [cs.CV] UPDATED)
    In many imaging modalities, objects of interest can occur in a variety of locations and poses (i.e. are subject to translations and rotations in 2d or 3d), but the location and pose of an object does not change its semantics (i.e. the object's essence). That is, the specific location and rotation of an airplane in satellite imagery, or the 3d rotation of a chair in a natural image, or the rotation of a particle in a cryo-electron micrograph, do not change the intrinsic nature of those objects. Here, we consider the problem of learning semantic representations of objects that are invariant to pose and location in a fully unsupervised manner. We address shortcomings in previous approaches to this problem by introducing TARGET-VAE, a translation and rotation group-equivariant variational autoencoder framework. TARGET-VAE combines three core innovations: 1) a rotation and translation group-equivariant encoder architecture, 2) a structurally disentangled distribution over latent rotation, translation, and a rotation-translation-invariant semantic object representation, which are jointly inferred by the approximate inference network, and 3) a spatially equivariant generator network. In comprehensive experiments, we show that TARGET-VAE learns disentangled representations without supervision that significantly improve upon, and avoid the pathologies of, previous methods. When trained on images highly corrupted by rotation and translation, the semantic representations learned by TARGET-VAE are similar to those learned on consistently posed objects, dramatically improving clustering in the semantic latent space. Furthermore, TARGET-VAE is able to perform remarkably accurate unsupervised pose and location inference. We expect methods like TARGET-VAE will underpin future approaches for unsupervised object generation, pose prediction, and object detection.  ( 2 min )
    GraB: Finding Provably Better Data Permutations than Random Reshuffling. (arXiv:2205.10733v3 [cs.LG] UPDATED)
    Random reshuffling, which randomly permutes the dataset each epoch, is widely adopted in model training because it yields faster convergence than with-replacement sampling. Recent studies indicate greedily chosen data orderings can further speed up convergence empirically, at the cost of using more computation and memory. However, greedy ordering lacks theoretical justification and has limited utility due to its non-trivial memory and computation overhead. In this paper, we first formulate an example-ordering framework named herding and answer affirmatively that SGD with herding converges at the rate $O(T^{-2/3})$ on smooth, non-convex objectives, faster than the $O(n^{1/3}T^{-2/3})$ obtained by random reshuffling, where $n$ denotes the number of data points and $T$ denotes the total number of iterations. To reduce the memory overhead, we leverage discrepancy minimization theory to propose an online Gradient Balancing algorithm (GraB) that enjoys the same rate as herding, while reducing the memory usage from $O(nd)$ to just $O(d)$ and computation from $O(n^2)$ to $O(n)$, where $d$ denotes the model dimension. We show empirically on applications including MNIST, CIFAR10, WikiText and GLUE that GraB can outperform random reshuffling in terms of both training and validation performance, and even outperform state-of-the-art greedy ordering while reducing memory usage over $100\times$.  ( 2 min )
    StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning. (arXiv:2110.06206v3 [cs.LG] UPDATED)
    Reinforcement Learning (RL) can be considered as a sequence modeling task: given a sequence of past state-action-reward experiences, an agent predicts a sequence of next actions. In this work, we propose State-Action-Reward Transformer (StARformer) for visual RL, which explicitly models short-term state-action-reward representations (StAR-representations), essentially introducing a Markovian-like inductive bias to improve long-term modeling. Our approach first extracts StAR-representations by self-attending image state patches, action, and reward tokens within a short temporal window. These are then combined with pure image state representations -- extracted as convolutional features, to perform self-attention over the whole sequence. Our experiments show that StARformer outperforms the state-of-the-art Transformer-based method on image-based Atari and DeepMind Control Suite benchmarks, in both offline-RL and imitation learning settings. StARformer is also more compliant with longer sequences of inputs. Our code is available at https://github.com/elicassion/StARformer.  ( 2 min )
    The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. (arXiv:2210.05021v2 [cs.LG] UPDATED)
    Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (artificial) data from the same distribution. However, this traditional viewpoint does not explain the success of prevalent augmentations in modern machine learning (e.g. randomized masking, cutout, mixup), that greatly alter the training data distribution. In this work, we develop a new theoretical framework to characterize the impact of a general class of DA on underparameterized and overparameterized linear model generalization. Our framework reveals that DA induces implicit spectral regularization through a combination of two distinct effects: a) manipulating the relative proportion of eigenvalues of the data covariance matrix in a training-data-dependent manner, and b) uniformly boosting the entire spectrum of the data covariance matrix through ridge regression. These effects, when applied to popular augmentations, give rise to a wide variety of phenomena, including discrepancies in generalization between over-parameterized and under-parameterized regimes and differences between regression and classification tasks. Our framework highlights the nuanced and sometimes surprising impacts of DA on generalization, and serves as a testbed for novel augmentation design.  ( 2 min )
    Is Integer Arithmetic Enough for Deep Learning Training?. (arXiv:2207.08822v3 [cs.LG] UPDATED)
    The ever-increasing computational complexity of deep learning models makes their training and deployment difficult on various cloud and edge platforms. Replacing floating-point arithmetic with low-bit integer arithmetic is a promising approach to save energy, memory footprint, and latency of deep learning models. As such, quantization has attracted the attention of researchers in recent years. However, using integer numbers to form a fully functional integer training pipeline including forward pass, back-propagation, and stochastic gradient descent is not studied in detail. Our empirical and mathematical results reveal that integer arithmetic seems to be enough to train deep learning models. Unlike recent proposals, instead of quantization, we directly switch the number representation of computations. Our novel training method forms a fully integer training pipeline that does not change the trajectory of the loss and accuracy compared to floating-point, nor does it need any special hyper-parameter tuning, distribution adjustment, or gradient clipping. Our experimental results show that our proposed method is effective in a wide variety of tasks such as classification (including vision transformers), object detection, and semantic segmentation.  ( 2 min )
    Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption. (arXiv:2301.01583v1 [cs.CV])
    Capsule neural networks replace simple, scalar-valued neurons with vector-valued capsules. They are motivated by the pattern recognition system in the human brain, where complex objects are decomposed into a hierarchy of simpler object parts. Such a hierarchy is referred to as a parse-tree. Conceptually, capsule neural networks have been defined to realize such parse-trees. The capsule neural network (CapsNet), by Sabour, Frosst, and Hinton, is the first actual implementation of the conceptual idea of capsule neural networks. CapsNets achieved state-of-the-art performance on simple image recognition tasks with fewer parameters and greater robustness to affine transformations than comparable approaches. This sparked extensive follow-up research. However, despite major efforts, no work was able to scale the CapsNet architecture to more reasonable-sized datasets. Here, we provide a reason for this failure and argue that it is most likely not possible to scale CapsNets beyond toy examples. In particular, we show that the concept of a parse-tree, the main idea behind capsule neuronal networks, is not present in CapsNets. We also show theoretically and experimentally that CapsNets suffer from a vanishing gradient problem that results in the starvation of many capsules during training.  ( 2 min )
    Process, Bias and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning. (arXiv:2205.05664v3 [cs.AR] UPDATED)
    Analog computing is attractive compared to digital computing due to its potential for achieving higher computational density and higher energy efficiency. However, unlike digital circuits, conventional analog computing circuits cannot be easily mapped across different process nodes due to differences in transistor biasing regimes, temperature variations and limited dynamic range. In this work, we generalize the previously reported margin-propagation-based analog computing framework for designing novel \textit{shape-based analog computing} (S-AC) circuits that can be easily cross-mapped across different process nodes. Similar to digital designs S-AC designs can also be scaled for precision, speed, and power. As a proof-of-concept, we show several examples of S-AC circuits implementing mathematical functions that are commonly used in machine learning (ML) architectures. Using circuit simulations we demonstrate that the circuit input/output characteristics remain robust when mapped from a planar CMOS 180nm process to a FinFET 7nm process. Also, using benchmark datasets we demonstrate that the classification accuracy of a S-AC based neural network remains robust when mapped across the two processes and to changes in temperature.  ( 2 min )
    Automating Nearest Neighbor Search Configuration with Constrained Optimization. (arXiv:2301.01702v1 [cs.LG])
    The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications. A number of techniques have been developed for ANN search that are efficient, accurate, and scalable. However, such techniques typically have a number of parameters that affect the speed-recall tradeoff, and exhibit poor performance when such parameters aren't properly set. Tuning these parameters has traditionally been a manual process, demanding in-depth knowledge of the underlying search algorithm. This is becoming an increasingly unrealistic demand as ANN search grows in popularity. To tackle this obstacle to ANN adoption, this work proposes a constrained optimization-based approach to tuning quantization-based ANN algorithms. Our technique takes just a desired search cost or recall as input, and then generates tunings that, empirically, are very close to the speed-recall Pareto frontier and give leading performance on standard benchmarks.  ( 2 min )
    GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation. (arXiv:2203.02177v2 [cs.LG] UPDATED)
    Conversations have become a critical data format on social media platforms. Understanding conversation from emotion, content and other aspects also attracts increasing attention from researchers due to its widespread application in human-computer interaction. In real-world environments, we often encounter the problem of incomplete modalities, which has become a core issue of conversation understanding. To address this problem, researchers propose various methods. However, existing approaches are mainly designed for individual utterances rather than conversational data, which cannot fully exploit temporal and speaker information in conversations. To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)", filling the gap of existing works. Our GCNet contains two well-designed graph neural network-based modules, "Speaker GNN" and "Temporal GNN", to capture temporal and speaker dependencies. To make full use of complete and incomplete data, we jointly optimize classification and reconstruction tasks in an end-to-end manner. To verify the effectiveness of our method, we conduct experiments on three benchmark conversational datasets. Experimental results demonstrate that our GCNet is superior to existing state-of-the-art approaches in incomplete multimodal learning. Code is available at https://github.com/zeroQiaoba/GCNet.  ( 2 min )
    Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach. (arXiv:2012.08679v5 [cs.NI] UPDATED)
    Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. As a crucial problem in MEC, service migration needs to decide how to migrate user services for maintaining the Quality-of-Service when users roam between MEC servers with limited coverage and capacity. However, finding an optimal migration policy is intractable due to the dynamic MEC environment and user mobility. Many existing studies make centralized migration decisions based on complete system-level information, which is time-consuming and also lacks desirable scalability. To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information. Specifically, the service migration problem is modeled as a Partially Observable Markov Decision Process (POMDP). To solve the POMDP, we design a new encoder network that combines a Long Short-Term Memory (LSTM) and an embedding matrix for effective extraction of hidden information, and further propose a tailored off-policy actor-critic algorithm for efficient training. The extensive experimental results based on real-world mobility traces demonstrate that this new method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms and can achieve near-optimal results on various MEC scenarios.  ( 2 min )
    Fairness in Graph Mining: A Survey. (arXiv:2204.09888v2 [cs.LG] UPDATED)
    Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we summarize the widely used datasets in this emerging research field and provide insights on current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances.  ( 2 min )
    Empirical Differential Privacy. (arXiv:1910.12820v5 [cs.LG] UPDATED)
    We show how to achieve differential privacy with no or reduced added noise, based on the empirical noise in the data itself. Unlike previous works on noiseless privacy, the empirical viewpoint avoids making any explicit assumptions about the random process generating the data.  ( 2 min )
    ExPoSe: Combining State-Based Exploration with Gradient-Based Online Search. (arXiv:2202.01461v3 [cs.AI] UPDATED)
    A tree-based online search algorithm iteratively simulates trajectories and updates action-values of a set of states stored in a tree structure. It works reasonably well in practice but fails to take advantage of the information gathered from similar states. Depending upon the smoothness of the action-value function, a simple way to interpolate information among similar states is to perform online learning; policy gradient search provides a practical algorithm to achieve this. However, policy gradient search does not have an explicit exploration mechanism, which is present in tree-based online search algorithms. In this paper, we propose an efficient and effective online search algorithm, named Exploratory Policy Gradient Search (ExPoSe), that leverages information sharing among states by directly updating the search policy parameters while following a well-defined exploration mechanism during the online search. We conduct experiments on several decision-making problems, including Atari games, Sokoban and Hamiltonian cycle search in sparse graphs and show that ExPoSe consistently outperforms popular online search algorithms across all domains.  ( 2 min )
    Social Fraud Detection Review: Methods, Challenges and Analysis. (arXiv:2111.05645v2 [cs.LG] UPDATED)
    Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user who carries out the review, and the item being reviewed. As features are extracted for the component representation, a feature-wise review is provided based on behavioral, text-based features and their combination. With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. The lack of labeled datasets is explained and potential solutions are suggested. To help new researchers in the area develop a better understanding, a topic analysis and an overview of future directions is provided in each step of the proposed systematic framework.  ( 2 min )
    Graph state-space models. (arXiv:2301.01741v1 [cs.LG])
    State-space models constitute an effective modeling tool to describe multivariate time series and operate by maintaining an updated representation of the system state from which predictions are made. Within this framework, relational inductive biases, e.g., associated with functional dependencies existing among signals, are not explicitly exploited leaving unattended great opportunities for effective modeling approaches. The manuscript aims, for the first time, at filling this gap by matching state-space modeling and spatio-temporal data where the relational information, say the functional graph capturing latent dependencies, is learned directly from data and is allowed to change over time. Within a probabilistic formulation that accounts for the uncertainty in the data-generating process, an encoder-decoder architecture is proposed to learn the state-space model end-to-end on a downstream task. The proposed methodological framework generalizes several state-of-the-art methods and demonstrates to be effective in extracting meaningful relational information while achieving optimal forecasting performance in controlled environments.  ( 2 min )
    Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks. (arXiv:2006.07356v3 [stat.ML] UPDATED)
    We investigate gradient descent training of wide neural networks and the corresponding implicit bias in function space. For univariate regression, we show that the solution of training a width-$n$ shallow ReLU network is within $n^{- 1/2}$ of the function which fits the training data and whose difference from the initial function has the smallest 2-norm of the second derivative weighted by a curvature penalty that depends on the probability distribution that is used to initialize the network parameters. We compute the curvature penalty function explicitly for various common initialization procedures. For instance, asymmetric initialization with a uniform distribution yields a constant curvature penalty, and thence the solution function is the natural cubic spline interpolation of the training data. \hj{For stochastic gradient descent we obtain the same implicit bias result.} We obtain a similar result for different activation functions. For multivariate regression we show an analogous result, whereby the second derivative is replaced by the Radon transform of a fractional Laplacian. For initialization schemes that yield a constant penalty function, the solutions are polyharmonic splines. Moreover, we show that the training trajectories are captured by trajectories of smoothing splines with decreasing regularization strength.  ( 2 min )
    Holistic Adversarial Robustness of Deep Learning Models. (arXiv:2202.07201v2 [cs.LG] UPDATED)
    Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. With the proliferation of deep-learning-based technology, the potential risks associated with model development and deployment can be amplified and become dreadful vulnerabilities. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models, including attacks, defenses, verification, and novel applications.  ( 2 min )
    Bias-Scalable Near-Memory CMOS Analog Processor for Machine Learning. (arXiv:2202.05022v3 [cs.ET] UPDATED)
    Bias-scalable analog computing is attractive for implementing machine learning (ML) processors with distinct power-performance specifications. For instance, ML implementations for server workloads are focused on higher computational throughput for faster training, whereas ML implementations for edge devices are focused on energy-efficient inference. In this paper, we demonstrate the implementation of bias-scalable approximate analog computing circuits using the generalization of the margin-propagation principle called shape-based analog computing (S-AC). The resulting S-AC core integrates several near-memory compute elements, which include: (a) non-linear activation functions; (b) inner-product compute circuits; and (c) a mixed-signal compressive memory, all of which can be scaled for performance or power while preserving its functionality. Using measured results from prototypes fabricated in a 180nm CMOS process, we demonstrate that the performance of computing modules remains robust to transistor biasing and variations in temperature. In this paper, we also demonstrate the effect of bias-scalability and computational accuracy on a simple ML regression task.  ( 2 min )
    Learning to segment fetal brain tissue from noisy annotations. (arXiv:2203.14962v2 [eess.IV] UPDATED)
    Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI.  ( 2 min )
    Large-width asymptotics for ReLU neural networks with $\alpha$-Stable initializations. (arXiv:2206.08065v3 [cs.LG] UPDATED)
    There is a recent and growing literature on large-width asymptotic properties of Gaussian neural networks (NNs), namely NNs whose weights are initialized as Gaussian distributions. Two popular problems are: i) the study of the large-width distributions of NNs, which characterizes the infinitely wide limit of a rescaled NN in terms of a Gaussian stochastic process; ii) the study of the large-width training dynamics of NNs, which characterizes the infinitely wide dynamics in terms of a deterministic kernel, referred to as the neural tangent kernel (NTK), and shows that, for a sufficiently large width, the gradient descent achieves zero training error at a linear rate. In this paper, we consider these problems for $\alpha$-Stable NNs, namely NNs whose weights are initialized as $\alpha$-Stable distributions with $\alpha\in(0,2]$. First, for $\alpha$-Stable NNs with a ReLU activation function, we show that if the NN's width goes to infinity then a rescaled NN converges weakly to an $\alpha$-Stable stochastic process. As a difference with respect to the Gaussian setting, our result shows that the choice of the activation function affects the scaling of the NN, that is: to achieve the infinitely wide $\alpha$-Stable process, the ReLU activation requires an additional logarithmic term in the scaling with respect to sub-linear activations. Then, we study the large-width training dynamics of $\alpha$-Stable ReLU-NNs, characterizing the infinitely wide dynamics in terms of a random kernel, referred to as the $\alpha$-Stable NTK, and showing that, for a sufficiently large width, the gradient descent achieves zero training error at a linear rate. The randomness of the $\alpha$-Stable NTK is a further difference with respect to the Gaussian setting, that is: within the $\alpha$-Stable setting, the randomness of the NN at initialization does not vanish in the large-width regime of the training.  ( 3 min )
    Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design. (arXiv:2203.07120v2 [cs.LG] UPDATED)
    Various real-world scientific applications involve the mathematical modeling of complex uncertain systems with numerous unknown parameters. Accurate parameter estimation is often practically infeasible in such systems, as the available training data may be insufficient and the cost of acquiring additional data may be high. In such cases, it is desirable to represent the model uncertainty in a Bayesian paradigm, based on which we can design robust operators retaining the best overall performance across all possible models and design optimal experiments that can effectively reduce uncertainty to enhance the performance of such operators maximally. While objective-based uncertainty quantification (objective-UQ) based on MOCU (mean objective cost of uncertainty) has been an effective means for quantifying uncertainty in complex systems, a major drawback has been the high computational cost of estimating MOCU. In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach. We adopt a neural message-passing model for surrogate modeling, incorporating a novel axiomatic constraint loss that penalizes an increase in the estimated system uncertainty. As an illustrative example, we consider the optimal experimental design (OED) problem for uncertain Kuramoto models, where the goal is to predict the experiments that can most effectively enhance robust synchronization performance through uncertainty reduction. We show that our proposed approach can accelerate MOCU-based OED by four to five orders of magnitude, virtually without any visible performance loss compared to the state-of-the-art. The proposed approach is applicable to general OED tasks, beyond the Kuramoto model.  ( 2 min )
    Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation. (arXiv:2205.10481v2 [cs.CV] UPDATED)
    In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix. By representing the limited amount of supervisory information as a pairwise constraint matrix, we observe that the ideal affinity matrix for clustering shares the same low-rank structure as the ideal pairwise constraint matrix. Thus, we stack the two matrices into a 3-D tensor, where a global low-rank constraint is imposed to promote the affinity matrix construction and augment the initial pairwise constraints synchronously. Besides, we use the local geometry structure of input samples to complement the global low-rank prior to achieve better affinity matrix learning. The proposed model is formulated as a Laplacian graph regularized convex low-rank tensor representation problem, which is further solved with an alternative iterative algorithm. In addition, we propose to refine the affinity matrix with the augmented pairwise constraints. Comprehensive experimental results on eight commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods. The code is publicly available at https://github.com/GuanxingLu/Subspace-Clustering.  ( 2 min )
    GUAP: Graph Universal Attack Through Adversarial Patching. (arXiv:2301.01731v1 [cs.LG])
    Graph neural networks (GNNs) are a class of effective deep learning models for node classification tasks; yet their predictive capability may be severely compromised under adversarially designed unnoticeable perturbations to the graph structure and/or node data. Most of the current work on graph adversarial attacks aims at lowering the overall prediction accuracy, but we argue that the resulting abnormal model performance may catch attention easily and invite quick counterattack. Moreover, attacks through modification of existing graph data may be hard to conduct if good security protocols are implemented. In this work, we consider an easier attack harder to be noticed, through adversarially patching the graph with new nodes and edges. The attack is universal: it targets a single node each time and flips its connection to the same set of patch nodes. The attack is unnoticeable: it does not modify the predictions of nodes other than the target. We develop an algorithm, named GUAP, that achieves high attack success rate but meanwhile preserves the prediction accuracy. GUAP is fast to train by employing a sampling strategy. We demonstrate that a 5% sampling in each epoch yields 20x speedup in training, with only a slight degradation in attack performance. Additionally, we show that the adversarial patch trained with the graph convolutional network transfers well to other GNNs, such as the graph attention network.  ( 2 min )
    Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels. (arXiv:2005.14220v5 [cs.IT] UPDATED)
    Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design of the communications in a multi-agent system (MAS) in which agents cooperate to maximize the averaged sum of discounted one-stage rewards of a collaborative task. Due to the bit-budgeted communications between the agents, each agent should efficiently represent its local observation and communicate an abstracted version of the observations to improve the collaborative task performance. We first show that this problem can be approximated as a form of data-quantization problem which we call task-oriented data compression (TODC). We then introduce the state-aggregation for information compression algorithm (SAIC) to solve the formulated TODC problem. It is shown that SAIC is able to achieve near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a geometric consensus problem and its performance is compared with several benchmarks. Numerical experiments confirm the promise of this indirect design approach for task-oriented multi-agent communications.  ( 3 min )
    Constrained regret minimization for multi-criterion multi-armed bandits. (arXiv:2006.09649v2 [cs.LG] UPDATED)
    We consider a stochastic multi-armed bandit setting and study the problem of constrained regret minimization over a given time horizon. Each arm is associated with an unknown, possibly multi-dimensional distribution, and the merit of an arm is determined by several, possibly conflicting attributes. The aim is to optimize a 'primary' attribute subject to user-provided constraints on other 'secondary' attributes. We assume that the attributes can be estimated using samples from the arms' distributions, and that the estimators enjoy suitable concentration properties. We propose an algorithm called Con-LCB that guarantees a logarithmic regret, i.e., the average number of plays of all non-optimal arms is at most logarithmic in the horizon. The algorithm also outputs a Boolean flag that correctly identifies, with high probability, whether the given instance is feasible/infeasible with respect to the constraints. We also show that Con-LCB is optimal within a universal constant, i.e., that more sophisticated algorithms cannot do much better universally. Finally, we establish a fundamental trade-off between regret minimization and feasibility identification. Our framework finds natural applications, for instance, in financial portfolio optimization, where risk constrained maximization of expected return is meaningful.  ( 2 min )
    A Dual-Purpose Deep Learning Model for Auscultated Lung and Tracheal Sound Analysis Based on Mixed Set Training. (arXiv:2107.04229v2 [cs.SD] UPDATED)
    Many deep learning-based computerized respiratory sound analysis methods have previously been developed. However, these studies focus on either lung sound only or tracheal sound only. The effectiveness of using a lung sound analysis algorithm on tracheal sound and vice versa has never been investigated. Furthermore, no one knows whether using lung and tracheal sounds together in training a respiratory sound analysis model is beneficial. In this study, we first constructed a tracheal sound database, HF_Tracheal_V1, containing 10448 15-s tracheal sound recordings, 21741 inhalation labels, 15858 exhalation labels, and 6414 continuous adventitious sound (CAS) labels. HF_Tracheal_V1 and our previously built lung sound database, HF_Lung_V2, were either combined (mixed set), used one after the other (domain adaptation), or used alone to train convolutional neural network bidirectional gate recurrent unit models for inhalation, exhalation, and CAS detection in lung and tracheal sounds. The results revealed that the models trained using lung sound alone performed poorly in tracheal sound analysis and vice versa. However, mixed set training or domain adaptation improved the performance for 1) inhalation and exhalation detection in lung sounds and 2) inhalation, exhalation, and CAS detection in tracheal sounds compared to positive controls (the models trained using lung sound alone and used in lung sound analysis and vice versa). In particular, the model trained on the mixed set had great flexibility to serve two purposes, lung and tracheal sound analyses, at the same time.  ( 3 min )
    Best Arm Identification with Contextual Information under a Small Gap. (arXiv:2209.07330v4 [cs.LG] UPDATED)
    We study the best-arm identification (BAI) problem with a fixed budget and contextual (covariate) information. In each round of an adaptive experiment, after observing contextual information, we choose a treatment arm using past observations and current context. Our goal is to identify the best treatment arm, which is a treatment arm with the maximal expected reward marginalized over the contextual distribution, with a minimal probability of misidentification. In this study, we consider a class of nonparametric bandit models that converge to location-shift models when the gaps go to zero. First, we derive lower bounds of the misidentification probability for a certain class of strategies and bandit models (probabilistic models of potential outcomes) under a small-gap regime. A small-gap regime is a situation where gaps of the expected rewards between the best and suboptimal treatment arms go to zero, which corresponds to one of the worst cases in identifying the best treatment arm. We then develop the ``Random Sampling (RS)-Augmented Inverse Probability weighting (AIPW) strategy,'' which is asymptotically optimal in the sense that the probability of misidentification under the strategy matches the lower bound when the budget goes to infinity in the small-gap regime. The RS-AIPW strategy consists of the RS rule tracking a target sample allocation ratio and the recommendation rule using the AIPW estimator.  ( 2 min )
    Pattern Recognition Experiments on Mathematical Expressions. (arXiv:2301.01624v1 [math.NT])
    We provide the results of pattern recognition experiments on mathematical expressions. We give a few examples of conjectured results. None of which was thoroughly checked for novelty. We did not attempt to prove all the relations found and focused on their generation.  ( 2 min )
    Cost-Sensitive Stacking: an Empirical Evaluation. (arXiv:2301.01748v1 [cs.LG])
    Many real-world classification problems are cost-sensitive in nature, such that the misclassification costs vary between data instances. Cost-sensitive learning adapts classification algorithms to account for differences in misclassification costs. Stacking is an ensemble method that uses predictions from several classifiers as the training data for another classifier, which in turn makes the final classification decision. While a large body of empirical work exists where stacking is applied in various domains, very few of these works take the misclassification costs into account. In fact, there is no consensus in the literature as to what cost-sensitive stacking is. In this paper we perform extensive experiments with the aim of establishing what the appropriate setup for a cost-sensitive stacking ensemble is. Our experiments, conducted on twelve datasets from a number of application domains, using real, instance-dependent misclassification costs, show that for best performance, both levels of stacking require cost-sensitive classification decision.  ( 2 min )
    Augmenting data-driven models for energy systems through feature engineering: A Python framework for feature engineering. (arXiv:2301.01720v1 [cs.LG])
    Data-driven modeling is an approach in energy systems modeling that has been gaining popularity. In data-driven modeling, machine learning methods such as linear regression, neural networks or decision-tree based methods are being applied. While these methods do not require domain knowledge, they are sensitive to data quality. Therefore, improving data quality in a dataset is beneficial for creating machine learning-based models. The improvement of data quality can be implemented through preprocessing methods. A selected type of preprocessing is feature engineering, which focuses on evaluating and improving the quality of certain features inside the dataset. Feature engineering methods include methods such as feature creation, feature expansion, or feature selection. In this work, a Python framework containing different feature engineering methods is presented. This framework contains different methods for feature creation, expansion and selection; in addition, methods for transforming or filtering data are implemented. The implementation of the framework is based on the Python library scikit-learn. The framework is demonstrated on a case study of a use case from energy demand prediction. A data-driven model is created including selected feature engineering methods. The results show an improvement in prediction accuracy through the engineered features.  ( 2 min )
    Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow. (arXiv:2301.01766v1 [math.ST])
    Gaussian mixture models form a flexible and expressive parametric family of distributions that has found applications in a wide variety of applications. Unfortunately, fitting these models to data is a notoriously hard problem from a computational perspective. Currently, only moment-based methods enjoy theoretical guarantees while likelihood-based methods are dominated by heuristics such as Expectation-Maximization that are known to fail in simple examples. In this work, we propose a new algorithm to compute the nonparametric maximum likelihood estimator (NPMLE) in a Gaussian mixture model. Our method is based on gradient descent over the space of probability measures equipped with the Wasserstein-Fisher-Rao geometry for which we establish convergence guarantees. In practice, it can be approximated using an interacting particle system where the weight and location of particles are updated alternately. We conduct extensive numerical experiments to confirm the effectiveness of the proposed algorithm compared not only to classical benchmarks but also to similar gradient descent algorithms with respect to simpler geometries. In particular, these simulations illustrate the benefit of updating both weight and location of the interacting particles.  ( 2 min )
    Episodes Discovery Recommendation with Multi-Source Augmentations. (arXiv:2301.01737v1 [cs.IR])
    Recommender systems (RS) commonly retrieve potential candidate items for users from a massive number of items by modeling user interests based on historical interactions. However, historical interaction data is highly sparse, and most items are long-tail items, which limits the representation learning for item discovery. This problem is further augmented by the discovery of novel or cold-start items. For example, after a user displays interest in bitcoin financial investment shows in the podcast space, a recommender system may want to suggest, e.g., a newly released blockchain episode from a more technical show. Episode correlations help the discovery, especially when interaction data of episodes is limited. Accordingly, we build upon the classical Two-Tower model and introduce the novel Multi-Source Augmentations using a Contrastive Learning framework (MSACL) to enhance episode embedding learning by incorporating positive episodes from numerous correlated semantics. Extensive experiments on a real-world podcast recommendation dataset from a large audio streaming platform demonstrate the effectiveness of the proposed framework for user podcast exploration and cold-start episode recommendation.  ( 2 min )
    Matrices with Gaussian noise: optimal estimates for singular subspace perturbation. (arXiv:1803.00679v2 [stat.ML] UPDATED)
    The Davis--Kahan--Wedin $\sin \Theta$ theorem describes how the singular subspaces of a matrix change when subjected to a small perturbation. This classic result is sharp in the worst case scenario. In this paper, we prove a stochastic version of the Davis--Kahan--Wedin $\sin \Theta$ theorem when the perturbation is a Gaussian random matrix. Under certain structural assumptions, we obtain an optimal bound that significantly improves upon the classic Davis--Kahan--Wedin $\sin \Theta$ theorem. One of our key tools is a new perturbation bound for the singular values, which may be of independent interest.  ( 2 min )
    Text sampling strategies for predicting missing bibliographic links. (arXiv:2301.01673v1 [cs.LG])
    The paper proposes various strategies for sampling text data when performing automatic sentence classification for the purpose of detecting missing bibliographic links. We construct samples based on sentences as semantic units of the text and add their immediate context which consists of several neighboring sentences. We examine a number of sampling strategies that differ in context size and position. The experiment is carried out on the collection of STEM scientific papers. Including the context of sentences into samples improves the result of their classification. We automatically determine the optimal sampling strategy for a given text collection by implementing an ensemble voting when classifying the same data sampled in different ways. Sampling strategy taking into account the sentence context with hard voting procedure leads to the classification accuracy of 98% (F1-score). This method of detecting missing bibliographic links can be used in recommendation engines of applied intelligent information systems.  ( 2 min )
    Learning-based MPC from Big Data Using Reinforcement Learning. (arXiv:2301.01667v1 [eess.SY])
    This paper presents an approach for learning Model Predictive Control (MPC) schemes directly from data using Reinforcement Learning (RL) methods. The state-of-the-art learning methods use RL to improve the performance of parameterized MPC schemes. However, these learning algorithms are often gradient-based methods that require frequent evaluations of computationally expensive MPC schemes, thereby restricting their use on big datasets. We propose to tackle this issue by using tools from RL to learn a parameterized MPC scheme directly from data in an offline fashion. Our approach derives an MPC scheme without having to solve it over the collected dataset, thereby eliminating the computational complexity of existing techniques for big data. We evaluate the proposed method on three simulated experiments of varying complexity.  ( 2 min )
    A General Framework for Learning Mean-Field Games. (arXiv:2003.06069v3 [cs.LG] UPDATED)
    This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population. It first establishes the existence of a unique Nash Equilibrium to this GMFG, and demonstrates that naively combining reinforcement learning with the fixed-point approach in classical MFGs yields unstable algorithms. It then proposes value-based and policy-based reinforcement learning algorithms (GMF-V and GMF-P, respectively) with smoothed policies, with analysis of their convergence properties and computational complexities. Experiments on an equilibrium product pricing problem demonstrate that GMF-V-Q and GMF-P-TRPO, two specific instantiations of GMF-V and GMF-P, respectively, with Q-learning and TRPO, are both efficient and robust in the GMFG setting. Moreover, their performance is superior in convergence speed, accuracy, and stability when compared with existing algorithms for multi-agent reinforcement learning in the $N$-player setting.  ( 2 min )
    Learning Decorrelated Representations Efficiently Using Fast Fourier Transform. (arXiv:2301.01569v1 [cs.LG])
    Barlow Twins and VICReg are self-supervised representation learning models that use regularizers to decorrelate features. Although they work as well as conventional representation learning models, their training can be computationally demanding if the dimension of projected representations is high; as these regularizers are defined in terms of individual elements of a cross-correlation or covariance matrix, computing the loss for $d$-dimensional projected representations of $n$ samples takes $O(n d^2)$ time. In this paper, we propose a relaxed version of decorrelating regularizers that can be computed in $O(n d\log d)$ time by the fast Fourier transform. We also propose an inexpensive trick to mitigate the undesirable local minima that develop with the relaxation. Models learning representations using the proposed regularizers show comparable accuracy to existing models in downstream tasks, whereas the training requires less memory and is faster when $d$ is large.  ( 2 min )
    Learning Ambiguity from Crowd Sequential Annotations. (arXiv:2301.01579v1 [cs.CL])
    Most crowdsourcing learning methods treat disagreement between annotators as noisy labelings while inter-disagreement among experts is often a good indicator for the ambiguity and uncertainty that is inherent in natural language. In this paper, we propose a framework called Learning Ambiguity from Crowd Sequential Annotations (LA-SCA) to explore the inter-disagreement between reliable annotators and effectively preserve confusing label information. First, a hierarchical Bayesian model is developed to infer ground-truth from crowds and group the annotators with similar reliability together. By modeling the relationship between the size of group the annotator involved in, the annotator's reliability and element's unambiguity in each sequence, inter-disagreement between reliable annotators on ambiguous elements is computed to obtain label confusing information that is incorporated to cost-sensitive sequence labeling. Experimental results on POS tagging and NER tasks show that our proposed framework achieves competitive performance in inferring ground-truth from crowds and predicting unknown sequences, and interpreting hierarchical clustering results helps discover labeling patterns of annotators with similar reliability.  ( 2 min )
    Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification. (arXiv:2301.01597v1 [quant-ph])
    Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Some QNNs with specific encoding methods can be efficiently simulated by classical surrogates, while others with quantum memory may perform better than classical classifiers. Here we systematically investigate the problem-dependent power of quantum neural classifiers (QCs) on multi-class classification tasks. Through the analysis of expected risk, a measure that weighs the training loss and the generalization error of a classifier jointly, we identify two key findings: first, the training loss dominates the power rather than the generalization ability; second, QCs undergo a U-shaped risk curve, in contrast to the double-descent risk curve of deep neural classifiers. We also reveal the intrinsic connection between optimal QCs and the Helstrom bound and the equiangular tight frame. Using these findings, we propose a method that uses loss dynamics to probe whether a QC may be more effective than a classical classifier on a particular learning task. Numerical results demonstrate the effectiveness of our approach to explain the superiority of QCs over multilayer Perceptron on parity datasets and their limitations over convolutional neural networks on image datasets. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.  ( 2 min )
    COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images. (arXiv:2301.01679v1 [eess.IV])
    As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of a huge amount of well-annotated data poses a challenge in building effective deep neural networks in the case of novel diseases and pandemics. Motivated by this, we present COVID-Net USPro, an explainable few-shot deep prototypical network, that monitors and detects COVID-19 positive cases with high precision and recall from minimal ultrasound images. COVID-Net USPro achieves 99.65% overall accuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases when trained with only 5 shots. The analytic pipeline and results were verified by our contributing clinician with extensive experience in POCUS interpretation, ensuring that the network makes decisions based on actual patterns.  ( 2 min )
    Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis. (arXiv:2301.01588v1 [q-bio.NC])
    Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with First-Episode Psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian Process Classifier (GPC), to demonstrate the practicality of resting-state Power Spectral Density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.  ( 2 min )
    Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries. (arXiv:2301.01701v1 [cs.CR])
    Reverse engineering binaries is required to understand and analyse programs for which the source code is unavailable. Decompilers can transform the largely unreadable binaries into a more readable source code-like representation. However, reverse engineering is time-consuming, much of which is taken up by labelling the functions with semantic information. While the automated summarisation of decompiled code can help Reverse Engineers understand and analyse binaries, current work mainly focuses on summarising source code, and no suitable dataset exists for this task. In this work, we extend large pre-trained language models of source code to summarise decompiled binary functions. Furthermore, we investigate the impact of input and data properties on the performance of such models. Our approach consists of two main components; the data and the model. We first build CAPYBARA, a dataset of 214K decompiled function-documentation pairs across various compiler optimisations. We extend CAPYBARA further by generating synthetic datasets and deduplicating the data. Next, we fine-tune the CodeT5 base model with CAPYBARA to create BinT5. BinT5 achieves the state-of-the-art BLEU-4 score of 60.83, 58.82, and 44.21 for summarising source, decompiled, and synthetically stripped decompiled code, respectively. This indicates that these models can be extended to decompiled binaries successfully. Finally, we found that the performance of BinT5 is not heavily dependent on the dataset size and compiler optimisation level. We recommend future research to further investigate transferring knowledge when working with less expressive input formats such as stripped binaries.  ( 2 min )
    On the Convergence of Stochastic Gradient Descent in Low-precision Number Formats. (arXiv:2301.01651v1 [cs.LG])
    Deep learning models are dominating almost all artificial intelligence tasks such as vision, text, and speech processing. Stochastic Gradient Descent (SGD) is the main tool for training such models, where the computations are usually performed in single-precision floating-point number format. The convergence of single-precision SGD is normally aligned with the theoretical results of real numbers since they exhibit negligible error. However, the numerical error increases when the computations are performed in low-precision number formats. This provides compelling reasons to study the SGD convergence adapted for low-precision computations. We present both deterministic and stochastic analysis of the SGD algorithm, obtaining bounds that show the effect of number format. Such bounds can provide guidelines as to how SGD convergence is affected when constraints render the possibility of performing high-precision computations remote.  ( 2 min )
    On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations. (arXiv:2301.01481v1 [cs.CV])
    Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging.  ( 2 min )
    A Survey on Deep Industrial Transfer Learning in Fault Prognostics. (arXiv:2301.01705v1 [cs.LG])
    Due to its probabilistic nature, fault prognostics is a prime example of a use case for deep learning utilizing big data. However, the low availability of such data sets combined with the high effort of fitting, parameterizing and evaluating complex learning algorithms to the heterogenous and dynamic settings typical for industrial applications oftentimes prevents the practical application of this approach. Automatic adaptation to new or dynamically changing fault prognostics scenarios can be achieved using transfer learning or continual learning methods. In this paper, a first survey of such approaches is carried out, aiming at establishing best practices for future research in this field. It is shown that the field is lacking common benchmarks to robustly compare results and facilitate scientific progress. Therefore, the data sets utilized in these publications are surveyed as well in order to identify suitable candidates for such benchmark scenarios.  ( 2 min )
    Data-Driven Model Identification via Hyperparameter Optimization for the Autonomous Racing System. (arXiv:2301.01470v1 [cs.RO])
    In this letter, we propose a model identification method via hyperparameter optimization (MIHO). Our method is able to identify the parameters of the parametric models in a data-driven manner. We utilize MIHO for the dynamics parameters of the AV-21, the full-scaled autonomous race vehicle, and integrate them into our model-based planning and control systems. In experiments, the models with the optimized parameters demonstrate the generalization ability of the vehicle dynamics model. We further conduct extensive field tests to validate our model-based system. The tests show that our race systems leverage the learned model dynamics well and successfully perform obstacle avoidance and high-speed driving over $200 km/h$ at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for MIHO and videos of the tests are available at https://github.com/hynkis/MIHO.  ( 2 min )
    CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis. (arXiv:2301.01642v1 [stat.ML])
    There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which,in turn, also motivates an urgent need for psychiatrists to fully understand the decision behavior of the used GNNs. However, most of the existing GNN explainers are either post-hoc in which another interpretive model needs to be created to explain a well-trained GNN, or do not consider the causal relationship between the extracted explanation and the decision, such that the explanation itself contains spurious correlations and suffers from weak faithfulness. In this work, we propose a granger causality-inspired graph neural network (CI-GNN), a built-in interpretable model that is able to identify the most influential subgraph (i.e., functional connectivity within brain regions) that is causally related to the decision (e.g., major depressive disorder patients or healthy controls), without the training of an auxillary interpretive network. CI-GNN learns disentangled subgraph-level representations {\alpha} and \b{eta} that encode, respectively, the causal and noncausal aspects of original graph under a graph variational autoencoder framework, regularized by a conditional mutual information (CMI) constraint. We theoretically justify the validity of the CMI regulation in capturing the causal relationship. We also empirically evaluate the performance of CI-GNN against three baseline GNNs and four state-of-the-art GNN explainers on synthetic data and two large-scale brain disease datasets. We observe that CI-GNN achieves the best performance in a wide range of metrics and provides more reliable and concise explanations which have clinical evidence.  ( 2 min )
    Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE. (arXiv:2301.01596v1 [cs.LG])
    The global COVID-19 pandemic has caused more than six million deaths worldwide. Medicalized hotels were established in Taiwan as quarantine facilities for COVID-19 patients with no or mild symptoms. Due to limited medical care available at these hotels, it is of paramount importance to identify patients at risk of clinical deterioration. This study aimed to develop and evaluate a graph-based deep learning approach for progressive hospital transfer risk prediction in a medicalized hotel setting. Vital sign measurements were obtained for 632 patients and daily patient similarity graphs were constructed. Inductive graph convolutional network models were trained on top of the temporally integrated graphs to predict hospital transfer risk. The proposed models achieved AUC scores above 0.83 for hospital transfer risk prediction based on the measurements of past 1, 2, and 3 days, outperforming baseline machine learning methods. A post-hoc analysis on the constructed diffusion-based graph using Local Clustering Coefficient discovered a high-risk cluster with significantly older mean age, higher body temperature, lower SpO2, and shorter length of stay. Further time-to-hospital-transfer survival analysis also revealed a significant decrease in survival probability in the discovered high-risk cluster. The obtained results demonstrated promising predictability and interpretability of the proposed graph-based approach. This technique may help preemptively detect high-risk patients at community-based medical facilities similar to a medicalized hotel.  ( 2 min )
    Multi-Task Learning with Prior Information. (arXiv:2301.01572v1 [cs.LG])
    Multi-task learning aims to boost the generalization performance of multiple related tasks simultaneously by leveraging information contained in those tasks. In this paper, we propose a multi-task learning framework, where we utilize prior knowledge about the relations between features. We also impose a penalty on the coefficients changing for each specific feature to ensure related tasks have similar coefficients on common features shared among them. In addition, we capture a common set of features via group sparsity. The objective is formulated as a non-smooth convex optimization problem, which can be solved with various methods, including gradient descent method with fixed stepsize, iterative shrinkage-thresholding algorithm (ISTA) with back-tracking, and its variation -- fast iterative shrinkage-thresholding algorithm (FISTA). In light of the sub-linear convergence rate of the methods aforementioned, we propose an asymptotically linear convergent algorithm with theoretical guarantee. Empirical experiments on both regression and classification tasks with real-world datasets demonstrate that our proposed algorithms are capable of improving the generalization performance of multiple related tasks.  ( 2 min )
    Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning. (arXiv:2301.01593v1 [cs.CY])
    In this paper, we study the problem of MOOC quality evaluation which is essential for improving the course materials, promoting students' learning efficiency, and benefiting user services. While achieving promising performances, current works still suffer from the complicated interactions and relationships of entities in MOOC platforms. To tackle the challenges, we formulate the problem as a course representation learning task-based and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation. Specifically, We first build a MOOC Heterogeneous Network (HIN) to represent the interactions and relationships among entities in MOOC platforms. And then we decompose the MOOC HIN into multiple single-relation graphs based on meta-paths to depict the multi-view semantics of courses. The course representation learning can be further converted to a multi-view graph representation task. Different from traditional graph representation learning, the learned course representations are expected to match the following three types of validity: (1) the agreement on expressiveness between the raw course portfolio and the learned course representations; (2) the consistency between the representations in each view and the unified representations; (3) the alignment between the course and MOOC platform representations. Therefore, we propose to exploit mutual information for preserving the validity of course representations. We conduct extensive experiments over real-world MOOC datasets to demonstrate the effectiveness of our proposed method.  ( 2 min )
    Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer. (arXiv:2301.01664v1 [cs.CL])
    Recent studies on knowledge graphs (KGs) show that path-based methods empowered by pre-trained language models perform well in the provision of inductive and explainable relation predictions. In this paper, we introduce the concepts of relation path coverage and relation path confidence to filter out unreliable paths prior to model training to elevate the model performance. Moreover, we propose Knowledge Reasoning Sentence Transformer (KRST) to predict inductive relations in KGs. KRST is designed to encode the extracted reliable paths in KGs, allowing us to properly cluster paths and provide multi-aspect explanations. We conduct extensive experiments on three real-world datasets. The experimental results show that compared to SOTA models, KRST achieves the best performance in most transductive and inductive test cases (4 of 6), and in 11 of 12 few-shot test cases.  ( 2 min )
    UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach. (arXiv:2301.01474v1 [eess.SY])
    Metaverse is expected to create a virtual world closely connected with reality to provide users with immersive experience with the support of 5G high data rate communication technique. A huge amount of data in physical world needs to be synchronized to the virtual world to provide immersive experience for users, and there will be higher requirements on coverage to include more users into Metaverse. However, 5G signal suffers severe attenuation, which makes it more expensive to maintain the same coverage. Unmanned aerial vehicle (UAV) is a promising candidate technique for future implementation of Metaverse as a low-cost and high-mobility platform for communication devices. In this paper, we propose a proximal policy optimization (PPO) based double-agent cooperative reinforcement learning method for channel allocation and trajectory control of UAV to collect and synchronize data from the physical world to the virtual world, and expand the coverage of Metaverse services economically. Simulation results show that our proposed method is able to achieve better performance compared to the benchmark approaches.  ( 2 min )
    Federated Learning for Data Streams. (arXiv:2301.01542v1 [cs.LG])
    Federated learning (FL) is an effective solution to train machine learning models on the increasing amount of data generated by IoT devices and smartphones while keeping such data localized. Most previous work on federated learning assumes that clients operate on static datasets collected before training starts. This approach may be inefficient because 1) it ignores new samples clients collect during training, and 2) it may require a potentially long preparatory phase for clients to collect enough data. Moreover, learning on static datasets may be simply impossible in scenarios with small aggregate storage across devices. It is, therefore, necessary to design federated algorithms able to learn from data streams. In this work, we formulate and study the problem of \emph{federated learning for data streams}. We propose a general FL algorithm to learn from data streams through an opportune weighted empirical risk minimization. Our theoretical analysis provides insights to configure such an algorithm, and we evaluate its performance on a wide range of machine learning tasks.  ( 2 min )
    Counterfactual Explanations for Land Cover Mapping in a Multi-class Setting. (arXiv:2301.01520v1 [cs.LG])
    Counterfactual explanations are an emerging tool to enhance interpretability of deep learning models. Given a sample, these methods seek to find and display to the user similar samples across the decision boundary. In this paper, we propose a generative adversarial counterfactual approach for satellite image time series in a multi-class setting for the land cover classification task. One of the distinctive features of the proposed approach is the lack of prior assumption on the targeted class for a given counterfactual explanation. This inherent flexibility allows for the discovery of interesting information on the relationship between land cover classes. The other feature consists of encouraging the counterfactual to differ from the original sample only in a small and compact temporal segment. These time-contiguous perturbations allow for a much sparser and, thus, interpretable solution. Furthermore, plausibility/realism of the generated counterfactual explanations is enforced via the proposed adversarial learning strategy.  ( 2 min )
    Machine Learning technique for isotopic determination of radioisotopes using HPGe $\mathrm{\gamma}$-ray spectra. (arXiv:2301.01415v1 [physics.data-an])
    $\mathrm{\gamma}$-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. In this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing $\mathrm{\gamma}$-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application.  ( 2 min )
    Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach. (arXiv:2301.01405v1 [cs.LG])
    Learning from noisy labels plays an important role in the deep learning era. Despite numerous studies with promising results, identifying clean labels from a noisily-annotated dataset is still challenging since the conventional noisy label learning problem with single noisy label per instance is not identifiable, i.e., it does not theoretically have a unique solution unless one has access to clean labels or introduces additional assumptions. This paper aims to formally investigate such identifiability issue by formulating the noisy label learning problem as a multinomial mixture model, enabling the formulation of the identifiability constraint. In particular, we prove that the noisy label learning problem is identifiable if there are at least $2C - 1$ noisy labels per instance provided, with $C$ being the number of classes. In light of such requirement, we propose a method that automatically generates additional noisy labels per training sample by estimating the noisy label distribution based on nearest neighbours. Such additional noisy labels allow us to apply the Expectation - Maximisation algorithm to estimate the posterior of clean labels. We empirically demonstrate that the proposed method is not only capable of estimating clean labels without any heuristics in several challenging label noise benchmarks, including synthetic, web-controlled and real-world label noises, but also of performing competitively with many state-of-the-art methods.  ( 2 min )
    Kernel Subspace and Feature Extraction. (arXiv:2301.01410v1 [cs.LG])
    We study kernel methods in machine learning from the perspective of feature subspace. We establish a one-to-one correspondence between feature subspaces and kernels and propose an information-theoretic measure for kernels. In particular, we construct a kernel from Hirschfeld--Gebelein--R\'{e}nyi maximal correlation functions, coined the maximal correlation kernel, and demonstrate its information-theoretic optimality. We use the support vector machine (SVM) as an example to illustrate a connection between kernel methods and feature extraction approaches. We show that the kernel SVM on maximal correlation kernel achieves minimum prediction error. Finally, we interpret the Fisher kernel as a special maximal correlation kernel and establish its optimality.  ( 2 min )
    The Predictive Forward-Forward Algorithm. (arXiv:2301.01452v1 [cs.LG])
    In this work, we propose a generalization of the forward-forward (FF) algorithm that we call the predictive forward-forward (PFF) algorithm. Specifically, we design a dynamic, recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit, combining elements of predictive coding, an emerging and viable neurobiological process theory of cortical function, with the forward-forward adaptation scheme. Furthermore, PFF efficiently learns to propagate learning signals and updates synapses with forward passes only, eliminating some of the key structural and computational constraints imposed by a backprop-based scheme. Besides computational advantages, the PFF process could be further useful for understanding the learning mechanisms behind biological neurons that make use of local (and global) signals despite missing feedback connections. We run several experiments on image data and demonstrate that the PFF procedure works as well as backprop, offering a promising brain-inspired algorithm for classifying, reconstructing, and synthesizing data patterns. As a result, our approach presents further evidence of the promise afforded by backprop-alternative credit assignment algorithms within the context of brain-inspired computing.  ( 2 min )
    Online Learning of Smooth Functions. (arXiv:2301.01434v1 [cs.LG])
    In this paper, we study the online learning of real-valued functions where the hidden function is known to have certain smoothness properties. Specifically, for $q \ge 1$, let $\mathcal F_q$ be the class of absolutely continuous functions $f: [0,1] \to \mathbb R$ such that $\|f'\|_q \le 1$. For $q \ge 1$ and $d \in \mathbb Z^+$, let $\mathcal F_{q,d}$ be the class of functions $f: [0,1]^d \to \mathbb R$ such that any function $g: [0,1] \to \mathbb R$ formed by fixing all but one parameter of $f$ is in $\mathcal F_q$. For any class of real-valued functions $\mathcal F$ and $p>0$, let $\text{opt}_p(\mathcal F)$ be the best upper bound on the sum of $p^{\text{th}}$ powers of absolute prediction errors that a learner can guarantee in the worst case. In the single-variable setup, we find new bounds for $\text{opt}_p(\mathcal F_q)$ that are sharp up to a constant factor. We show for all $\varepsilon \in (0, 1)$ that $\text{opt}_{1+\varepsilon}(\mathcal{F}_{\infty}) = \Theta(\varepsilon^{-\frac{1}{2}})$ and $\text{opt}_{1+\varepsilon}(\mathcal{F}_q) = \Theta(\varepsilon^{-\frac{1}{2}})$ for all $q \ge 2$. We also show for $\varepsilon \in (0,1)$ that $\text{opt}_2(\mathcal F_{1+\varepsilon})=\Theta(\varepsilon^{-1})$. In addition, we obtain new exact results by proving that $\text{opt}_p(\mathcal F_q)=1$ for $q \in (1,2)$ and $p \ge 2+\frac{1}{q-1}$. In the multi-variable setup, we establish inequalities relating $\text{opt}_p(\mathcal F_{q,d})$ to $\text{opt}_p(\mathcal F_q)$ and show that $\text{opt}_p(\mathcal F_{\infty,d})$ is infinite when $pd$. We also obtain sharp bounds on learning $\mathcal F_{\infty,d}$ for $p < d$ when the number of trials is bounded.  ( 2 min )
    DADAgger: Disagreement-Augmented Dataset Aggregation. (arXiv:2301.01348v1 [cs.LG])
    DAgger is an imitation algorithm that aggregates its original datasets by querying the expert on all samples encountered during training. In order to reduce the number of samples queried, we propose a modification to DAgger, known as DADAgger, which only queries the expert for state-action pairs that are out of distribution (OOD). OOD states are identified by measuring the variance of the action predictions of an ensemble of models on each state, which we simulate using dropout. Testing on the Car Racing and Half Cheetah environments achieves comparable performance to DAgger but with reduced expert queries, and better performance than a random sampling baseline. We also show that our algorithm may be used to build efficient, well-balanced training datasets by running with no initial data and only querying the expert to resolve uncertainty.  ( 2 min )
    An ensemble-based framework for mispronunciation detection of Arabic phonemes. (arXiv:2301.01378v1 [cs.SD])
    Determination of mispronunciations and ensuring feedback to users are maintained by computer-assisted language learning (CALL) systems. In this work, we introduce an ensemble model that defines the mispronunciation of Arabic phonemes and assists learning of Arabic, effectively. To the best of our knowledge, this is the very first attempt to determine the mispronunciations of Arabic phonemes employing ensemble learning techniques and conventional machine learning models, comprehensively. In order to observe the effect of feature extraction techniques, mel-frequency cepstrum coefficients (MFCC), and Mel spectrogram are blended with each learning algorithm. To show the success of proposed model, 29 letters in the Arabic phonemes, 8 of which are hafiz, are voiced by a total of 11 different person. The amount of data set has been enhanced employing the methods of adding noise, time shifting, time stretching, pitch shifting. Extensive experiment results demonstrate that the utilization of voting classifier as an ensemble algorithm with Mel spectrogram feature extraction technique exhibits remarkable classification result with 95.9% of accuracy.  ( 2 min )
    Task Weighting in Meta-learning with Trajectory Optimisation. (arXiv:2301.01400v1 [cs.LG])
    Developing meta-learning algorithms that are un-biased toward a subset of training tasks often requires hand-designed criteria to weight tasks, potentially resulting in sub-optimal solutions. In this paper, we introduce a new principled and fully-automated task-weighting algorithm for meta-learning methods. By considering the weights of tasks within the same mini-batch as an action, and the meta-parameter of interest as the system state, we cast the task-weighting meta-learning problem to a trajectory optimisation and employ the iterative linear quadratic regulator to determine the optimal action or weights of tasks. We theoretically show that the proposed algorithm converges to an $\epsilon_{0}$-stationary point, and empirically demonstrate that the proposed approach out-performs common hand-engineering weighting methods in two few-shot learning benchmarks.  ( 2 min )
    Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning. (arXiv:2301.01369v1 [eess.IV])
    Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface reconstruction. Due to dramatic structural and appearance changes associated with developmental and aging processes, existing brain tissue segmentation methods are only viable for specific age groups. Consequently, methods developed for one age group may fail for another. In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model. To overcome the challenges related to structural variability underpinned by biological processes, intensity inhomogeneity, motion artifacts, scanner-induced differences, and acquisition protocols, we propose to use contrastive learning to improve the quality of feature representations in a latent space for effective lifespan tissue segmentation. We compared our approach with commonly used segmentation methods on a large-scale dataset of 2,464 MR images. Experimental results show that our model accurately segments brain tissues across the lifespan and outperforms existing methods.  ( 2 min )
    Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations. (arXiv:2301.01371v1 [astro-ph.EP])
    The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep learning techniques such as neural networks have proved effective at differentiating promising astrophysical eclipsing candidates from other phenomena such as stellar variability and systematic instrumental effects in an efficient, unbiased and sustainable manner. This paper presents a high quality dataset containing light curves from the Primary Mission and 1st Extended Mission full frame images and periodic signals detected via Box Least Squares (Kov\'acs et al. 2002; Hartman 2012). The dataset was curated using a thorough manual review process then used to train a neural network called Astronet-Triage-v2. On our test set, for transiting/eclipsing events we achieve a 99.6% recall (true positives over all data with positive labels) at a precision of 75.7% (true positives over all predicted positives). Since 90% of our training data is from the Primary Mission, we also test our ability to generalize on held-out 1st Extended Mission data. Here, we find an area under the precision-recall curve of 0.965, a 4% improvement over Astronet-Triage (Yu et al. 2019). On the TESS Object of Interest (TOI) Catalog through April 2022, a shortlist of planets and planet candidates, Astronet-Triage-v2 is able to recover 3577 out of 4140 TOIs, while Astronet-Triage only recovers 3349 targets at an equal level of precision. In other words, upgrading to Astronet-Triage-v2 helps save at least 200 planet candidates from being lost. The new model is currently used for planet candidate triage in the Quick-Look Pipeline (Huang et al. 2020a,b; Kunimoto et al. 2021).  ( 3 min )
    Recent Advances on Federated Learning: A Systematic Survey. (arXiv:2301.01299v1 [cs.LG])
    Federated learning has emerged as an effective paradigm to achieve privacy-preserving collaborative learning among different parties. Compared to traditional centralized learning that requires collecting data from each party, in federated learning, only the locally trained models or computed gradients are exchanged, without exposing any data information. As a result, it is able to protect privacy to some extent. In recent years, federated learning has become more and more prevalent and there have been many surveys for summarizing related methods in this hot research topic. However, most of them focus on a specific perspective or lack the latest research progress. In this paper, we provide a systematic survey on federated learning, aiming to review the recent advanced federated methods and applications from different aspects. Specifically, this paper includes four major contributions. First, we present a new taxonomy of federated learning in terms of the pipeline and challenges in federated scenarios. Second, we summarize federated learning methods into several categories and briefly introduce the state-of-the-art methods under these categories. Third, we overview some prevalent federated learning frameworks and introduce their features. Finally, some potential deficiencies of current methods and several future directions are discussed.  ( 2 min )
    oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation. (arXiv:2301.01333v1 [cs.LG])
    With the rapid development of deep learning models and hardware support for dense computing, the deep learning (DL) workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broad range of operations scattered across the models. Accelerating a few compute-intensive operations using the expert-tuned implementation of primitives does not fully exploit the performance potential of AI hardware. Various efforts are made to compile a full deep neural network (DNN) graph. One of the biggest challenges is to achieve end-to-end compilation by generating expert-level performance code for the dense compute-intensive operations and applying compilation optimization at the scope of DNN computation graph across multiple compute-intensive operations. We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high-performance code generation of the deep neural network graph. oneDNN Graph Compiler addresses unique optimization challenges in the deep learning domain, such as low-precision computation, aggressive fusion, optimization for static tensor shapes and memory layout, constant weight optimization, and memory buffer reuse. Experimental results demonstrate up to 2x performance gains over primitives-based optimization for performance-critical DNN computation graph patterns on Intel Xeon Scalable Processors.  ( 2 min )
    Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metric. (arXiv:2301.01315v1 [stat.ML])
    (Conditional) Generative Adversarial Networks (GANs) have found great success in recent years, due to their ability to approximate (conditional) distributions over extremely high dimensional spaces. However, they are highly unstable and computationally expensive to train, especially in the time series setting. Recently, it has been proposed the use of a key object in rough path theory, called the signature of a path, which is able to convert the min-max formulation given by the (conditional) GAN framework into a classical minimization problem. However, this method is extremely expensive in terms of memory cost, sometimes even becoming prohibitive. To overcome this, we propose the use of \textit{Conditional Neural Stochastic Differential Equations}, which have a constant memory cost as a function of depth, being more memory efficient than traditional deep learning architectures. We empirically test that this proposed model is more efficient than other classical approaches, both in terms of memory cost and computational time, and that it usually outperforms them in terms of performance.  ( 2 min )
    Towards Deployable RL -- What's Broken with RL Research and a Potential Fix. (arXiv:2301.01320v1 [cs.LG])
    Reinforcement learning (RL) has demonstrated great potential, but is currently full of overhyping and pipe dreams. We point to some difficulties with current research which we feel are endemic to the direction taken by the community. To us, the current direction is not likely to lead to "deployable" RL: RL that works in practice and can work in practical situations yet still is economically viable. We also propose a potential fix to some of the difficulties of the field.  ( 2 min )
    Contextual Conservative Q-Learning for Offline Reinforcement Learning. (arXiv:2301.01298v1 [cs.LG])
    Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of practical application. However, extrapolation error generated by distribution shift will still lead to the overestimation for those actions that transit to out-of-distribution(OOD) states, which degrades the reliability and robustness of the offline policy. In this paper, we propose Contextual Conservative Q-Learning(C-CQL) to learn a robustly reliable policy through the contextual information captured via an inverse dynamics model. With the supervision of the inverse dynamics model, it tends to learn a policy that generates stable transition at perturbed states, for the fact that pertuebed states are a common kind of OOD states. In this manner, we enable the learnt policy more likely to generate transition that destines to the empirical next state distributions of the offline dataset, i.e., robustly reliable transition. Besides, we theoretically reveal that C-CQL is the generalization of the Conservative Q-Learning(CQL) and aggressive State Deviation Correction(SDC). Finally, experimental results demonstrate the proposed C-CQL achieves the state-of-the-art performance in most environments of offline Mujoco suite and a noisy Mujoco setting.  ( 2 min )
    Decentralized Gradient Tracking with Local Steps. (arXiv:2301.01313v1 [math.OC])
    Gradient tracking (GT) is an algorithm designed for solving decentralized optimization problems over a network (such as training a machine learning model). A key feature of GT is a tracking mechanism that allows to overcome data heterogeneity between nodes. We develop a novel decentralized tracking mechanism, $K$-GT, that enables communication-efficient local updates in GT while inheriting the data-independence property of GT. We prove a convergence rate for $K$-GT on smooth non-convex functions and prove that it reduces the communication overhead asymptotically by a linear factor $K$, where $K$ denotes the number of local steps. We illustrate the robustness and effectiveness of this heterogeneity correction on convex and non-convex benchmark problems and on a non-convex neural network training task with the MNIST dataset.  ( 2 min )
    Operator theory, kernels, and Feedforward Neural Networks. (arXiv:2301.01327v1 [cs.LG])
    In this paper we show how specific families of positive definite kernels serve as powerful tools in analyses of iteration algorithms for multiple layer feedforward Neural Network models. Our focus is on particular kernels that adapt well to learning algorithms for data-sets/features which display intrinsic self-similarities at feedforward iterations of scaling.  ( 2 min )
  • Open

    GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. (arXiv:2211.01962v3 [cs.LG] UPDATED)
    We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable Markov decision process (POMDP), and predictive state representation (PSR) as special cases. Toward finding the minimum assumption that empowers sample efficient learning, we propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation in online interactive decision making. In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs and PSRs. Furthermore, in terms of algorithm design, we propose a generic posterior sampling algorithm, which can be implemented in both model-free and model-based fashion, under both fully observable and partially observable settings. The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.  ( 3 min )
    Best Arm Identification with Contextual Information under a Small Gap. (arXiv:2209.07330v4 [cs.LG] UPDATED)
    We study the best-arm identification (BAI) problem with a fixed budget and contextual (covariate) information. In each round of an adaptive experiment, after observing contextual information, we choose a treatment arm using past observations and current context. Our goal is to identify the best treatment arm, which is a treatment arm with the maximal expected reward marginalized over the contextual distribution, with a minimal probability of misidentification. In this study, we consider a class of nonparametric bandit models that converge to location-shift models when the gaps go to zero. First, we derive lower bounds of the misidentification probability for a certain class of strategies and bandit models (probabilistic models of potential outcomes) under a small-gap regime. A small-gap regime is a situation where gaps of the expected rewards between the best and suboptimal treatment arms go to zero, which corresponds to one of the worst cases in identifying the best treatment arm. We then develop the ``Random Sampling (RS)-Augmented Inverse Probability weighting (AIPW) strategy,'' which is asymptotically optimal in the sense that the probability of misidentification under the strategy matches the lower bound when the budget goes to infinity in the small-gap regime. The RS-AIPW strategy consists of the RS rule tracking a target sample allocation ratio and the recommendation rule using the AIPW estimator.  ( 2 min )
    Constrained regret minimization for multi-criterion multi-armed bandits. (arXiv:2006.09649v2 [cs.LG] UPDATED)
    We consider a stochastic multi-armed bandit setting and study the problem of constrained regret minimization over a given time horizon. Each arm is associated with an unknown, possibly multi-dimensional distribution, and the merit of an arm is determined by several, possibly conflicting attributes. The aim is to optimize a 'primary' attribute subject to user-provided constraints on other 'secondary' attributes. We assume that the attributes can be estimated using samples from the arms' distributions, and that the estimators enjoy suitable concentration properties. We propose an algorithm called Con-LCB that guarantees a logarithmic regret, i.e., the average number of plays of all non-optimal arms is at most logarithmic in the horizon. The algorithm also outputs a Boolean flag that correctly identifies, with high probability, whether the given instance is feasible/infeasible with respect to the constraints. We also show that Con-LCB is optimal within a universal constant, i.e., that more sophisticated algorithms cannot do much better universally. Finally, we establish a fundamental trade-off between regret minimization and feasibility identification. Our framework finds natural applications, for instance, in financial portfolio optimization, where risk constrained maximization of expected return is meaningful.  ( 2 min )
    The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. (arXiv:2210.05021v2 [cs.LG] UPDATED)
    Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (artificial) data from the same distribution. However, this traditional viewpoint does not explain the success of prevalent augmentations in modern machine learning (e.g. randomized masking, cutout, mixup), that greatly alter the training data distribution. In this work, we develop a new theoretical framework to characterize the impact of a general class of DA on underparameterized and overparameterized linear model generalization. Our framework reveals that DA induces implicit spectral regularization through a combination of two distinct effects: a) manipulating the relative proportion of eigenvalues of the data covariance matrix in a training-data-dependent manner, and b) uniformly boosting the entire spectrum of the data covariance matrix through ridge regression. These effects, when applied to popular augmentations, give rise to a wide variety of phenomena, including discrepancies in generalization between over-parameterized and under-parameterized regimes and differences between regression and classification tasks. Our framework highlights the nuanced and sometimes surprising impacts of DA on generalization, and serves as a testbed for novel augmentation design.  ( 2 min )
    Time-uniform central limit theory, asymptotic confidence sequences, and anytime-valid causal inference. (arXiv:2103.06476v6 [math.ST] UPDATED)
    Confidence intervals based on the central limit theorem (CLT) are a cornerstone of classical statistics. Despite being only asymptotically valid, they are ubiquitous because they permit statistical inference under very weak assumptions, and can often be applied to problems even when nonasymptotic inference is impossible. This paper introduces time-uniform analogues of such asymptotic confidence intervals. To elaborate, our methods take the form of confidence sequences (CS) -- sequences of confidence intervals that are uniformly valid over time. CSs provide valid inference at arbitrary stopping times, incurring no penalties for "peeking" at the data, unlike classical confidence intervals which require the sample size to be fixed in advance. Existing CSs in the literature are nonasymptotic, and hence do not enjoy the aforementioned broad applicability of asymptotic confidence intervals. Our work bridges the gap by giving a definition for "asymptotic CSs", and deriving a universal asymptotic CS that requires only weak CLT-like assumptions. While the CLT approximates the distribution of a sample average by that of a Gaussian at a fixed sample size, we use strong invariance principles (stemming from the seminal 1970s work of Komlos, Major, and Tusnady) to uniformly approximate the entire sample average process by an implicit Gaussian process. We demonstrate their utility by deriving nonparametric asymptotic CSs for the average treatment effect based on doubly robust estimators in observational studies, for which no nonasymptotic methods can exist even in the fixed-time regime (due to confounding bias). These enable doubly robust causal inference that can be continuously monitored and adaptively stopped.  ( 3 min )
    Approximate blocked Gibbs sampling for Bayesian neural networks. (arXiv:2208.11389v2 [stat.ML] UPDATED)
    In this work, minibatch MCMC sampling for feedforward neural networks is made more feasible. To this end, it is proposed to sample subgroups of parameters via a blocked Gibbs sampling scheme. By partitioning the parameter space, sampling is possible irrespective of layer width. It is also possible to alleviate vanishing acceptance rates for increasing depth by reducing the proposal variance in deeper layers. Increasing the length of a non-convergent chain increases the predictive accuracy in classification tasks, so avoiding vanishing acceptance rates and consequently enabling longer chain runs have practical benefits. Moreover, non-convergent chain realizations aid in the quantification of predictive uncertainty. An open problem is how to perform minibatch MCMC sampling for feedforward neural networks in the presence of augmented data.  ( 2 min )
    Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow. (arXiv:2301.01766v1 [math.ST])
    Gaussian mixture models form a flexible and expressive parametric family of distributions that has found applications in a wide variety of applications. Unfortunately, fitting these models to data is a notoriously hard problem from a computational perspective. Currently, only moment-based methods enjoy theoretical guarantees while likelihood-based methods are dominated by heuristics such as Expectation-Maximization that are known to fail in simple examples. In this work, we propose a new algorithm to compute the nonparametric maximum likelihood estimator (NPMLE) in a Gaussian mixture model. Our method is based on gradient descent over the space of probability measures equipped with the Wasserstein-Fisher-Rao geometry for which we establish convergence guarantees. In practice, it can be approximated using an interacting particle system where the weight and location of particles are updated alternately. We conduct extensive numerical experiments to confirm the effectiveness of the proposed algorithm compared not only to classical benchmarks but also to similar gradient descent algorithms with respect to simpler geometries. In particular, these simulations illustrate the benefit of updating both weight and location of the interacting particles.  ( 2 min )
    CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis. (arXiv:2301.01642v1 [stat.ML])
    There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which,in turn, also motivates an urgent need for psychiatrists to fully understand the decision behavior of the used GNNs. However, most of the existing GNN explainers are either post-hoc in which another interpretive model needs to be created to explain a well-trained GNN, or do not consider the causal relationship between the extracted explanation and the decision, such that the explanation itself contains spurious correlations and suffers from weak faithfulness. In this work, we propose a granger causality-inspired graph neural network (CI-GNN), a built-in interpretable model that is able to identify the most influential subgraph (i.e., functional connectivity within brain regions) that is causally related to the decision (e.g., major depressive disorder patients or healthy controls), without the training of an auxillary interpretive network. CI-GNN learns disentangled subgraph-level representations {\alpha} and \b{eta} that encode, respectively, the causal and noncausal aspects of original graph under a graph variational autoencoder framework, regularized by a conditional mutual information (CMI) constraint. We theoretically justify the validity of the CMI regulation in capturing the causal relationship. We also empirically evaluate the performance of CI-GNN against three baseline GNNs and four state-of-the-art GNN explainers on synthetic data and two large-scale brain disease datasets. We observe that CI-GNN achieves the best performance in a wide range of metrics and provides more reliable and concise explanations which have clinical evidence.  ( 2 min )
    Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks. (arXiv:2301.01440v1 [math.OC])
    Volt/VAR control rules facilitate the autonomous operation of distributed energy resources (DER) to regulate voltage in power distribution grids. According to non-incremental control rules, such as the one mandated by the IEEE Standard 1547, the reactive power setpoint of each DER is computed as a piecewise-linear curve of the local voltage. However, the slopes of such curves are upper-bounded to ensure stability. On the other hand, incremental rules add a memory term into the setpoint update, rendering them universally stable. They can thus attain enhanced steady-state voltage profiles. Optimal rule design (ORD) for incremental rules can be formulated as a bilevel program. We put forth a scalable solution by reformulating ORD as training a deep neural network (DNN). This DNN emulates the Volt/VAR dynamics for incremental rules derived as iterations of proximal gradient descent (PGD). Analytical findings and numerical tests corroborate that the proposed ORD solution can be neatly adapted to single/multi-phase feeders.  ( 2 min )
    Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behavior. (arXiv:2301.01476v1 [stat.AP])
    Deep learning methods have gained popularity in recent years through the media and the relative ease of implementation through open source packages such as Keras. We investigate the applicability of popular recurrent neural networks in forecasting call center volumes at a large financial services company. These series are highly complex with seasonal patterns - between hours of the day, day of the week, and time of the year - in addition to autocorrelation between individual observations. Though we investigate the financial services industry, the recommendations for modeling cyclical nonlinear behavior generalize across all sectors. We explore the optimization of parameter settings and convergence criteria for Elman (simple), Long Short-Term Memory (LTSM), and Gated Recurrent Unit (GRU) RNNs from a practical point of view. A designed experiment using actual call center data across many different "skills" (income call streams) compares performance measured by validation error rates of the best observed RNN configurations against other modern and classical forecasting techniques. We summarize the utility of and considerations required for using deep learning methods in forecasting.  ( 2 min )
    Large-width asymptotics for ReLU neural networks with $\alpha$-Stable initializations. (arXiv:2206.08065v3 [cs.LG] UPDATED)
    There is a recent and growing literature on large-width asymptotic properties of Gaussian neural networks (NNs), namely NNs whose weights are initialized as Gaussian distributions. Two popular problems are: i) the study of the large-width distributions of NNs, which characterizes the infinitely wide limit of a rescaled NN in terms of a Gaussian stochastic process; ii) the study of the large-width training dynamics of NNs, which characterizes the infinitely wide dynamics in terms of a deterministic kernel, referred to as the neural tangent kernel (NTK), and shows that, for a sufficiently large width, the gradient descent achieves zero training error at a linear rate. In this paper, we consider these problems for $\alpha$-Stable NNs, namely NNs whose weights are initialized as $\alpha$-Stable distributions with $\alpha\in(0,2]$. First, for $\alpha$-Stable NNs with a ReLU activation function, we show that if the NN's width goes to infinity then a rescaled NN converges weakly to an $\alpha$-Stable stochastic process. As a difference with respect to the Gaussian setting, our result shows that the choice of the activation function affects the scaling of the NN, that is: to achieve the infinitely wide $\alpha$-Stable process, the ReLU activation requires an additional logarithmic term in the scaling with respect to sub-linear activations. Then, we study the large-width training dynamics of $\alpha$-Stable ReLU-NNs, characterizing the infinitely wide dynamics in terms of a random kernel, referred to as the $\alpha$-Stable NTK, and showing that, for a sufficiently large width, the gradient descent achieves zero training error at a linear rate. The randomness of the $\alpha$-Stable NTK is a further difference with respect to the Gaussian setting, that is: within the $\alpha$-Stable setting, the randomness of the NN at initialization does not vanish in the large-width regime of the training.  ( 3 min )
    Counterfactual Explanations for Land Cover Mapping in a Multi-class Setting. (arXiv:2301.01520v1 [cs.LG])
    Counterfactual explanations are an emerging tool to enhance interpretability of deep learning models. Given a sample, these methods seek to find and display to the user similar samples across the decision boundary. In this paper, we propose a generative adversarial counterfactual approach for satellite image time series in a multi-class setting for the land cover classification task. One of the distinctive features of the proposed approach is the lack of prior assumption on the targeted class for a given counterfactual explanation. This inherent flexibility allows for the discovery of interesting information on the relationship between land cover classes. The other feature consists of encouraging the counterfactual to differ from the original sample only in a small and compact temporal segment. These time-contiguous perturbations allow for a much sparser and, thus, interpretable solution. Furthermore, plausibility/realism of the generated counterfactual explanations is enforced via the proposed adversarial learning strategy.  ( 2 min )
    A General Framework for Learning Mean-Field Games. (arXiv:2003.06069v3 [cs.LG] UPDATED)
    This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population. It first establishes the existence of a unique Nash Equilibrium to this GMFG, and demonstrates that naively combining reinforcement learning with the fixed-point approach in classical MFGs yields unstable algorithms. It then proposes value-based and policy-based reinforcement learning algorithms (GMF-V and GMF-P, respectively) with smoothed policies, with analysis of their convergence properties and computational complexities. Experiments on an equilibrium product pricing problem demonstrate that GMF-V-Q and GMF-P-TRPO, two specific instantiations of GMF-V and GMF-P, respectively, with Q-learning and TRPO, are both efficient and robust in the GMFG setting. Moreover, their performance is superior in convergence speed, accuracy, and stability when compared with existing algorithms for multi-agent reinforcement learning in the $N$-player setting.  ( 2 min )
    Federated Learning for Data Streams. (arXiv:2301.01542v1 [cs.LG])
    Federated learning (FL) is an effective solution to train machine learning models on the increasing amount of data generated by IoT devices and smartphones while keeping such data localized. Most previous work on federated learning assumes that clients operate on static datasets collected before training starts. This approach may be inefficient because 1) it ignores new samples clients collect during training, and 2) it may require a potentially long preparatory phase for clients to collect enough data. Moreover, learning on static datasets may be simply impossible in scenarios with small aggregate storage across devices. It is, therefore, necessary to design federated algorithms able to learn from data streams. In this work, we formulate and study the problem of \emph{federated learning for data streams}. We propose a general FL algorithm to learn from data streams through an opportune weighted empirical risk minimization. Our theoretical analysis provides insights to configure such an algorithm, and we evaluate its performance on a wide range of machine learning tasks.  ( 2 min )
    Matrices with Gaussian noise: optimal estimates for singular subspace perturbation. (arXiv:1803.00679v2 [stat.ML] UPDATED)
    The Davis--Kahan--Wedin $\sin \Theta$ theorem describes how the singular subspaces of a matrix change when subjected to a small perturbation. This classic result is sharp in the worst case scenario. In this paper, we prove a stochastic version of the Davis--Kahan--Wedin $\sin \Theta$ theorem when the perturbation is a Gaussian random matrix. Under certain structural assumptions, we obtain an optimal bound that significantly improves upon the classic Davis--Kahan--Wedin $\sin \Theta$ theorem. One of our key tools is a new perturbation bound for the singular values, which may be of independent interest.  ( 2 min )
    First-order penalty methods for bilevel optimization. (arXiv:2301.01716v1 [math.OC])
    In this paper we study a class of unconstrained and constrained bilevel optimization problems in which the lower-level part is a convex optimization problem, while the upper-level part is possibly a nonconvex optimization problem. In particular, we propose penalty methods for solving them, whose subproblems turn out to be a structured minimax problem and are suitably solved by a first-order method developed in this paper. Under some suitable assumptions, an \emph{operation complexity} of ${\cal O}(\varepsilon^{-4}\log\varepsilon^{-1})$ and ${\cal O}(\varepsilon^{-7}\log\varepsilon^{-1})$, measured by their fundamental operations, is established for the proposed penalty methods for finding an $\varepsilon$-KKT solution of the unconstrained and constrained bilevel optimization problems, respectively. To the best of our knowledge, the methodology and results in this paper are new.  ( 2 min )
    Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks. (arXiv:2006.07356v3 [stat.ML] UPDATED)
    We investigate gradient descent training of wide neural networks and the corresponding implicit bias in function space. For univariate regression, we show that the solution of training a width-$n$ shallow ReLU network is within $n^{- 1/2}$ of the function which fits the training data and whose difference from the initial function has the smallest 2-norm of the second derivative weighted by a curvature penalty that depends on the probability distribution that is used to initialize the network parameters. We compute the curvature penalty function explicitly for various common initialization procedures. For instance, asymmetric initialization with a uniform distribution yields a constant curvature penalty, and thence the solution function is the natural cubic spline interpolation of the training data. \hj{For stochastic gradient descent we obtain the same implicit bias result.} We obtain a similar result for different activation functions. For multivariate regression we show an analogous result, whereby the second derivative is replaced by the Radon transform of a fractional Laplacian. For initialization schemes that yield a constant penalty function, the solutions are polyharmonic splines. Moreover, we show that the training trajectories are captured by trajectories of smoothing splines with decreasing regularization strength.  ( 2 min )
    Kernel Subspace and Feature Extraction. (arXiv:2301.01410v1 [cs.LG])
    We study kernel methods in machine learning from the perspective of feature subspace. We establish a one-to-one correspondence between feature subspaces and kernels and propose an information-theoretic measure for kernels. In particular, we construct a kernel from Hirschfeld--Gebelein--R\'{e}nyi maximal correlation functions, coined the maximal correlation kernel, and demonstrate its information-theoretic optimality. We use the support vector machine (SVM) as an example to illustrate a connection between kernel methods and feature extraction approaches. We show that the kernel SVM on maximal correlation kernel achieves minimum prediction error. Finally, we interpret the Fisher kernel as a special maximal correlation kernel and establish its optimality.  ( 2 min )
    Geometric Ergodicity in Modified Variations of Riemannian Manifold and Lagrangian Monte Carlo. (arXiv:2301.01409v1 [stat.ME])
    Riemannian manifold Hamiltonian (RMHMC) and Lagrangian Monte Carlo (LMC) have emerged as powerful methods of Bayesian inference. Unlike Euclidean Hamiltonian Monte Carlo (EHMC) and the Metropolis-adjusted Langevin algorithm (MALA), the geometric ergodicity of these Riemannian algorithms has not been extensively studied. On the other hand, the manifold Metropolis-adjusted Langevin algorithm (MMALA) has recently been shown to exhibit geometric ergodicity under certain conditions. This work investigates the mixture of the LMC and RMHMC transition kernels with MMALA in order to equip the resulting method with an "inherited" geometric ergodicity theory. We motivate this mixture kernel based on an analogy between single-step HMC and MALA. We then proceed to evaluate the original and modified transition kernels on several benchmark Bayesian inference tasks.  ( 2 min )
    Online Learning of Smooth Functions. (arXiv:2301.01434v1 [cs.LG])
    In this paper, we study the online learning of real-valued functions where the hidden function is known to have certain smoothness properties. Specifically, for $q \ge 1$, let $\mathcal F_q$ be the class of absolutely continuous functions $f: [0,1] \to \mathbb R$ such that $\|f'\|_q \le 1$. For $q \ge 1$ and $d \in \mathbb Z^+$, let $\mathcal F_{q,d}$ be the class of functions $f: [0,1]^d \to \mathbb R$ such that any function $g: [0,1] \to \mathbb R$ formed by fixing all but one parameter of $f$ is in $\mathcal F_q$. For any class of real-valued functions $\mathcal F$ and $p>0$, let $\text{opt}_p(\mathcal F)$ be the best upper bound on the sum of $p^{\text{th}}$ powers of absolute prediction errors that a learner can guarantee in the worst case. In the single-variable setup, we find new bounds for $\text{opt}_p(\mathcal F_q)$ that are sharp up to a constant factor. We show for all $\varepsilon \in (0, 1)$ that $\text{opt}_{1+\varepsilon}(\mathcal{F}_{\infty}) = \Theta(\varepsilon^{-\frac{1}{2}})$ and $\text{opt}_{1+\varepsilon}(\mathcal{F}_q) = \Theta(\varepsilon^{-\frac{1}{2}})$ for all $q \ge 2$. We also show for $\varepsilon \in (0,1)$ that $\text{opt}_2(\mathcal F_{1+\varepsilon})=\Theta(\varepsilon^{-1})$. In addition, we obtain new exact results by proving that $\text{opt}_p(\mathcal F_q)=1$ for $q \in (1,2)$ and $p \ge 2+\frac{1}{q-1}$. In the multi-variable setup, we establish inequalities relating $\text{opt}_p(\mathcal F_{q,d})$ to $\text{opt}_p(\mathcal F_q)$ and show that $\text{opt}_p(\mathcal F_{\infty,d})$ is infinite when $pd$. We also obtain sharp bounds on learning $\mathcal F_{\infty,d}$ for $p < d$ when the number of trials is bounded.  ( 2 min )
    Testing High-dimensional Multinomials with Applications to Text Analysis. (arXiv:2301.01381v1 [stat.ME])
    Motivated by applications in text mining and discrete distribution inference, we investigate the testing for equality of probability mass functions of $K$ groups of high-dimensional multinomial distributions. A test statistic, which is shown to have an asymptotic standard normal distribution under the null, is proposed. The optimal detection boundary is established, and the proposed test is shown to achieve this optimal detection boundary across the entire parameter space of interest. The proposed method is demonstrated in simulation studies and applied to analyze two real-world datasets to examine variation among consumer reviews of Amazon movies and diversity of statistical paper abstracts.  ( 2 min )
    Covariate-guided Bayesian mixture model for multivariate time series. (arXiv:2301.01373v1 [stat.ME])
    With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate time series and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this paper, we propose a group-based method to cluster a collection of multivariate time series via a Bayesian mixture of smoothing splines. Our method assumes each multivariate time series is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy (fNIRS), which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.  ( 2 min )
    Measuring tail risk at high-frequency: An $L_1$-regularized extreme value regression approach with unit-root predictors. (arXiv:2301.01362v1 [econ.EM])
    We study tail risk dynamics in high-frequency financial markets and their connection with trading activity and market uncertainty. We introduce a dynamic extreme value regression model accommodating both stationary and local unit-root predictors to appropriately capture the time-varying behaviour of the distribution of high-frequency extreme losses. To characterize trading activity and market uncertainty, we consider several volatility and liquidity predictors, and propose a two-step adaptive $L_1$-regularized maximum likelihood estimator to select the most appropriate ones. We establish the oracle property of the proposed estimator for selecting both stationary and local unit-root predictors, and show its good finite sample properties in an extensive simulation study. Studying the high-frequency extreme losses of nine large liquid U.S. stocks using 42 liquidity and volatility predictors, we find the severity of extreme losses to be well predicted by low levels of price impact in period of high volatility of liquidity and volatility.  ( 2 min )
    Towards Deployable RL -- What's Broken with RL Research and a Potential Fix. (arXiv:2301.01320v1 [cs.LG])
    Reinforcement learning (RL) has demonstrated great potential, but is currently full of overhyping and pipe dreams. We point to some difficulties with current research which we feel are endemic to the direction taken by the community. To us, the current direction is not likely to lead to "deployable" RL: RL that works in practice and can work in practical situations yet still is economically viable. We also propose a potential fix to some of the difficulties of the field.  ( 2 min )
    Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metric. (arXiv:2301.01315v1 [stat.ML])
    (Conditional) Generative Adversarial Networks (GANs) have found great success in recent years, due to their ability to approximate (conditional) distributions over extremely high dimensional spaces. However, they are highly unstable and computationally expensive to train, especially in the time series setting. Recently, it has been proposed the use of a key object in rough path theory, called the signature of a path, which is able to convert the min-max formulation given by the (conditional) GAN framework into a classical minimization problem. However, this method is extremely expensive in terms of memory cost, sometimes even becoming prohibitive. To overcome this, we propose the use of \textit{Conditional Neural Stochastic Differential Equations}, which have a constant memory cost as a function of depth, being more memory efficient than traditional deep learning architectures. We empirically test that this proposed model is more efficient than other classical approaches, both in terms of memory cost and computational time, and that it usually outperforms them in terms of performance.  ( 2 min )

  • Open

    Will A.I generated shit like art and text be undetectable some day? Will there always be some other A.I that can detect that shit?
    submitted by /u/WaggleMcDaggle [link] [comments]  ( 50 min )
    ai to write quotes for me
    Hi everyone So i write a lot of quotes to do with various building jobs on ms word and I'm wondering if there's an ai that i can get to read all the quotes I've ever written and then get to write quotes for me based on a few promts. eg if i say replace a bathroom with a shower and bath in it and it will write the quote or the brunt of it for me. submitted by /u/pummers88 [link] [comments]  ( 52 min )
    Have AI generate Git commit messages for you
    submitted by /u/abisknees [link] [comments]  ( 51 min )
    ChatGPT and the unbundling of online search
    submitted by /u/bendee983 [link] [comments]  ( 60 min )
    WSJ News Exclusive | ChatGPT Creator OpenAI Is in Talks for Tender Offer That Would Value It at $29 Billion
    submitted by /u/Plopfish [link] [comments]  ( 50 min )
    The AI newsletter that covers the coolest things happening in AI in simple-to-understand bites. Each issue covers: 3 cool use cases or tools, 2 big stories, and 1 funny meme or tweet about AI.
    submitted by /u/Folly237 [link] [comments]  ( 80 min )
    Text-to-law: Are large language models good lobbyists?
    submitted by /u/Peaking_AI [link] [comments]  ( 50 min )
    does anyone know any good ai to detect chords or notes when loading a song??
    submitted by /u/Pollo3652 [link] [comments]  ( 51 min )
    Baidu Research Releases Top 10 Tech Trends for 2023
    ​ https://preview.redd.it/oca9tqrfq9aa1.png?width=1920&format=png&auto=webp&s=8194a46975518ee82e5ecbd4654cbb2b69739545 Baidu Research today released its predictions for the top 10 technology trends of 2023, including big models, digital-real convergence, virtual-real symbiosis, autonomous driving, robotics, scientific computing, quantum computing, privacy computing, ethics in technology, and sustainability. For more: http://research.baidu.com/Blog/index-view?id=178 submitted by /u/trcytony [link] [comments]  ( 50 min )
    I solo developed an avatar app. It's faster and cheaper. Looking for feedback!
    submitted by /u/okaris [link] [comments]  ( 50 min )
    Why are coders/programmers persuaded that they won't be replaced by Al?
    They are like other jobs. If Al can master emotional/artistic jobs, medicine, law etc why not something like coding? Personally I believe that 95% of each field will loose their jobs and the 5% that will remain will have more a supervisory role. P submitted by /u/Ok_Dragonfly_2167 [link] [comments]  ( 63 min )
    Adoption of AI in Medical Imaging Diagnosis
    The adoption of artificial intelligence (AI) in medical imaging has the potential to transform the way we diagnose and treat various health conditions. From X-rays to MRI scans, these techniques allow doctors to see inside the human body and identify problems that might not be visible to the naked eye. However, the adoption of AI in the medical field is not without challenges. A recent study has explored the determinants for the adoption of AI-powered decision-making support systems in the medical imaging workflow. The study used data from clinicians who participated in an international evaluation of healthcare practitioners and applied confirmatory factor analysis and structural equation modeling to understand the factors that influence the adoption and usage of AI in medical imaging. The results of the study showed that understanding the role of security, risk, and trust is crucial for the intended usage of AI in the medical imaging workflow. These findings provide valuable insights for researchers and practitioners looking to understand the adoption and acceptance of AI in the medical field. As AI continues to make advances in the field of medical imaging, it is important to consider the role of these factors in the adoption and acceptance of AI by healthcare practitioners. By understanding and addressing these challenges, we can ensure that the adoption of AI in medical imaging is seamless and effective. In your opinion, what are the biggest challenges to the widespread adoption of AI in the medical imaging workflow? submitted by /u/FMCalisto [link] [comments]  ( 53 min )
    Did anyone have this with Meitu AI ? It only puts blood on my photos
    submitted by /u/LastTranslator8157 [link] [comments]  ( 50 min )
    Death of the narrator? Apple unveils suite of AI-voiced audiobooks
    Apple quietly launched a catalogue of books narrated by AI in a move that may mark the beginning of the end for human narrators. The strategy marks an attempt to upend the lucrative and fast-growing audiobook market - but it also promises to intensify scrutiny over allegations of Apple's anti-competitive behaviour. Apple's development of AI to narrate books could represent a significant shift in how major technology companies see the future of audiobooks. In recent months, Apple approached independent publishers as potential partners, including some in the Canadian market, but not all agreed to participate Apple would shoulder the costs of production and writers would receive royalties from sales. The Future While there is potential for backlash by professional voice actors, authors themselves are increasingly being asked to narrate their own books There is a financial incentive for the writers, both in the upfront payments and the expanded availability of their work But producing an audiobook with a human voice can take weeks and can cost publishers thousands of dollars Apple and Amazon For years, Apple has sold books and audiobooks through its Books app Apple was rumored to be interested in developing its own audiobook service and shifting from a reseller to a producer The move represents a direct shot at rival Amazon, with Apple listing what it said were the benefits of its own system compared to Kindle's Direct Publishing Lawmakers in Europe and the United States have put in place increasing scrutiny of the company in the wake of allegations that Apple limits competition ​ This is from the AI With Vibes Newsletter, read the full issue here: https://aiwithvibes.beehiiv.com/p/new-world-goodbye-homework-elon-loves-ai-essays submitted by /u/Mk_Makanaki [link] [comments]  ( 56 min )
    Meet GPTZero: The AI-Powered Anti-Plagiarism Program
    submitted by /u/liquidocelotYT [link] [comments]  ( 51 min )
    Considering going into the ai field as a career, what is everyone's thoughts on the moral implications of a career?
    submitted by /u/tartigrade78 [link] [comments]  ( 58 min )
    How are those videos made ?
    Hi, I was simply wondering how that kind of videos are made. I guess there is a video "reference" layer (camera movement and some simple shapes ?), then an AI pass that applies some prompts on it ? https://www.youtube.com/watch?v=yeYUirOYiE4 https://twitter.com/ArtificialBob/status/1579404863825653763 ​ Could someone link a tutorial so I could understand the process ? Thanks ! submitted by /u/grosbouff [link] [comments]  ( 51 min )
    Top Trends in A.I. in 2023
    submitted by /u/BackgroundResult [link] [comments]  ( 24 min )
    Ai generared guide to riches
    Sure! Here is a more detailed version of the script adapted for a Reddit post: Hello Redditors! I wanted to share some practical strategies that you can use to get rich. Whether you're just starting out on your wealth-building journey or you're looking for ways to take your financial situation to the next level, these strategies can help. Define your financial goals Before you get started, it's important to define what "getting rich" means to you. This might involve setting a specific financial goal, like saving a certain amount of money or achieving a certain level of income. It's important to have a clear idea of what you want to achieve so that you can stay motivated and focused as you work towards financial success. Create a plan Once you have your goals in mind, it's time to devel…  ( 56 min )
    Sullivan King & Subtronics - Take Flight AI Music Video 4K 60 FPS
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 40 min )
    AI generated Sam Harris meditation
    submitted by /u/justLV [link] [comments]  ( 50 min )
    Innocent Man Arrested In Another Facial Recognition Failure
    submitted by /u/vadhavaniyafaijan [link] [comments]  ( 24 min )
    Open Source AI Resources/Recommendations?
    Does anyone in the community have any recommended open source AI projects to get that are still available? I've heard ChatGPT has a few open source community driven competitors? Additionally, does anyone have any recommended open source AI image/video upres programs they use? I really want to try and preserve (and hopefully contribute) to what's out there currently. I know there has been a large movement to keep open source AI projects alive while these private groups gain momentum. Also, this goes out to the mods, but maybe we should have a pinned post with a list of community driven/open source AI that people can download, mess with, and hopefully contribute to? submitted by /u/OldsDiesel [link] [comments]  ( 41 min )
  • Open

    [D] Can common pooled RAM and VRAM increase devices' capabilities with regards to operating on larger models?
    A lot of interesting models that came out recently have been using the transformer architecture, or otherwise required a lot of VRAM, such amounts, in fact, that it has become unattainable to even just run inference for almost everyone, because the amount of necessary VRAM can only be attained by pooling together numerous >1k usd GPUs. But VRAM in itself isn't that crazy expensive, it's the the rest of that super powerful GPU that's attached to them that's expensive. If I understand correctly, some manufacturers, like, say, Apple, have been using common pool of memory for both RAM and VRAM, thereby having GPUs with a crazy amount of VRAM. For example, their least expensive Mac Studio has an amount of VRAM equivalent to an A100. So my question is, does a unified memory pool (common RAM and VRAM) lead to more capable inference? And if so, should/will manufacturers gravitate towards such a hardware solution? And are there any technical obstacles inhibiting such a path? submitted by /u/whyvitamins [link] [comments]  ( 62 min )
    [D] Special-purpose "neuromorphic" chips for AI - current state of the art?
    There are a number of companies out there making special-purpose chip "neuromorphic" architectures that are supposed to be better suited for neural networks. Some of them you can buy for as little as $500. Most of them are designed for Spiking Neural Networks, probably because of the similarity to the human brain. Innatera's chip implements the neural network on an analog computer, which I find very interesting. Is the performance really better than GPUs? Could this achieve the the dream of running a model on as little power as the brain uses? Are spiking neural networks useful for anything? I don't know of any tasks where a SNN is the current state-of-the-art in performance. All the good results right now seem to be coming out of transformers, but maybe that's just because they're so well-suited for the hardware we have available. submitted by /u/currentscurrents [link] [comments]  ( 62 min )
    [D] SOTA Head Pose Estimation Models
    Hey guys! Just wondering what the state of the art is for off the shelf head pose estimation. I’m looking to compute the pitch, yaw, and roll values for the heads in some videos I’m processing. I need something that will work off the shelf that’s a bit more reliable than using landmark based methods. Thank you! submitted by /u/TightestKnees [link] [comments]  ( 60 min )
    [D] Has any research been done on using GANs to develop proteins for selective binding?
    While long ago I hailed from the realm of biochemistry, I'm sitting more on the data side these days and can't help but be nostalgic for the possible use of generative neural nets on the bio side. Having a little curiosity, I found that this work has already began somewhat. For instance, this group managed to get a reasonable design schema by incorporating Attention into their GANs: https://www.nature.com/articles/s42256-021-00310-5 To me then, if we can develop "reasonable" (i.e. soluble among other properties) protein sequences using GANs, a natural extension would be to train them for selective binding. For instance, imagine adding the loss from a pre-trained Discriminator that predicts binding a certain target into the above. On one hand, that seems like a tall order, but given some of the research I've thumbed through, I believe such a discriminator is already shown as tenable. Here's a few papers: A meta review of the topic An implementation using Attention Another implementation Being able to serve up amino acid sequences with selective binding properties seems really really attractive and a natural "next step" for GAN research here. The closest I have seen though is this work which is more of the flavor of redesigning a sequence rather than a de novo approach. Anyways, I'm no researcher in the field and don't have a dog in the fight so to speak, but it's just an exciting thought to me. Curious if it's been done yet? And if not, just interested in sparking some discussion in case others have similar interests. submitted by /u/jshkk [link] [comments]  ( 61 min )
    [Project] Public MIT IAP talks on speech & language ML starting Monday
    https://iap.gridspace.com/ First week is on sound and DSP. Second week on NLP and language. Third on speech ML models. And fourth week on applications. There's a signup google form on the site. submitted by /u/arrowoftime [link] [comments]  ( 58 min )
    Image matching within database? [P]
    A friend and I are working on a project that requires us to take images as input find images that match them from our database. What is the most effective way to do this? We've tried SIFT and a few similar solutions, but nothing's been super effective so far. Does anyone have any suggestions? Are there any solid open-source solutions? submitted by /u/Clarkmilo [link] [comments]  ( 62 min )
    [News] AMD Instinct MI300 APU for AI and HPC announced
    https://www.anandtech.com/show/18721/ces-2023-amd-instinct-mi300-data-center-apu-silicon-in-hand-146b-transistors-shipping-h223 I wonder if this is the beginning of dissolution of NVIDIA's monopoly on AI. submitted by /u/samobon [link] [comments]  ( 60 min )
    using RL for liquidity provision on Uniswap V3 [R]
    Hi, has anyone done any work in using ML or RL to automate providing liquidity in uniswapv3? Rewards are based on volume against the TVL, you earn a percentage of each trade, so high volume in a low liquidity market can earn some very good rewards as long as the price is stable enough that it doesn't effect the APR.. ideally i'd like to assess trends in volume based on TA indicators and then assign a portion of funds to the pool based on some forecasted returns.. i'm a complete newbie but come from a data engineering background, so i'd like to figure out of there's any where i can look into starting from or if the is a completely green field subject for now. submitted by /u/adamnmcc [link] [comments]  ( 24 min )
    [R] Airlift Challenge Competition
    Announcing the Airlift Challenge Competition Website: https://airliftchallenge.com The Airlift Challenge seeks improved algorithms to plan and execute airlift operations in the face of dynamic disruptions. Participants compete by submitting OpenAI Gym-based agents that minimize delivery time and cost using reinforcement learning, optimization, heuristics, or any other technique. Overview Airlifts demand the delivery of large sets of cargo into areas of need under tight deadlines. Yet, there are many obstacles preventing timely delivery. Airports have limited capacity to process airplanes, thus limiting throughput and potentially creating bottlenecks. Weather disruptions can cause delays or force airplanes to re-route. Unexpected cargo may be staged for an urgent delivery. This competi…  ( 63 min )
    Combining pre-trained word embeddings [R]
    Which is the best possible method to combine the embeddings of two words. Example is "comparison" + "contrast" and "expansion" and "restatement". So I want the ML model to understand that contrast is more related to comparison and restatement is more related to expansion. My initial institution is vector addition of the embeddings of these words. I would like to hear you feedbacks on the same. Thanks in advance :-) submitted by /u/BothEntertainment786 [link] [comments]  ( 61 min )
    Is there another way to determine the effect of the features other than the inbuilt features importance and SHAP values? [Research] [Discussion]
    I am working on a classification churn model, I have used feature importance to show the features' importance at model fit. I used shap values to show permutation importance of each feature. But I feel it's harder to represent causation of the prediction to each feature. Any ideas ? submitted by /u/spb-world [link] [comments]  ( 59 min )
    Democratizing Index Tracking: A GNN-based Meta-Learning Method for Sparse Portfolio Optimization [R] [P]
    Have you ever wanted to invest in a US ETF or mutual fund, but found that many of the actively managed index trackers were expensive or out of reach due to regulations? I have recently developed a solution to this problem that allows small investors to create their sparse stock portfolios for tracking an index by proposing a novel population-based large-scale non-convex optimization method via a Deep Generative Model that learns to sample good portfolios. Sparse VGT Tracker - QuantConnect Backtest I've compared this approach to the state-of-the-art evolutionary strategy (Fast CMA-ES) and found that it is more efficient at finding optimal index-tracking portfolios. The PyTorch implementations of both methods and the dataset are available on my GitHub repository for reproducibility and further improvement. Check out the repository to learn more about this new meta-learning approach for evolutionary optimization, or run your small index fund at home! Best Index-Tracking Validation Loss Achieved on Out-of-Sample Period in 100 Epochs submitted by /u/k_yuksel [link] [comments]  ( 64 min )
    [D] Isolation Forest
    I've a isolation forest model in production environment that trains every time we try to find anomalies and it classifies different points as anomalies. What should I do such that only most likely anomaly points are provided as results and results don't differ much? I do not want to set random state. submitted by /u/TKMater [link] [comments]  ( 58 min )
    [P] Learning chatbot for MOS KIM-1 (1976, 6502 CPU, 1K RAM), run on KIM Uno
    Hi everyone! I programmed a learning chatbot for (a clone of) the MOS KIM-1, hailing from 1976 with its 6502 CPU and 1K RAM. Basically, it works this way - you give it a byte, and it answers with a byte; at the same time, it learns from each interaction, which byte "should" answer which, and updates its knowledge base accordingly. It actually runs on a KIM Uno, an Arduino based clone of the KIM-1. This is the GitHub page with the code, contained in two short programs: one (optional) to slighly pre-populate the knowledge base with about a dozen of bytes that would constitute a nucleus of original replies (to be evolved into "your" interactions, as you chat on), starting from $0100, as well as the actual chatbot program to be launched from $0200 (the "user input" byte is to be entered prior to run in $0010, and the reply will be contained after run at $0013, so yes, you are "chatting" in hex), in each case, both in assembler and already assembled (and ready to be entered into the KIM-1): https://github.com/KedalionDaimon/MOS-KIM-1-chatbot And this is my YouTube video, presenting it: https://www.youtube.com/watch?v=7MJgi5kua3M submitted by /u/NinoIvanov [link] [comments]  ( 63 min )
    [D] BERTopic and fuzzy clustering
    Hi everyone, I am having a chunk of textual conversational data which I need to analyze for topics in it. I am currently using BERTopic to do it but the issue with it is that it does hard clustering, i.e., each datapoint belongs to exactly one topic cluster. But many of the sentences are multi intent and do fall in many topics simultaneously. Can soft/fuzzy clustering , where each datapoint can belong to more than one topic clusters, be done via BERTopic? If yes, then how can it be implemented? If not, which other algorithms can be used? submitted by /u/Devinco001 [link] [comments]  ( 58 min )
  • Open

    AI Engineer: Learn About The Role And Skills Needed For Success In 2023
    Computer engineering has come a long way from merely being a curriculum major to a key credential for your AI portfolio. Artificial engineering is a role that commands huge skill and expertise in the realm of technology. Unsurprisingly, the demand for their services outstrips the supply. The industry is oozing with valuable companies with diversified… Read More »AI Engineer: Learn About The Role And Skills Needed For Success In 2023 The post AI Engineer: Learn About The Role And Skills Needed For Success In 2023 appeared first on Data Science Central.  ( 21 min )
    Seven steps to simple DIY market forecasting
    I became a true data hound after a stint as a market analyst in the semiconductor industry. The role involved these activities: The habits and mindset I developed as a data hound, forecaster, and spreadsheet jockey inform the way I track emerging data technology trends to this day. So I thought I’d share the main… Read More »Seven steps to simple DIY market forecasting The post Seven steps to simple DIY market forecasting appeared first on Data Science Central.  ( 21 min )
  • Open

    Can someone help me code (Python) Monte Carlo Tree Search to find the highest value leaf node. Don't need to be spoonfed. I just need a rough outline and someone to ask questions to. Thanks. Ref. Question 2
    submitted by /u/Kamal_Ata_Turk [link] [comments]  ( 55 min )
    Democratizing Index Tracking: A GNN-based Meta-Learning Method for Sparse Portfolio Optimization
    Have you ever wanted to invest in a US ETF or mutual fund, but found that many of the actively managed index trackers were expensive or out of reach due to regulations? I have recently developed a solution to this problem that allows small investors to create their sparse stock portfolios for tracking an index by proposing a novel population-based large-scale non-convex optimization method via a Deep Generative Model that learns to sample good portfolios. Sparse VGT Tracker - QuantConnect Backtest I've compared this approach to the state-of-the-art evolutionary strategy (Fast CMA-ES) and found that it is more efficient at finding optimal index-tracking portfolios. The PyTorch implementations of both methods and the dataset are available on my GitHub repository for reproducibility and further improvement. Check out the repository to learn more about this new meta-learning approach for evolutionary optimization, or run your small index fund at home! Best Index-Tracking Validation Loss Achieved on Out-of-Sample Period in 100 Epochs submitted by /u/k_yuksel [link] [comments]  ( 24 min )
    Reinforcement learning in ChatGPT
    Today, I read the paper about InstructGPT on which ChatGPT is based, and I was surprised to see that it uses reinforcement learning in the training process. It uses PPO to optimize its prompts on a reward signal given by another trained model. Though I found this approach really interesting, I was left wondering how prompts are created by the policy. I have never heard of PPO being used to generate text before, and I am very curious as to what this action-space looks like. Does anyone here have any insight on this? Also, it would be very interesting to hear what people here think RL advances can do for further research in generative models like this! submitted by /u/Embarrassed-Print-13 [link] [comments]  ( 57 min )
    How many steps are models usually trained before deployment?
    Since it depends on sample efficiency (which varies by method), the average method used to train the Model finally deployed is in consideration. View Poll submitted by /u/XecutionStyle [link] [comments]  ( 55 min )
  • Open

    Simulating discrimination in virtual reality
    The role-playing game “On the Plane” simulates xenophobia to foster greater understanding and reflection via virtual experiences.  ( 9 min )
  • Open

    Tipping Point: NVIDIA DRIVE Scales AI-Powered Transportation at CES 2023
    Autonomous vehicle (AV) technology is heading to the mainstream. The NVIDIA DRIVE ecosystem showcased significant milestones toward widespread intelligent transportation at CES. Growth is occurring in vehicle deployment plans as well as AI solutions integrating further into the car. Foxconn joined the NVIDIA DRIVE ecosystem. The world’s largest technology manufacturer will produce electronic control units Read article >  ( 6 min )
    GFN Thursday Brings RTX 4080 to the Cloud With GeForce NOW Ultimate Membership
    GFN Thursday rings in the new year with a recap of the biggest cloud gaming news from CES 2023: the GeForce NOW Ultimate membership. Powered by the latest NVIDIA GPU technology, Ultimate members can play their favorite PC games at performance never before available from the cloud. Plus, with a new year comes new games. Read article >  ( 7 min )
  • Open

    Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence. (arXiv:2301.01218v1 [cs.CR])
    Deep neural networks are vulnerable to adversarial attacks. In this paper, we take the role of investigators who want to trace the attack and identify the source, that is, the particular model which the adversarial examples are generated from. Techniques derived would aid forensic investigation of attack incidents and serve as deterrence to potential attacks. We consider the buyers-seller setting where a machine learning model is to be distributed to various buyers and each buyer receives a slightly different copy with same functionality. A malicious buyer generates adversarial examples from a particular copy $\mathcal{M}_i$ and uses them to attack other copies. From these adversarial examples, the investigator wants to identify the source $\mathcal{M}_i$. To address this problem, we propose a two-stage separate-and-trace framework. The model separation stage generates multiple copies of a model for a same classification task. This process injects unique characteristics into each copy so that adversarial examples generated have distinct and traceable features. We give a parallel structure which embeds a ``tracer'' in each copy, and a noise-sensitive training loss to achieve this goal. The tracing stage takes in adversarial examples and a few candidate models, and identifies the likely source. Based on the unique features induced by the noise-sensitive loss function, we could effectively trace the potential adversarial copy by considering the output logits from each tracer. Empirical results show that it is possible to trace the origin of the adversarial example and the mechanism can be applied to a wide range of architectures and datasets.
    StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph. (arXiv:2205.14209v2 [cs.CL] UPDATED)
    Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood. We propose a method named StarGraph, which gives a novel way to utilize the neighborhood information for large-scale knowledge graphs to obtain entity representations. An incomplete two-hop neighborhood subgraph for each target node is at first generated, then processed by a modified self-attention network to obtain the entity representation, which is used to replace the entity embedding in conventional methods. We achieved SOTA performance on ogbl-wikikg2 and got competitive results on fb15k-237. The experimental results proves that StarGraph is efficient in parameters, and the improvement made on ogbl-wikikg2 demonstrates its great effectiveness of representation learning on large-scale knowledge graphs. The code is now available at \url{https://github.com/hzli-ucas/StarGraph}.
    Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data. (arXiv:2102.11872v5 [cs.LG] UPDATED)
    In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier. Previous methods for such combined clustering and classification either 1) are classifier-specific and not generic, or 2) independently perform clustering and classifier training, which may not form clusters that can potentially benefit classifier performance. The question of how to perform clustering to improve the performance of classifiers trained on the clusters has received scant attention in previous literature, despite its importance in several real-world applications. In this paper, first, we theoretically analyze the generalization performance of classifiers trained on clustered data and find conditions under which clustering can potentially aid classification. This motivates the design of a simple k-means-based classification algorithm called Clustering Aware Classification (CAC) and its neural variant {DeepCAC}. DeepCAC effectively leverages deep representation learning to learn latent embeddings and finds clusters in a manner that make the clustered data suitable for training classifiers for each underlying subpopulation. Our experiments on synthetic and real benchmark datasets demonstrate the efficacy of DeepCAC over previous methods for combined clustering and classification.
    Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation. (arXiv:2208.10779v2 [cs.CV] UPDATED)
    Learning discriminative representations of unlabelled data is a challenging task. Contrastive self-supervised learning provides a framework to learn meaningful representations using learned notions of similarity measures from simple pretext tasks. In this work, we propose a simple and efficient framework for self-supervised image segmentation using contrastive learning on image patches, without using explicit pretext tasks or any further labeled fine-tuning. A fully convolutional neural network (FCNN) is trained in a self-supervised manner to discern features in the input images and obtain confidence maps which capture the network's belief about the objects belonging to the same class. Positive- and negative- patches are sampled based on the average entropy in the confidence maps for contrastive learning. Convergence is assumed when the information separation between the positive patches is small, and the positive-negative pairs is large. The proposed model only consists of a simple FCNN with 10.8k parameters and requires about 5 minutes to converge on the high resolution microscopy datasets, which is orders of magnitude smaller than the relevant self-supervised methods to attain similar performance. We evaluate the proposed method for the task of segmenting nuclei from two histopathology datasets, and show comparable performance with relevant self-supervised and supervised methods.
    Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples. (arXiv:2301.01217v1 [cs.CR])
    There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimization framework with a surrogate model to remove (minimize) errors from the original samples, and then applied to protect the data against unknown target models. However, existing UE generation methods all rely on an ideal assumption called label-consistency, where the hackers and protectors are assumed to hold the same label for a given sample. In this work, we propose and promote a more practical label-agnostic setting, where the hackers may exploit the protected data quite differently from the protectors. E.g., a m-class unlearnable dataset held by the protector may be exploited by the hacker as a n-class dataset. Existing UE generation methods are rendered ineffective in this challenging setting. To tackle this challenge, we present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations. Furthermore, we propose to leverage VisionandLanguage Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains. We empirically verify the effectiveness of our proposed approach under a variety of settings with different datasets, target models, and even commercial platforms Microsoft Azure and Baidu PaddlePaddle.
    Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds. (arXiv:2010.06097v5 [cs.LG] UPDATED)
    In the paper, we study a class of useful minimax problems on Riemanian manifolds and propose a class of effective Riemanian gradient-based methods to solve these minimax problems. Specifically, we propose an effective Riemannian gradient descent ascent (RGDA) algorithm for the deterministic minimax optimization. Moreover, we prove that our RGDA has a sample complexity of $O(\kappa^2\epsilon^{-2})$ for finding an $\epsilon$-stationary solution of the Geodesically-Nonconvex Strongly-Concave (GNSC) minimax problems, where $\kappa$ denotes the condition number. At the same time, we present an effective Riemannian stochastic gradient descent ascent (RSGDA) algorithm for the stochastic minimax optimization, which has a sample complexity of $O(\kappa^4\epsilon^{-4})$ for finding an $\epsilon$-stationary solution. To further reduce the sample complexity, we propose an accelerated Riemannian stochastic gradient descent ascent (Acc-RSGDA) algorithm based on the momentum-based variance-reduced technique. We prove that our Acc-RSGDA algorithm achieves a lower sample complexity of $\tilde{O}(\kappa^{4}\epsilon^{-3})$ in searching for an $\epsilon$-stationary solution of the GNSC minimax problems. Extensive experimental results on the robust distributional optimization and robust Deep Neural Networks (DNNs) training over Stiefel manifold demonstrate efficiency of our algorithms.
    Explaining Imitation Learning through Frames. (arXiv:2301.01088v1 [cs.LG])
    As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.
    SpectroscopyNet: Learning to pre-process Spectroscopy Signals without clean data. (arXiv:2110.13748v2 [cs.LG] UPDATED)
    In this work we propose a deep learning approach to clean spectroscopy signals using only uncleaned data. Cleaning signals from spectroscopy instrument noise is challenging as noise exhibits an unknown, non-zero mean, multivariate distributions. Our framework is a siamese neural net that learns identifiable disentanglement of the signal and noise components under a stationarity assumption. The disentangled representations satisfy reconstruction fidelity, reduce consistencies with measurements of unrelated targets and imposes relaxed-orthogonality constraints between the signal and noise representations. Evaluations on a laser induced breakdown spectroscopy (LIBS) dataset from the ChemCam instrument onboard the Martian Curiosity rover show a superior performance in cleaning LIBS measurements compared to the standard feature engineered approaches being used by the ChemCam team.  ( 2 min )
    RAIDER: Reinforcement-aided Spear Phishing Detector. (arXiv:2105.07582v3 [cs.CR] UPDATED)
    Spear Phishing is a harmful cyber-attack facing business and individuals worldwide. Considerable research has been conducted recently into the use of Machine Learning (ML) techniques to detect spear-phishing emails. ML-based solutions may suffer from zero-day attacks; unseen attacks unaccounted for in the training data. As new attacks emerge, classifiers trained on older data are unable to detect these new varieties of attacks resulting in increasingly inaccurate predictions. Spear Phishing detection also faces scalability challenges due to the growth of the required features which is proportional to the number of the senders within a receiver mailbox. This differs from traditional phishing attacks which typically perform only a binary classification between phishing and benign emails. Therefore, we devise a possible solution to these problems, named RAIDER: Reinforcement AIded Spear Phishing DEtectoR. A reinforcement-learning based feature evaluation system that can automatically find the optimum features for detecting different types of attacks. By leveraging a reward and penalty system, RAIDER allows for autonomous features selection. RAIDER also keeps the number of features to a minimum by selecting only the significant features to represent phishing emails and detect spear-phishing attacks. After extensive evaluation of RAIDER over 11,000 emails and across 3 attack scenarios, our results suggest that using reinforcement learning to automatically identify the significant features could reduce the dimensions of the required features by 55% in comparison to existing ML-based systems. It also improves the accuracy of detecting spoofing attacks by 4% from 90% to 94%. In addition, RAIDER demonstrates reasonable detection accuracy even against a sophisticated attack named Known Sender in which spear-phishing emails greatly resemble those of the impersonated sender.  ( 3 min )
    Pseudo-Inverted Bottleneck Convolution for DARTS Search Space. (arXiv:2301.01286v1 [cs.LG])
    Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.  ( 2 min )
    Hypernetworks for Zero-shot Transfer in Reinforcement Learning. (arXiv:2211.15457v2 [cs.LG] UPDATED)
    In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
    Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning. (arXiv:2301.00944v1 [cs.LG])
    In large-scale machine learning, recent works have studied the effects of compressing gradients in stochastic optimization in order to alleviate the communication bottleneck. These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays. Perhaps surprisingly, despite the surge of interest in large-scale, multi-agent reinforcement learning, almost nothing is known about the analogous question: Are common reinforcement learning (RL) algorithms also robust to similar perturbations? In this paper, we investigate this question by studying a variant of the classical temporal difference (TD) learning algorithm with a perturbed update direction, where a general compression operator is used to model the perturbation. Our main technical contribution is to show that compressed TD algorithms, coupled with an error-feedback mechanism used widely in optimization, exhibit the same non-asymptotic theoretical guarantees as their SGD counterparts. We then extend our results significantly to nonlinear stochastic approximation algorithms and multi-agent settings. In particular, we prove that for multi-agent TD learning, one can achieve linear convergence speedups in the number of agents while communicating just $\tilde{O}(1)$ bits per agent at each time step. Our work is the first to provide finite-time results in RL that account for general compression operators and error-feedback in tandem with linear function approximation and Markovian sampling. Our analysis hinges on studying the drift of a novel Lyapunov function that captures the dynamics of a memory variable introduced by error feedback.
    Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS). (arXiv:2301.01038v1 [cs.LG])
    Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history of application in production and have had numerous positive impacts, but also hold great potential when integrated with Industry 4.0 and advanced machine learning, particularly deep learning, solutions. However, in order to implement these solutions in production and enable widespread adoption, the scalability and transferability of deep learning methods have become a focus of research. While transfer learning has proven successful in many cases, particularly with computer vision and homogenous data inputs, it can be challenging to apply to heterogeneous data. Motivated by the need to transfer and standardize established processes to different, non-identical environments and by the challenge of adapting to heterogeneous data representations, this work introduces the Domain Adaptation Neural Network with Cyclic Supervision (DBACS) approach. DBACS addresses the issue of model generalization through domain adaptation, specifically for heterogeneous data, and enables the transfer and scalability of deep learning-based statistical control methods in a general manner. Additionally, the cyclic interactions between the different parts of the model enable DBACS to not only adapt to the domains, but also match them. To the best of our knowledge, DBACS is the first deep learning approach to combine adaptation and matching for heterogeneous data settings. For comparison, this work also includes subspace alignment and a multi-view learning that deals with heterogeneous representations by mapping data into correlated latent feature spaces. Finally, DBACS with its ability to adapt and match, is applied to a virtual metrology use case for an etching process run on different machine types in semiconductor manufacturing.
    Finding the Most Transferable Tasks for Brain Image Segmentation. (arXiv:2301.00934v1 [eess.IV])
    Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source tasks are available for transfer. In this paper, we propose a prior knowledge guided and transferability based framework to select the best source tasks among a collection of brain image segmentation tasks, to improve the transfer learning performance on the given target task. The framework consists of modality analysis, RoI (region of interest) analysis, and transferability estimation, such that the source task selection can be refined step by step. Specifically, we adapt the state-of-the-art analytical transferability estimation metrics to medical image segmentation tasks and further show that their performance can be significantly boosted by filtering candidate source tasks based on modality and RoI characteristics. Our experiments on brain matter, brain tumor, and white matter hyperintensities segmentation datasets reveal that transferring from different tasks under the same modality is often more successful than transferring from the same task under different modalities. Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance. And such similarity can be captured using the Structural Similarity index in the label space.
    Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning. (arXiv:2212.09612v1 [physics.flu-dyn] CROSS LISTED)
    We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair. We approach the problem by means of Multi Objective Reinforcement Learning (MORL), combining scalarization techniques together with a Q-learning algorithm, for Lagrangian drifters that have variable swimming velocity. We show that MORL is able to find a set of trade-off solutions forming an optimal Pareto frontier. As a benchmark, we show that a set of heuristic strategies are dominated by the MORL solutions. We consider the situation in which the agents cannot update their control variables continuously, but only after a discrete (decision) time, $\tau$. We show that there is a range of decision times, in between the Lyapunov time and the continuous updating limit, where Reinforcement Learning finds strategies that significantly improve over heuristics. In particular, we discuss how large decision times require enhanced knowledge of the flow, whereas for smaller $\tau$ all a priori heuristic strategies become Pareto optimal.
    Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network. (arXiv:2211.10563v2 [cs.CV] UPDATED)
    Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.
    Invariance-Aware Randomized Smoothing Certificates. (arXiv:2211.14207v2 [cs.LG] UPDATED)
    Building models that comply with the invariances inherent to different domains, such as invariance under translation or rotation, is a key aspect of applying machine learning to real world problems like molecular property prediction, medical imaging, protein folding or LiDAR classification. For the first time, we study how the invariances of a model can be leveraged to provably guarantee the robustness of its predictions. We propose a gray-box approach, enhancing the powerful black-box randomized smoothing technique with white-box knowledge about invariances. First, we develop gray-box certificates based on group orbits, which can be applied to arbitrary models with invariance under permutation and Euclidean isometries. Then, we derive provably tight gray-box certificates. We experimentally demonstrate that the provably tight certificates can offer much stronger guarantees, but that in practical scenarios the orbit-based method is a good approximation.
    Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem. (arXiv:2210.00411v3 [cs.CV] UPDATED)
    Self-supervised monocular depth estimation (MDE) models universally suffer from the notorious edge-fattening issue. Triplet loss, as a widespread metric learning strategy, has largely succeeded in many computer vision applications. In this paper, we redesign the patch-based triplet loss in MDE to alleviate the ubiquitous edge-fattening issue. We show two drawbacks of the raw triplet loss in MDE and demonstrate our problem-driven redesigns. First, we present a min. operator based strategy applied to all negative samples, to prevent well-performing negatives sheltering the error of edge-fattening negatives. Second, we split the anchor-positive distance and anchor-negative distance from within the original triplet, which directly optimizes the positives without any mutual effect with the negatives. Extensive experiments show the combination of these two small redesigns can achieve unprecedented results: Our powerful and versatile triplet loss not only makes our model outperform all previous SoTA by a large margin, but also provides substantial performance boosts to a large number of existing models, while introducing no extra inference computation at all.
    Stochastic Langevin Differential Inclusions with Applications to Machine Learning. (arXiv:2206.11533v2 [math.OC] UPDATED)
    Stochastic differential equations of Langevin-diffusion form have received significant attention, thanks to their foundational role in both Bayesian sampling algorithms and optimization in machine learning. In the latter, they serve as a conceptual model of the stochastic gradient flow in training over-parametrized models. However, the literature typically assumes smoothness of the potential, whose gradient is the drift term. Nevertheless, there are many problems, for which the potential function is not continuously differentiable, and hence the drift is not Lipschitz continuous everywhere. This is exemplified by robust losses and Rectified Linear Units in regression problems. In this paper, we show some foundational results regarding the flow and asymptotic properties of Langevin-type Stochastic Differential Inclusions under assumptions appropriate to the machine-learning settings. In particular, we show strong existence of the solution, as well as asymptotic minimization of the canonical free-energy functional.
    UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup. (arXiv:2209.08928v3 [cs.LG] UPDATED)
    Subpopulation shift widely exists in many real-world machine learning applications, referring to the training and test distributions containing the same subpopulation groups but varying in subpopulation frequencies. Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset. However, some recent studies have recognized that most of these approaches fail to improve the performance over empirical risk minimization especially when applied to over-parameterized neural networks. In this work, we propose a simple yet practical framework, called uncertainty-aware mixup (UMIX), to mitigate the overfitting issue in over-parameterized models by reweighting the ''mixed'' samples according to the sample uncertainty. The training-trajectories-based uncertainty estimation is equipped in the proposed UMIX for each sample to flexibly characterize the subpopulation distribution. We also provide insightful theoretical analysis to verify that UMIX achieves better generalization bounds over prior works. Further, we conduct extensive empirical studies across a wide range of tasks to validate the effectiveness of our method both qualitatively and quantitatively. Code is available at https://github.com/TencentAILabHealthcare/UMIX.
    Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds. (arXiv:2111.14843v4 [cs.SD] UPDATED)
    Audio-visual navigation combines sight and hearing to navigate to a sound-emitting source in an unmapped environment. While recent approaches have demonstrated the benefits of audio input to detect and find the goal, they focus on clean and static sound sources and struggle to generalize to unheard sounds. In this work, we propose the novel dynamic audio-visual navigation benchmark which requires catching a moving sound source in an environment with noisy and distracting sounds, posing a range of new challenges. We introduce a reinforcement learning approach that learns a robust navigation policy for these complex settings. To achieve this, we propose an architecture that fuses audio-visual information in the spatial feature space to learn correlations of geometric information inherent in both local maps and audio signals. We demonstrate that our approach consistently outperforms the current state-of-the-art by a large margin across all tasks of moving sounds, unheard sounds, and noisy environments, on two challenging 3D scanned real-world environments, namely Matterport3D and Replica. The benchmark is available at this http URL  ( 2 min )
    Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network. (arXiv:2211.10532v2 [cs.CV] UPDATED)
    Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e.g., 4x and 8x). The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Dense Block (RIDB). Moreover, we propose a joint discriminator which discriminates both image data and embedded semantics. The joint discriminator learns the joint probability distribution of the image space and latent space. We also use a Relativistic average Least Squares loss (RaLS) as the adversarial loss to alleviate the gradient vanishing problem and enhance the stability of the training procedure. Extensive experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons.
    Adversarial Self-Attention for Language Understanding. (arXiv:2206.12608v2 [cs.CL] UPDATED)
    Deep neural models (e.g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness. This paper advances self-attention mechanism to its robust variant for Transformer-based pre-trained language models (e.g. BERT). We propose \textit{Adversarial Self-Attention} mechanism (ASA), which adversarially biases the attentions to effectively suppress the model reliance on features (e.g. specific keywords) and encourage its exploration of broader semantics. We conduct comprehensive evaluation across a wide range of tasks for both pre-training and fine-tuning stages. For pre-training, ASA unfolds remarkable performance gain compared to naive training for longer steps. For fine-tuning, ASA-empowered models outweigh naive models by a large margin considering both generalization and robustness.
    Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set. (arXiv:2206.13211v2 [cs.LG] UPDATED)
    The recent work ``Combinatorial Optimization with Physics-Inspired Graph Neural Networks'' [Nat Mach Intell 4 (2022) 367] introduces a physics-inspired unsupervised Graph Neural Network (GNN) to solve combinatorial optimization problems on sparse graphs. To test the performances of these GNNs, the authors of the work show numerical results for two fundamental problems: maximum cut and maximum independent set (MIS). They conclude that "the graph neural network optimizer performs on par or outperforms existing solvers, with the ability to scale beyond the state of the art to problems with millions of variables." In this comment, we show that a simple greedy algorithm, running in almost linear time, can find solutions for the MIS problem of much better quality than the GNN. The greedy algorithm is faster by a factor of $10^4$ with respect to the GNN for problems with a million variables. We do not see any good reason for solving the MIS with these GNN, as well as for using a sledgehammer to crack nuts. In general, many claims of superiority of neural networks in solving combinatorial problems are at risk of being not solid enough, since we lack standard benchmarks based on really hard problems. We propose one of such hard benchmarks, and we hope to see future neural network optimizers tested on these problems before any claim of superiority is made.
    Optimal transport with $f$-divergence regularization and generalized Sinkhorn algorithm. (arXiv:2105.14337v2 [math.OC] UPDATED)
    Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general $f$-divergence leads to a natural generalization. The case of divergences defined by superlinear functions was recently studied by Di Marino and Gerolin. Using convex analysis, we extend the theory developed so far to include all $f$-divergences defined by functions of Legendre type, and prove that under some mild conditions, strong duality holds, optimums in both the primal and dual problems are attained, the generalization of the $c$-transform is well-defined, and we give sufficient conditions for the generalized Sinkhorn algorithm to converge to an optimal solution. We propose a practical algorithm for computing an approximate solution of the optimal transport problem with $f$-divergence regularization via the generalized Sinkhorn algorithm. Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different $f$-divergences for regularization, which influences convergence speed, numerical stability and sparsity of the optimal coupling.  ( 2 min )
    Efficient Quantized Sparse Matrix Operations on Tensor Cores. (arXiv:2209.06979v3 [cs.DC] UPDATED)
    The exponentially growing model size drives the continued success of deep learning, but it brings prohibitive computation and memory cost. From the algorithm perspective, model sparsification and quantization have been studied to alleviate the problem. From the architecture perspective, hardware vendors provide Tensor cores for acceleration. However, it is very challenging to gain practical speedups from sparse, low-precision matrix operations on Tensor cores, because of the strict requirements for data layout and lack of support for efficiently manipulating the low-precision integers. We propose Magicube, a high-performance sparse-matrix library for low-precision integers on Tensor cores. Magicube supports SpMM and SDDMM, two major sparse operations in deep learning with mixed precision. Experimental results on an NVIDIA A100 GPU show that Magicube achieves on average 1.44x (up to 2.37x) speedup over the vendor-optimized library for sparse kernels, and 1.43x speedup over the state-of-the-art with a comparable accuracy for end-to-end sparse Transformer inference.
    A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits. (arXiv:2111.12550v2 [cs.HC] UPDATED)
    Crowdsourcing system has emerged as an effective platform for labeling data with relatively low cost by using non-expert workers. Inferring correct labels from multiple noisy answers on data, however, has been a challenging problem, since the quality of the answers varies widely across tasks and workers. Many existing works have assumed that there is a fixed ordering of workers in terms of their skill levels, and focused on estimating worker skills to aggregate the answers from workers with different weights. In practice, however, the worker skill changes widely across tasks, especially when the tasks are heterogeneous. In this paper, we consider a new model, called $d$-type specialization model, in which each task and worker has its own (unknown) type and the reliability of each worker can vary in the type of a given task and that of a worker. We allow that the number $d$ of types can scale in the number of tasks. In this model, we characterize the optimal sample complexity to correctly infer the labels within any given accuracy, and propose label inference algorithms achieving the order-wise optimal limit even when the types of tasks or those of workers are unknown. We conduct experiments both on synthetic and real datasets, and show that our algorithm outperforms the existing algorithms developed based on more strict model assumptions.  ( 2 min )
    A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems. (arXiv:2108.08298v5 [cs.LG] UPDATED)
    Temperature field reconstruction of heat source systems (TFR-HSS) with limited monitoring sensors occurred in thermal management plays an important role in real time health detection system of electronic equipment in engineering. However, prior methods with common interpolations usually cannot provide accurate reconstruction performance as required. In addition, there exists no public dataset for widely research of reconstruction methods to further boost the reconstruction performance and engineering applications. To overcome this problem, this work develops a machine learning modelling benchmark for TFR-HSS task. First, the TFR-HSS task is mathematically modelled from real-world engineering problem and four types of numerically modellings have been constructed to transform the problem into discrete mapping forms. Then, this work proposes a set of machine learning modelling methods, including the general machine learning methods and the deep learning methods, to advance the state-of-the-art methods over temperature field reconstruction. More importantly, this work develops a novel benchmark dataset, namely Temperature Field Reconstruction Dataset (TFRD), to evaluate these machine learning modelling methods for the TFR-HSS task. Finally, a performance analysis of typical methods is given on TFRD, which can be served as the baseline results on this benchmark.  ( 2 min )
    Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles. (arXiv:2110.12067v2 [stat.ML] UPDATED)
    Gaussian graphical models provide a powerful framework for uncovering conditional dependence relationships between sets of nodes; they have found applications in a wide variety of fields including sensor and communication networks, physics, finance, and computational biology. Often, one observes data on the nodes and the task is to learn the graph structure, or perform graphical model selection. While this is a well-studied problem with many popular techniques, there are typically three major practical challenges: i) many existing algorithms become computationally intractable in huge-data settings with tens of thousands of nodes; ii) the need for separate data-driven hyperparameter tuning considerably adds to the computational burden; iii) the statistical accuracy of selected edges often deteriorates as the dimension and/or the complexity of the underlying graph structures increase. We tackle these problems by developing the novel Minipatch Graph (MPGraph) estimator. Our approach breaks up the huge graph learning problem into many smaller problems by creating an ensemble of tiny random subsets of both the observations and the nodes, termed minipatches. We then leverage recent advances that use hard thresholding to solve the latent variable graphical model problem to consistently learn the graph on each minipatch. Our approach is computationally fast, embarrassingly parallelizable, memory efficient, and has integrated stability-based hyperparamter tuning. Additionally, we prove that under weaker assumptions than that of the Graphical Lasso, our MPGraph estimator achieves graph selection consistency. We compare our approach to state-of-the-art computational approaches for Gaussian graphical model selection including the BigQUIC algorithm, and empirically demonstrate that our approach is not only more statistically accurate but also extensively faster for huge graph learning problems.  ( 3 min )
    Independence Testing for Bounded Degree Bayesian Network. (arXiv:2204.08690v2 [cs.DS] UPDATED)
    We study the following independence testing problem: given access to samples from a distribution $P$ over $\{0,1\}^n$, decide whether $P$ is a product distribution or whether it is $\varepsilon$-far in total variation distance from any product distribution. For arbitrary distributions, this problem requires $\exp(n)$ samples. We show in this work that if $P$ has a sparse structure, then in fact only linearly many samples are required. Specifically, if $P$ is Markov with respect to a Bayesian network whose underlying DAG has in-degree bounded by $d$, then $\tilde{\Theta}(2^{d/2}\cdot n/\varepsilon^2)$ samples are necessary and sufficient for independence testing.
    PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning. (arXiv:2205.06401v3 [cs.CR] UPDATED)
    Contrastive learning pre-trains an image encoder using a large amount of unlabeled data such that the image encoder can be used as a general-purpose feature extractor for various downstream tasks. In this work, we propose PoisonedEncoder, a data poisoning attack to contrastive learning. In particular, an attacker injects carefully crafted poisoning inputs into the unlabeled pre-training data, such that the downstream classifiers built based on the poisoned encoder for multiple target downstream tasks simultaneously classify attacker-chosen, arbitrary clean inputs as attacker-chosen, arbitrary classes. We formulate our data poisoning attack as a bilevel optimization problem, whose solution is the set of poisoning inputs; and we propose a contrastive-learning-tailored method to approximately solve it. Our evaluation on multiple datasets shows that PoisonedEncoder achieves high attack success rates while maintaining the testing accuracy of the downstream classifiers built upon the poisoned encoder for non-attacker-chosen inputs. We also evaluate five defenses against PoisonedEncoder, including one pre-processing, three in-processing, and one post-processing defenses. Our results show that these defenses can decrease the attack success rate of PoisonedEncoder, but they also sacrifice the utility of the encoder or require a large clean pre-training dataset.
    Adaptive Sampling for Discovery. (arXiv:2205.14829v3 [stat.ML] UPDATED)
    In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses. This problem has wide applications to real-world discovery problems, for example drug discovery with the help of machine learning models. ASD algorithms face the well-known exploration-exploitation dilemma. The algorithm needs to choose points that yield information to improve model estimates but it also needs to exploit the model. We rigorously formulate the problem and propose a general information-directed sampling (IDS) algorithm. We provide theoretical guarantees for the performance of IDS in linear, graph and low-rank models. The benefits of IDS are shown in both simulation experiments and real-data experiments for discovering chemical reaction conditions.
    Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). (arXiv:2203.13366v7 [cs.IR] UPDATED)
    For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned knowledge and representations from one task to another, thus restricting the generalization ability of existing recommendation approaches, e.g., a sequential recommendation model can hardly be applied or transferred to a review generation method. To deal with such issues, considering that language can describe almost anything and language grounding is a powerful medium to represent various problems or tasks, we present a flexible and unified text-to-text paradigm called "Pretrain, Personalized Prompt, and Predict Paradigm" (P5) for recommendation, which unifies various recommendation tasks in a shared framework. In P5, all data such as user-item interactions, user descriptions, item metadata, and user reviews are converted to a common format -- natural language sequences. The rich information from natural language assists P5 to capture deeper semantics for personalization and recommendation. Specifically, P5 learns different tasks with the same language modeling objective during pretraining. Thus, it serves as the foundation model for various downstream recommendation tasks, allows easy integration with other modalities, and enables instruction-based recommendation based on prompts. P5 advances recommender systems from shallow model to deep model to big model, and will revolutionize the technical form of recommender systems towards universal recommendation engine. With adaptive personalized prompt for different users, P5 is able to make predictions in a zero-shot or few-shot manner and largely reduces the necessity for extensive fine-tuning. On several recommendation benchmarks, we conduct experiments to show the effectiveness of P5. We release the source code at https://github.com/jeykigung/P5.
    How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents. (arXiv:2112.07640v4 [cs.GT] UPDATED)
    The usage of automated learning agents is becoming increasingly prevalent in many online economic applications such as online auctions and automated trading. Motivated by such applications, this paper is dedicated to fundamental modeling and analysis of the strategic situations that the users of automated learning agents are facing. We consider strategic settings where several users engage in a repeated online interaction, assisted by regret-minimizing learning agents that repeatedly play a "game" on their behalf. We propose to view the outcomes of the agents' dynamics as inducing a "meta-game" between the users. Our main focus is on whether users can benefit in this meta-game from "manipulating" their own agents by misreporting their parameters to them. We define a general framework to model and analyze these strategic interactions between users of learning agents for general games and analyze the equilibria induced between the users in three classes of games. We show that, generally, users have incentives to misreport their parameters to their own agents, and that such strategic user behavior can lead to very different outcomes than those anticipated by standard analysis.
    Adaptive Discriminative Regularization for Visual Classification. (arXiv:2203.00833v2 [cs.LG] UPDATED)
    How to improve discriminative feature learning is central in classification. Existing works address this problem by explicitly increasing inter-class separability and intra-class similarity, whether by constructing positive and negative pairs for contrastive learning or posing tighter class separating margins. These methods do not exploit the similarity between different classes as they adhere to i.i.d. assumption in data. In this paper, we embrace the real-world data distribution setting that some classes share semantic overlaps due to their similar appearances or concepts. Regarding this hypothesis, we propose a novel regularization to improve discriminative learning. We first calibrate the estimated highest likelihood of one sample based on its semantically neighboring classes, then encourage the overall likelihood predictions to be deterministic by imposing an adaptive exponential penalty. As the gradient of the proposed method is roughly proportional to the uncertainty of the predicted likelihoods, we name it adaptive discriminative regularization (ADR), trained along with a standard cross entropy loss in classification. Extensive experiments demonstrate that it can yield consistent and non-trivial performance improvements in a variety of visual classification tasks (over 10 benchmarks). Furthermore, we find it is robust to long-tailed and noisy label data distribution. Its flexible design enables its compatibility with mainstream classification architectures and losses.
    TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence. (arXiv:2203.06764v3 [cs.CV] UPDATED)
    We present a self-supervised and self-calibrating multi-shot approach to imaging through atmospheric turbulence, called TurbuGAN. Our approach requires no paired training data, adapts itself to the distribution of the turbulence, leverages domain-specific data priors, and can generalize from tens to thousands of measurements. We achieve such functionality through an adversarial sensing framework adapted from CryoGAN, which uses a discriminator network to match the distributions of captured and simulated measurements. Our framework builds on CryoGAN by (1) generalizing the forward measurement model to incorporate physically accurate and computationally efficient models for light propagation through anisoplanatic turbulence, (2) enabling adaptation to slightly misspecified forward models, and (3) leveraging domain-specific prior knowledge using pretrained generative networks, when available. We validate TurbuGAN on both computationally simulated and experimentally captured images distorted with anisoplanatic turbulence.
    Probabilistic Approach for Road-Users Detection. (arXiv:2112.01360v2 [cs.CV] UPDATED)
    Object detection in autonomous driving applications implies that the detection and tracking of semantic objects are commonly native to urban driving environments, as pedestrians and vehicles. One of the major challenges in state-of-the-art deep-learning based object detection is false positive which occurrences with overconfident scores. This is highly undesirable in autonomous driving and other critical robotic-perception domains because of safety concerns. This paper proposes an approach to alleviate the problem of overconfident predictions by introducing a novel probabilistic layer to deep object detection networks in testing. The suggested approach avoids the traditional Sigmoid or Softmax prediction layer which often produces overconfident predictions. It is demonstrated that the proposed technique reduces overconfidence in the false positives without degrading the performance on the true positives. The approach is validated on the 2D-KITTI objection detection through the YOLOV4 and SECOND (Lidar-based detector). The proposed approach enables enabling interpretable probabilistic predictions without the requirement of re-training the network and therefore is very practical.
    Off-Policy Evaluation in Embedded Spaces. (arXiv:2203.02807v2 [cs.LG] UPDATED)
    Off-policy evaluation methods are important in recommendation systems and search engines, where data collected under an existing logging policy is used to estimate the performance of a new proposed policy. A common approach to this problem is weighting, where data is weighted by a density ratio between the probability of actions given contexts in the target and logged policies. In practice, two issues often arise. First, many problems have very large action spaces and we may not observe rewards for most actions, and so in finite samples we may encounter a positivity violation. Second, many recommendation systems are not probabilistic and so having access to logging and target policy densities may not be feasible. To address these issues, we introduce the featurized embedded permutation weighting estimator. The estimator computes the density ratio in an action embedding space, which reduces the possibility of positivity violations. The density ratio is computed leveraging recent advances in normalizing flows and density ratio estimation as a classification problem, in order to obtain estimates which are feasible in practice.
    Learning under Storage and Privacy Constraints. (arXiv:2202.02892v2 [cs.IT] UPDATED)
    Storage-efficient privacy-preserving learning is crucial due to the increasing amounts of sensitive user data required for modern learning tasks. We propose a framework for reducing the storage cost of user data while at the same time providing privacy guarantees, without essential loss in the utility of the data for learning. Our method comprises noise injection followed by lossy compression. We show that, when appropriately matching the lossy compression to the distribution of the added noise, the compressed examples converge, in distribution, to that of the noise-free training data as the sample size of the training data (or the dimension of the training data) increases. In this sense, the utility of the data for learning is essentially maintained, while reducing storage and privacy leakage by quantifiable amounts. We present experimental results on the CelebA dataset for gender classification and find that our suggested pipeline delivers in practice on the promise of the theory: the individuals in the images are unrecognizable (or less recognizable, depending on the noise level), overall storage of the data is substantially reduced, with no essential loss (and in some cases a slight boost) to the classification accuracy. As an added bonus, our experiments suggest that our method yields a substantial boost to robustness in the face of adversarial test data.
    A Survey on the Robustness of Feature Importance and Counterfactual Explanations. (arXiv:2111.00358v2 [cs.LG] UPDATED)
    There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models. Arguably, the most popular among them are local explanations that focus on investigating model behaviour for individual instances. Several methods have been proposed for local analysis, but relatively lesser effort has gone into understanding if the explanations are robust and accurately reflect the behaviour of underlying models. In this work, we present a survey of the works that analysed the robustness of two classes of local explanations (feature importance and counterfactual explanations) that are popularly used in analysing AI/ML models in finance. The survey aims to unify existing definitions of robustness, introduces a taxonomy to classify different robustness approaches, and discusses some interesting results. Finally, the survey introduces some pointers about extending current robustness analysis approaches so as to identify reliable explainability methods.
    Transferable Energy Storage Bidder. (arXiv:2301.01233v1 [cs.LG])
    Energy storage resources must consider both price uncertainties and their physical operating characteristics when participating in wholesale electricity markets. This is a challenging problem as electricity prices are highly volatile, and energy storage has efficiency losses, power, and energy constraints. This paper presents a novel, versatile, and transferable approach combining model-based optimization with a convolutional long short-term memory network for energy storage to respond to or bid into wholesale electricity markets. We apply transfer learning to the ConvLSTM network to quickly adapt the trained bidding model to new market environments. We test our proposed approach using historical prices from New York State, showing it achieves state-of-the-art results, achieving between 70% to near 90% profit ratio compared to perfect foresight cases, in both price response and wholesale market bidding setting with various energy storage durations. We also test a transfer learning approach by pre-training the bidding model using New York data and applying it to arbitrage in Queensland, Australia. The result shows transfer learning achieves exceptional arbitrage profitability with as little as three days of local training data, demonstrating its significant advantage over training from scratch in scenarios with very limited data availability.
    DMOps: Data Management Operation and Recipes. (arXiv:2301.01228v1 [cs.DB])
    Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Acknowledging its importance, various research and policies are suggested by academia, industry, and government departments. Although the capability of utilizing existing data is essential, the capability to build a dataset has become more important than ever. In consideration of this trend, we propose a "Data Management Operation and Recipes" that will guide the industry regardless of the task or domain. In other words, this paper presents the concept of DMOps derived from real-world experience. By offering a baseline for building data, we want to help the industry streamline its data operation optimally.
    Decentralized cooperative perception for autonomous vehicles: Learning to value the unknown. (arXiv:2301.01250v1 [cs.LG])
    Recently, we have been witnesses of accidents involving autonomous vehicles and their lack of sufficient information. One way to tackle this issue is to benefit from the perception of different view points, namely cooperative perception. We propose here a decentralized collaboration, i.e. peer-to-peer, in which the agents are active in their quest for full perception by asking for specific areas in their surroundings on which they would like to know more. Ultimately, we want to optimize a trade-off between the maximization of knowledge about moving objects and the minimization of the total volume of information received from others, to limit communication costs and message processing time. For this, we propose a way to learn a communication policy that reverses the usual communication paradigm by only requesting from other vehicles what is unknown to the ego-vehicle, instead of filtering on the sender side. We tested three different generative models to be taken as base for a Deep Reinforcement Learning (DRL) algorithm, and compared them to a broadcasting policy and a policy randomly selecting areas. In particular, we propose Locally Predictable VAE (LP-VAE), which appears to be producing better belief states for predictions than state-of-the-art models, both as a standalone model and in the context of DRL. Experiments were conducted in the driving simulator CARLA. Our best models reached on average a gain of 25% of the total complementary information, while only requesting about 5% of the ego-vehicle's perceptual field. This trade-off is adjustable through the interpretable hyperparameters of our reward function.
    Activity Detection for Grant-Free NOMA in Massive IoT Networks. (arXiv:2301.01274v1 [eess.SP])
    Recently, grant-free transmission paradigm has been introduced for massive Internet of Things (IoT) networks to save both time and bandwidth and transmit the message with low latency. In order to accurately decode the message of each device at the base station (BS), first, the active devices at each transmission frame must be identified. In this work, first we investigate the problem of activity detection as a threshold comparing problem. We show the convexity of the activity detection method through analyzing its probability of error which makes it possible to find the optimal threshold for minimizing the activity detection error. Consequently, to achieve an optimum solution, we propose a deep learning (DL)-based method called convolutional neural network (CNN)-activity detection (AD). In order to make it more practical, we consider unknown and time-varying activity rate for the IoT devices. Our simulations verify that our proposed CNN-AD method can achieve higher performance compared to the existing non-Bayesian greedy-based methods. This is while existing methods need to know the activity rate of IoT devices, while our method works for unknown and even time-varying activity rates
    Linear chain conditional random fields, hidden Markov models, and related classifiers. (arXiv:2301.01293v1 [stat.ML])
    Practitioners use Hidden Markov Models (HMMs) in different problems for about sixty years. Besides, Conditional Random Fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose two contributions. First, we show that basic Linear-Chain CRFs (LC-CRFs), considered as different from the HMMs, are in fact equivalent to them in the sense that for each LC-CRF there exists a HMM - that we specify - whom posterior distribution is identical to the given LC-CRF. Second, we show that it is possible to reformulate the generative Bayesian classifiers Maximum Posterior Mode (MPM) and Maximum a Posteriori (MAP) used in HMMs, as discriminative ones. The last point is of importance in many fields, especially in Natural Language Processing (NLP), as it shows that in some situations dropping HMMs in favor of CRFs was not necessary.
    A Tutorial on Parametric Variational Inference. (arXiv:2301.01236v1 [stat.ML])
    Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.
    ExploreADV: Towards exploratory attack for Neural Networks. (arXiv:2301.01223v1 [cs.CR])
    Although deep learning has made remarkable progress in processing various types of data such as images, text and speech, they are known to be susceptible to adversarial perturbations: perturbations specifically designed and added to the input to make the target model produce erroneous output. Most of the existing studies on generating adversarial perturbations attempt to perturb the entire input indiscriminately. In this paper, we propose ExploreADV, a general and flexible adversarial attack system that is capable of modeling regional and imperceptible attacks, allowing users to explore various kinds of adversarial examples as needed. We adapt and combine two existing boundary attack methods, DeepFool and Brendel\&Bethge Attack, and propose a mask-constrained adversarial attack system, which generates minimal adversarial perturbations under the pixel-level constraints, namely ``mask-constraints''. We study different ways of generating such mask-constraints considering the variance and importance of the input features, and show that our adversarial attack system offers users good flexibility to focus on sub-regions of inputs, explore imperceptible perturbations and understand the vulnerability of pixels/regions to adversarial attacks. We demonstrate our system to be effective based on extensive experiments and user study.
    Assessment of creditworthiness models privacy-preserving training with synthetic data. (arXiv:2301.01212v1 [q-fin.RM])
    Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3\% of AUC and 6\% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers' privacy and to address problems that until now have been hampered by the availability of information.
    Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios. (arXiv:2301.01231v1 [cs.LG])
    Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and Graph Attention Network to build a model capable of predicting reactivity ratios based on the monomers chemical structures.
    Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications. (arXiv:2301.01200v1 [cs.CV])
    The advances in remote sensing technologies have boosted applications for Earth observation. These technologies provide multiple observations or views with different levels of information. They might contain static or temporary views with different levels of resolution, in addition to having different types and amounts of noise due to sensor calibration or deterioration. A great variety of deep learning models have been applied to fuse the information from these multiple views, known as deep multi-view or multi-modal fusion learning. However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques. This article gathers works on multi-view fusion for Earth observation by focusing on the common practices and approaches used in the literature. We summarize and structure insights from several different publications concentrating on unifying points and ideas. In this manuscript, we provide a harmonized terminology while at the same time mentioning the various alternative terms that are used in literature. The topics covered by the works reviewed focus on supervised learning with the use of neural network models. We hope this review, with a long list of recent references, can support future research and lead to a unified advance in the area.
    A Multi-Source Information Learning Framework for Airbnb Price Prediction. (arXiv:2301.01222v1 [cs.LG])
    With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.
    Task-Guided IRL in POMDPs that Scales. (arXiv:2301.01219v1 [cs.LG])
    In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techniques make the often unrealistic assumption that the agent has access to full information about the environment. We remove this assumption by developing an algorithm for IRL in partially observable Markov decision processes (POMDPs). We address two limitations of existing IRL techniques. First, they require an excessive amount of data due to the information asymmetry between the expert and the learner. Second, most of these IRL techniques require solving the computationally intractable forward problem -- computing an optimal policy given a reward function -- in POMDPs. The developed algorithm reduces the information asymmetry while increasing the data efficiency by incorporating task specifications expressed in temporal logic into IRL. Such specifications may be interpreted as side information available to the learner a priori in addition to the demonstrations. Further, the algorithm avoids a common source of algorithmic complexity by building on causal entropy as the measure of the likelihood of the demonstrations as opposed to entropy. Nevertheless, the resulting problem is nonconvex due to the so-called forward problem. We solve the intrinsic nonconvexity of the forward problem in a scalable manner through a sequential linear programming scheme that guarantees to converge to a locally optimal policy. In a series of examples, including experiments in a high-fidelity Unity simulator, we demonstrate that even with a limited amount of data and POMDPs with tens of thousands of states, our algorithm learns reward functions and policies that satisfy the task while inducing similar behavior to the expert by leveraging the provided side information.
    Offline Reinforcement Learning with Differential Privacy. (arXiv:2206.00810v2 [cs.LG] UPDATED)
    The offline reinforcement learning (RL) problem is often motivated by the need to learn data-driven decision policies in financial, legal and healthcare applications. However, the learned policy could retain sensitive information of individuals in the training data (e.g., treatment and outcome of patients), thus susceptible to various privacy risks. We design offline RL algorithms with differential privacy guarantees which provably prevent such risks. These algorithms also enjoy strong instance-dependent learning bounds under both tabular and linear Markov decision process (MDP) settings. Our theory and simulation suggest that the privacy guarantee comes at (almost) no drop in utility comparing to the non-private counterpart for a medium-size dataset.
    TC-GNN: Accelerating Sparse Graph Neural Network Computation Via Dense Tensor Core on GPUs. (arXiv:2112.02052v2 [cs.LG] UPDATED)
    Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance of GNNs is usually unsatisfactory due to the highly sparse and irregular graph-based operations. To this end, we propose, TC-GNN, the first GPU Tensor Core Unit (TCU) based GNN acceleration framework. The core idea is to reconcile the "Sparse" GNN computation with "Dense" TCU. Specifically, we conduct an in-depth analysis of the sparse operations in mainstream GNN computing frameworks. We introduce a novel sparse graph translation technique to facilitate TCU processing of sparse GNN workload. We also implement an effective CUDA core and TCU collaboration design to fully utilize GPU resources. We fully integrate TC-GNN with the Pytorch framework for ease of programming. Rigorous experiments show an average of 1.70X speedup over the state-of-the-art Deep Graph Library framework across various GNN models and dataset settings.
    An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation. (arXiv:2301.01224v1 [cs.SE])
    Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.
    Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data. (arXiv:2301.01252v1 [physics.ao-ph])
    Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that they do not agree with ground-based measurements. An established means for improving their accuracy is to correct them by adopting machine learning algorithms. The problem is defined as a regression setting, in which the ground-based measurements have the role of the dependent variable and the satellite data are the predictor variables, together with topography factors (e.g., elevation). Most studies of this kind involve a limited number of machine learning algorithms, and are conducted at a small region and for a limited time period. Thus, the results obtained through them are of local importance and do not provide more general guidance and best practices. To provide results that are generalizable and to contribute to the delivery of best practices, we here compare eight state-of-the-art machine learning algorithms in correcting satellite precipitation data for the entire contiguous United States and for a 15-year period. We use monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) gridded dataset, together with monthly earth-observed precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The results suggest that extreme gradient boosting (XGBoost) and random forests are the most accurate in terms of the squared error scoring function. The remaining algorithms can be ordered as follows from the best to the worst ones: Bayesian regularized feed-forward neural networks, multivariate adaptive polynomial splines (poly-MARS), gradient boosting machines (gbm), multivariate adaptive regression splines (MARS), feed-forward neural networks and linear regression.
    On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. (arXiv:2106.08414v2 [cs.LG] UPDATED)
    Reinforcement learning is a framework for interactive decision-making with incentives sequentially revealed across time without a system dynamics model. Due to its scaling to continuous spaces, we focus on policy search where one iteratively improves a parameterized policy with stochastic policy gradient (PG) updates. In tabular Markov Decision Problems (MDPs), under persistent exploration and suitable parameterization, global optimality may be obtained. By contrast, in continuous space, the non-convexity poses a pathological challenge as evidenced by existing convergence results being mostly limited to stationarity or arbitrary local extrema. To close this gap, we step towards persistent exploration in continuous space through policy parameterizations defined by distributions of heavier tails defined by tail-index parameter alpha, which increases the likelihood of jumping in state space. Doing so invalidates smoothness conditions of the score function common to PG. Thus, we establish how the convergence rate to stationarity depends on the policy's tail index alpha, a Holder continuity parameter, integrability conditions, and an exploration tolerance parameter introduced here for the first time. Further, we characterize the dependence of the set of local maxima on the tail index through an exit and transition time analysis of a suitably defined Markov chain, identifying that policies associated with Levy Processes of a heavier tail converge to wider peaks. This phenomenon yields improved stability to perturbations in supervised learning, which we corroborate also manifests in improved performance of policy search, especially when myopic and farsighted incentives are misaligned.
    Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale. (arXiv:2301.01214v1 [cs.LG])
    Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavour. At the same time, tree-based ensemble algorithms for regression are adopted in various fields for solving algorithmic problems with high accuracy and low computational cost. The latter can constitute a crucial factor for selecting algorithms for satellite precipitation product correction at the daily and finer time scales, where the size of the datasets is particularly large. Still, information on which tree-based ensemble algorithm to select in such a case for the contiguous United States (US) is missing from the literature. In this work, we conduct an extensive comparison between three tree-based ensemble algorithms, specifically random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost), in the context of interest. We use daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also use earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database. The experiments refer to the entire contiguous US and additionally include the application of the linear regression algorithm for benchmarking purposes. The results suggest that XGBoost is the best-performing tree-based ensemble algorithm among those compared. They also suggest that IMERG is more useful than PERSIANN in the context investigated.
    Estimating Categorical Counterfactuals via Deep Twin Networks. (arXiv:2109.01904v5 [cs.LG] UPDATED)
    Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises the question of how to choose the causal mechanisms so that resulting counterfactual inference is trustworthy in a given domain. This question has been addressed in causal models with binary variables, but the case of categorical variables remains unanswered. We address this challenge by introducing for causal models with categorical variables the notion of counterfactual ordering, a principle that posits desirable properties causal mechanisms should posses, and prove that it is equivalent to specific functional constraints on the causal mechanisms. To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks. These are deep neural networks that, when trained, are capable of twin network counterfactual inference -- an alternative to the abduction, action, & prediction method. We empirically test our approach on diverse real-world and semi-synthetic data from medicine, epidemiology, and finance, reporting accurate estimation of counterfactual probabilities while demonstrating the issues that arise with counterfactual reasoning when counterfactual ordering is not enforced.
    Deep Learning for bias-correcting comprehensive high-resolution Earth system models. (arXiv:2301.01253v1 [physics.ao-ph])
    The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross-scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State-of-the-art bias correction methods only address errors in the simulated frequency distributions locally, at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a post-processing method based on physically constrained generative adversarial networks (GANs) can correct biases of a state-of-the-art, CMIP6-class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold-standard bias-adjustment frameworks it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.
    Understanding Imbalanced Semantic Segmentation Through Neural Collapse. (arXiv:2301.01100v1 [cs.CV])
    A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.
    DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation. (arXiv:2301.01202v1 [cs.CV])
    Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images, and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.  ( 2 min )
    Mutual Information Regularization for Vertical Federated Learning. (arXiv:2301.01142v1 [cs.LG])
    Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable collaborative learning while protecting data privacy and safety. However, previous works show that parties without labels (passive parties) in VFL can infer the sensitive label information owned by the party with labels (active party) or execute backdoor attacks to VFL. Meanwhile, active party can also infer sensitive feature information from passive party. All these pose new privacy and security challenges to VFL systems. We propose a new general defense method which limits the mutual information between private raw data, including both features and labels, and intermediate outputs to achieve a better trade-off between model utility and privacy. We term this defense Mutual Information Regularization Defense (MID). We theoretically and experimentally testify the effectiveness of our MID method in defending existing attacks in VFL, including label inference attacks, backdoor attacks and feature reconstruction attacks.  ( 2 min )
    Backdoor Attacks Against Dataset Distillation. (arXiv:2301.01197v1 [cs.CR])
    Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.  ( 2 min )
    A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge. (arXiv:2301.01172v1 [cs.CL])
    Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.  ( 2 min )
    Speed up the inference of diffusion models via shortcut MCMC sampling. (arXiv:2301.01206v1 [cs.CV])
    Diffusion probabilistic models have generated high quality image synthesis recently. However, one pain point is the notorious inference to gradually obtain clear images with thousands of steps, which is time consuming compared to other generative models. In this paper, we present a shortcut MCMC sampling algorithm, which balances training and inference, while keeping the generated data's quality. In particular, we add the global fidelity constraint with shortcut MCMC sampling to combat the local fitting from diffusion models. We do some initial experiments and show very promising results. Our implementation is available at https://github.com//vividitytech/diffusion-mcmc.git.  ( 2 min )
    Conservation Tools: The Next Generation of Engineering--Biology Collaborations. (arXiv:2301.01103v1 [q-bio.QM])
    The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.  ( 2 min )
    Cluster-guided Contrastive Graph Clustering Network. (arXiv:2301.01098v1 [cs.LG])
    Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.  ( 2 min )
    MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn. (arXiv:2301.01148v1 [cs.LG])
    The decarbonization of buildings presents new challenges for the reliability of the electrical grid as a result of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response. Residential demand response programs are hindered by the need for manual intervention by customers. To maximize the energy flexibility potential of residential buildings, an advanced control architecture is needed. Reinforcement learning is well-suited for the control of flexible resources as it is able to adapt to unique building characteristics compared to expert systems. Yet, factors hindering the adoption of RL in real-world applications include its large data requirements for training, control security and generalizability. Here we address these challenges by proposing the MERLIN framework and using a digital twin of a real-world 17-building grid-interactive residential community in CityLearn. We show that 1) independent RL-controllers for batteries improve building and district level KPIs compared to a reference RBC by tailoring their policies to individual buildings, 2) despite unique occupant behaviours, transferring the RL policy of any one of the buildings to other buildings provides comparable performance while reducing the cost of training, 3) training RL-controllers on limited temporal data that does not capture full seasonality in occupant behaviour has little effect on performance. Although, the zero-net-energy (ZNE) condition of the buildings could be maintained or worsened as a result of controlled batteries, KPIs that are typically improved by ZNE condition (electricity price and carbon emissions) are further improved when the batteries are managed by an advanced controller.  ( 2 min )
    Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning. (arXiv:2301.01113v1 [cs.SE])
    In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.  ( 2 min )
    Boosting Neural Networks to Decompile Optimized Binaries. (arXiv:2301.00969v1 [cs.LG])
    Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.  ( 2 min )
    Computing the Performance of A New Adaptive Sampling Algorithm Based on The Gittins Index in Experiments with Exponential Rewards. (arXiv:2301.01107v1 [stat.CO])
    Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.  ( 2 min )
    Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise. (arXiv:2301.01054v1 [eess.IV])
    In the past years, deep learning has seen an increase of usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their own uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole-Slide-Images under domain shift using the H\&E stained Camelyon17 breast cancer dataset. Although it is known that histopathological data can be subject to strong domain shift and label noise, to our knowledge this is the first work that compares the most common methods for uncertainty estimation under these aspects. In our experiments, we compare Stochastic Variational Inference, Monte-Carlo Dropout, Deep Ensembles, Test-Time Data Augmentation as well as combinations thereof. We observe that ensembles of methods generally lead to higher accuracies and better calibration and that Test-Time Data Augmentation can be a promising alternative when choosing an appropriate set of augmentations. Across methods, a rejection of the most uncertain tiles leads to a significant increase in classification accuracy on both in-distribution as well as out-of-distribution data. Furthermore, we conduct experiments comparing these methods under varying conditions of label noise. We observe that the border regions of the Camelyon17 dataset are subject to label noise and evaluate the robustness of the included methods against different noise levels. Lastly, we publish our code framework to facilitate further research on uncertainty estimation on histopathological data.  ( 2 min )
    RELIANT: Fair Knowledge Distillation for Graph Neural Networks. (arXiv:2301.01150v1 [cs.LG])
    Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.  ( 2 min )
    Vocabulary-informed Zero-shot and Open-set Learning. (arXiv:2301.00998v1 [cs.CV])
    Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.  ( 2 min )
    xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions. (arXiv:2301.01089v1 [cs.LG])
    Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep neural networks (DNN)-based approaches to learn the feature interactions in an end-to-end fashion. However, existing methods either do not learn both vector-wise interactions and bit-wise interactions simultaneously, or fail to combine them in a controllable manner. In this paper, we propose a new model, xDeepInt, based on a novel network architecture called polynomial interaction network (PIN) which learns higher-order vector-wise interactions recursively. By integrating subspace-crossing mechanism, we enable xDeepInt to balance the mixture of vector-wise and bit-wise feature interactions at a bounded order. Based on the network architecture, we customize a combined optimization strategy to conduct feature selection and interaction selection. We implement the proposed model and evaluate the model performance on three real-world datasets. Our experiment results demonstrate the efficacy and effectiveness of xDeepInt over state-of-the-art models. We open-source the TensorFlow implementation of xDeepInt: https://github.com/yanyachen/xDeepInt.  ( 2 min )
    On the causality-preservation capabilities of generative modelling. (arXiv:2301.01109v1 [cs.LG])
    Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.  ( 2 min )
    A Theory of I/O-Efficient Sparse Neural Network Inference. (arXiv:2301.01048v1 [cs.DC])
    As the accuracy of machine learning models increases at a fast rate, so does their demand for energy and compute resources. On a low level, the major part of these resources is consumed by data movement between different memory units. Modern hardware architectures contain a form of fast memory (e.g., cache, registers), which is small, and a slow memory (e.g., DRAM), which is larger but expensive to access. We can only process data that is stored in fast memory, which incurs data movement (input/output-operations, or I/Os) between the two units. In this paper, we provide a rigorous theoretical analysis of the I/Os needed in sparse feedforward neural network (FFNN) inference. We establish bounds that determine the optimal number of I/Os up to a factor of 2 and present a method that uses a number of I/Os within that range. Much of the I/O-complexity is determined by a few high-level properties of the FFNN (number of inputs, outputs, neurons, and connections), but if we want to get closer to the exact lower bound, the instance-specific sparsity patterns need to be considered. Departing from the 2-optimal computation strategy, we show how to reduce the number of I/Os further with simulated annealing. Complementing this result, we provide an algorithm that constructively generates networks with maximum I/O-efficiency for inference. We test the algorithms and empirically verify our theoretical and algorithmic contributions. In our experiments on real hardware we observe speedups of up to 45$\times$ relative to the standard way of performing inference.  ( 2 min )
    KoopmanLab: A PyTorch module of Koopman neural operator family for solving partial differential equations. (arXiv:2301.01104v1 [cs.LG])
    Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.  ( 2 min )
    Uncertainty in Real-Time Semantic Segmentation on Embedded Systems. (arXiv:2301.01201v1 [cs.CV])
    Application for semantic segmentation models in areas such as autonomous vehicles and human computer interaction require real-time predictive capabilities. The challenges of addressing real-time application is amplified by the need to operate on resource constrained hardware. Whilst development of real-time methods for these platforms has increased, these models are unable to sufficiently reason about uncertainty present. This paper addresses this by combining deep feature extraction from pre-trained models with Bayesian regression and moment propagation for uncertainty aware predictions. We demonstrate how the proposed method can yield meaningful uncertainty on embedded hardware in real-time whilst maintaining predictive performance.  ( 2 min )
    Deep R Programming. (arXiv:2301.01188v1 [cs.PL])
    Deep R Programming is a comprehensive course on one of the most popular languages in data science (statistical computing, graphics, machine learning, data wrangling and analytics). It introduces the base language in-depth and is aimed at ambitious students, practitioners, and researchers who would like to become independent users of this powerful environment. This textbook is a non-profit project. Its online and PDF versions are freely available at . This early draft is distributed in the hope that it will be useful.  ( 2 min )
    Risk-Averse MDPs under Reward Ambiguity. (arXiv:2301.01045v1 [cs.LG])
    We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.  ( 2 min )
    Effective and Efficient Training for Sequential Recommendation Using Cumulative Cross-Entropy Loss. (arXiv:2301.00979v1 [cs.IR])
    Increasing research interests focus on sequential recommender systems, aiming to model dynamic sequence representation precisely. However, the most commonly used loss function in state-of-the-art sequential recommendation models has essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss suffers the vanishing gradient problem from numerous negative sampling and predictionbiases; Binary Cross-Entropy (BCE) loss subjects to negative sampling numbers, thereby it is likely to ignore valuable negative examples and reduce the training efficiency; Cross-Entropy (CE) loss only focuses on the last timestamp of the training sequence, which causes low utilization of sequence information and results in inferior user sequence representation. To avoid these limitations, in this paper, we propose to calculate Cumulative Cross-Entropy (CCE) loss over the sequence. CCE is simple and direct, which enjoys the virtues of painless deployment, no negative sampling, and effective and efficient training. We conduct extensive experiments on five benchmark datasets to demonstrate the effectiveness and efficiency of CCE. The results show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec, and S3-Rec can reach 125.63%, 69.90%, and 33.24% average improvement of full ranking NDCG@5, respectively. Using CCE, the performance curve of the models on the test data increases rapidly with the wall clock time, and is superior to that of other loss functions in almost the whole process of model training.  ( 2 min )
    Continual Treatment Effect Estimation: Challenges and Opportunities. (arXiv:2301.01026v1 [cs.LG])
    A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.  ( 2 min )
    Dissecting Continual Learning a Structural and Data Analysis. (arXiv:2301.01033v1 [cs.CV])
    Continual Learning (CL) is a field dedicated to devise algorithms able to achieve lifelong learning. Overcoming the knowledge disruption of previously acquired concepts, a drawback affecting deep learning models and that goes by the name of catastrophic forgetting, is a hard challenge. Currently, deep learning methods can attain impressive results when the data modeled does not undergo a considerable distributional shift in subsequent learning sessions, but whenever we expose such systems to this incremental setting, performance drop very quickly. Overcoming this limitation is fundamental as it would allow us to build truly intelligent systems showing stability and plasticity. Secondly, it would allow us to overcome the onerous limitation of retraining these architectures from scratch with the new updated data. In this thesis, we tackle the problem from multiple directions. In a first study, we show that in rehearsal-based techniques (systems that use memory buffer), the quantity of data stored in the rehearsal buffer is a more important factor over the quality of the data. Secondly, we propose one of the early works of incremental learning on ViTs architectures, comparing functional, weight and attention regularization approaches and propose effective novel a novel asymmetric loss. At the end we conclude with a study on pretraining and how it affects the performance in Continual Learning, raising some questions about the effective progression of the field. We then conclude with some future directions and closing remarks.  ( 2 min )
    Through-life Monitoring of Resource-constrained Systems and Fleets. (arXiv:2301.01017v1 [cs.LG])
    A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.  ( 2 min )
    A Theory of Human-Like Few-Shot Learning. (arXiv:2301.01047v1 [cs.LG])
    We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.  ( 2 min )
    Meta-learning generalizable dynamics from trajectories. (arXiv:2301.00957v1 [cs.LG])
    We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.  ( 2 min )
    e-Inu: Simulating A Quadruped Robot With Emotional Sentience. (arXiv:2301.00964v1 [cs.RO])
    Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work.  ( 2 min )
    Data Valuation Without Training of a Model. (arXiv:2301.00930v1 [cs.LG])
    Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model, either by analyzing the behavior of the model during training or by measuring the performance gap of the model when the instance is removed from the dataset. Such approaches reveal characteristics and importance of individual instances, which may provide useful information in diagnosing and improving deep learning. However, most of the existing works on data valuation require actual training of a model, which often demands high-computational cost. In this paper, we provide a training-free data valuation score, called complexity-gap score, which is a data-centric score to quantify the influence of individual instances in generalization of two-layer overparameterized neural networks. The proposed score can quantify irregularity of the instances and measure how much each data instance contributes in the total movement of the network parameters during training. We theoretically analyze and empirically demonstrate the effectiveness of the complexity-gap score in finding 'irregular or mislabeled' data instances, and also provide applications of the score in analyzing datasets and diagnosing training dynamics.  ( 2 min )
    Improving Performance in Neural Networks by Dendrites-Activated Connections. (arXiv:2301.00924v1 [cs.NE])
    Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.  ( 2 min )
    Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition. (arXiv:2301.00986v1 [cs.CV])
    Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker is interested in exploiting. A backdoored DNN performs well on clean test images, yet persistently predicts an attacker-defined label for any sample in the presence of the backdoor trigger. Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain. In this work, we revisit the traditional backdoor threat model and incorporate additional video-related aspects to that model. We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically, leading to highly effective attacks in the video domain. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models, where we show that attacking a single modality is enough for achieving a high attack success rate.  ( 2 min )
    Deep Spectral Q-learning with Application to Mobile Health. (arXiv:2301.00927v1 [stat.ML])
    Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.  ( 2 min )
    Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System. (arXiv:2301.00904v1 [cs.RO])
    Reinforcement learning (RL)-based driver assistance systems seek to improve fuel consumption via continual improvement of powertrain control actions considering experiential data from the field. However, the need to explore diverse experiences in order to learn optimal policies often limits the application of RL techniques in safety-critical systems like vehicle control. In this paper, an exponential control barrier function (ECBF) is derived and utilized to filter unsafe actions proposed by an RL-based driver assistance system. The RL agent freely explores and optimizes the performance objectives while unsafe actions are projected to the closest actions in the safe domain. The reward is structured so that driver's acceleration requests are met in a manner that boosts fuel economy and doesn't compromise comfort. The optimal gear and traction torque control actions that maximize the cumulative reward are computed via the Maximum a Posteriori Policy Optimization (MPO) algorithm configured for a hybrid action space. The proposed safe-RL scheme is trained and evaluated in car following scenarios where it is shown that it effectively avoids collision both during training and evaluation while delivering on the expected fuel economy improvements for the driver assistance system.  ( 2 min )
    Exploring Complex Dynamical Systems via Nonconvex Optimization. (arXiv:2301.00923v1 [cs.LG])
    Cataloging the complex behaviors of dynamical systems can be challenging, even when they are well-described by a simple mechanistic model. If such a system is of limited analytical tractability, brute force simulation is often the only resort. We present an alternative, optimization-driven approach using tools from machine learning. We apply this approach to a novel, fully-optimizable, reaction-diffusion model which incorporates complex chemical reaction networks (termed "Dense Reaction-Diffusion Network" or "Dense RDN"). This allows us to systematically identify new states and behaviors, including pattern formation, dissipation-maximizing nonequilibrium states, and replication-like dynamical structures.  ( 2 min )
    Ranking Differential Privacy. (arXiv:2301.00841v1 [stat.ML])
    Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.  ( 2 min )
    Faster Approximate Dynamic Programming by Freezing Slow States. (arXiv:2301.00922v1 [cs.AI])
    We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly." Such structure is common in real-world problems where sequential decisions need to be made at high frequencies, yet information that varies at a slower timescale also influences the optimal policy. Examples include: (1) service allocation for a multi-class queue with (slowly varying) stochastic costs, (2) a restless multi-armed bandit with an environmental state, and (3) energy demand response, where both day-ahead and real-time prices play a role in the firm's revenue. Models that fully capture these problems often result in MDPs with large state spaces and large effective time horizons (due to frequent decisions), rendering them computationally intractable. We propose an approximate dynamic programming algorithmic framework based on the idea of "freezing" the slow states, solving a set of simpler finite-horizon MDPs (the lower-level MDPs), and applying value iteration (VI) to an auxiliary MDP that transitions on a slower timescale (the upper-level MDP). We also extend the technique to a function approximation setting, where a feature-based linear architecture is used. On the theoretical side, we analyze the regret incurred by each variant of our frozen-state approach. Finally, we give empirical evidence that the frozen-state approach generates effective policies using just a fraction of the computational cost, while illustrating that simply omitting slow states from the decision modeling is often not a viable heuristic.  ( 2 min )
    Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback. (arXiv:2301.00899v1 [cs.LG])
    Deep reinforcement learning has considerable potential to improve irrigation scheduling in many cropping systems by applying adaptive amounts of water based on various measurements over time. The goal is to discover an intelligent decision rule that processes information available to growers and prescribes sensible irrigation amounts for the time steps considered. Due to the technical novelty, however, the research on the technique remains sparse and impractical. To accelerate the progress, the paper proposes a general framework and actionable procedure that allow researchers to formulate their own optimisation problems and implement solution algorithms based on deep reinforcement learning. The effectiveness of the framework was demonstrated using a case study of irrigated wheat grown in a productive region of Australia where profits were maximised. Specifically, the decision rule takes nine state variable inputs: crop phenological stage, leaf area index, extractable soil water for each of the five top layers, cumulative rainfall and cumulative irrigation. It returns a probabilistic prescription over five candidate irrigation amounts (0, 10, 20, 30 and 40 mm) every day. The production system was simulated at Goondiwindi using the APSIM-Wheat crop model. After training in the learning environment using 1981--2010 weather data, the learned decision rule was tested individually for each year of 2011--2020. The results were compared against the benchmark profits obtained using irrigation schedules optimised individually for each of the considered years. The discovered decision rule prescribed daily irrigation amounts that achieved more than 96% of the benchmark profits. The framework is general and applicable to a wide range of cropping systems with realistic optimisation problems.  ( 2 min )
    Deep Learning and Computational Physics (Lecture Notes). (arXiv:2301.00942v1 [cs.LG])
    These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools.  ( 2 min )
    One-shot domain adaptation in video-based assessment of surgical skills. (arXiv:2301.00812v1 [cs.CV])
    Deep Learning (DL) has achieved automatic and objective assessment of surgical skills. However, DL models are data-hungry and restricted to their training domain. This prevents them from transitioning to new tasks where data is limited. Hence, domain adaptation is crucial to implement DL in real life. Here, we propose a meta-learning model, A-VBANet, that can deliver domain-agnostic surgical skill classification via one-shot learning. We develop the A-VBANet on five laparoscopic and robotic surgical simulators. Additionally, we test it on operating room (OR) videos of laparoscopic cholecystectomy. Our model successfully adapts with accuracies up to 99.5% in one-shot and 99.9% in few-shot settings for simulated tasks and 89.7% for laparoscopic cholecystectomy. For the first time, we provide a domain-agnostic procedure for video-based assessment of surgical skills. A significant implication of this approach is that it allows the use of data from surgical simulators to assess performance in the operating room.  ( 2 min )
    OF-AE: Oblique Forest AutoEncoders. (arXiv:2301.00880v1 [cs.LG])
    In the present work we propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, namely Oblique Forest AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest encoder introduced in [1]. More precisely, by employing oblique splits consisting in multivariate linear combination of features instead of the axis-parallel ones, we will devise an auto-encoder method through the computation of a sparse solution of a set of linear inequalities consisting of feature values constraints. The code for reproducing our results is available at https://github.com/CDAlecsa/Oblique-Forest-AutoEncoders.  ( 2 min )
    Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos. (arXiv:2301.00896v1 [cs.CV])
    Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same.  ( 2 min )
    Multidimensional Item Response Theory in the Style of Collaborative Filtering. (arXiv:2301.00909v1 [stat.ML])
    This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood (JML) to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course (MOOC). The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative "validation" of the factor model, using auxiliary information about the popularity of items consulted during an open-book exam in the course.  ( 2 min )
    A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecasting. (arXiv:2301.00819v1 [cs.LG])
    Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract spatial and temporal information from multi-dimensional input data to make day-ahead predictions. In this regard, our method incorporates an ultra-wide learning view, combining data from multiple numerical weather prediction models, wind farms, and geographical locations. Additionally, we experiment with global forecasting approaches to understand the impact of training the same model over the datasets obtained from multiple different wind farms, and we employ a method where spatial information extracted from convolutional layers is passed to a tree ensemble (e.g., Light Gradient Boosting Machine (LGBM)) instead of fully connected layers. The results show that our proposed CNN-RNN architecture outperforms other models such as LGBM, Extra Tree regressor and linear regression when trained globally, but fails to replicate such performance when trained individually on each farm. We also observe that passing the spatial information from CNN to LGBM improves its performance, providing further evidence of CNN's spatial feature extraction capabilities.  ( 2 min )
    Tweet's popularity dynamics. (arXiv:2301.00853v1 [cs.LG])
    This article charts the work of a 4 month project aimed at automatically identifying patterns of tweets popularity evolution using Machine Learning and Deep Learning techniques. To apprehend both the data and the extent of the problem, a straightforward clustering algorithm based on a point to point distance is used. Then, in an attempt to refine the algorithm, various analyses especially using feature extraction techniques are conducted. Although the algorithm eventually fails to automate such a task, this exercise raises a complex but necessary issue touching on the impact of virality on social networks.  ( 2 min )
    Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics. (arXiv:2301.00912v1 [cs.LG])
    Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.  ( 2 min )
    NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants. (arXiv:2301.00815v1 [cs.LG])
    Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.  ( 2 min )
    Robust Average-Reward Markov Decision Processes. (arXiv:2301.00858v1 [cs.LG])
    In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor $\gamma$ goes to $1$, and moreover, when $\gamma$ is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.  ( 2 min )
    A Survey on Protein Representation Learning: Retrospect and Prospect. (arXiv:2301.00813v1 [cs.LG])
    Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.  ( 2 min )
  • Open

    Adaptive Sampling for Discovery. (arXiv:2205.14829v3 [stat.ML] UPDATED)
    In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses. This problem has wide applications to real-world discovery problems, for example drug discovery with the help of machine learning models. ASD algorithms face the well-known exploration-exploitation dilemma. The algorithm needs to choose points that yield information to improve model estimates but it also needs to exploit the model. We rigorously formulate the problem and propose a general information-directed sampling (IDS) algorithm. We provide theoretical guarantees for the performance of IDS in linear, graph and low-rank models. The benefits of IDS are shown in both simulation experiments and real-data experiments for discovering chemical reaction conditions.  ( 2 min )
    Offline Reinforcement Learning with Differential Privacy. (arXiv:2206.00810v2 [cs.LG] UPDATED)
    The offline reinforcement learning (RL) problem is often motivated by the need to learn data-driven decision policies in financial, legal and healthcare applications. However, the learned policy could retain sensitive information of individuals in the training data (e.g., treatment and outcome of patients), thus susceptible to various privacy risks. We design offline RL algorithms with differential privacy guarantees which provably prevent such risks. These algorithms also enjoy strong instance-dependent learning bounds under both tabular and linear Markov decision process (MDP) settings. Our theory and simulation suggest that the privacy guarantee comes at (almost) no drop in utility comparing to the non-private counterpart for a medium-size dataset.  ( 2 min )
    A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits. (arXiv:2111.12550v2 [cs.HC] UPDATED)
    Crowdsourcing system has emerged as an effective platform for labeling data with relatively low cost by using non-expert workers. Inferring correct labels from multiple noisy answers on data, however, has been a challenging problem, since the quality of the answers varies widely across tasks and workers. Many existing works have assumed that there is a fixed ordering of workers in terms of their skill levels, and focused on estimating worker skills to aggregate the answers from workers with different weights. In practice, however, the worker skill changes widely across tasks, especially when the tasks are heterogeneous. In this paper, we consider a new model, called $d$-type specialization model, in which each task and worker has its own (unknown) type and the reliability of each worker can vary in the type of a given task and that of a worker. We allow that the number $d$ of types can scale in the number of tasks. In this model, we characterize the optimal sample complexity to correctly infer the labels within any given accuracy, and propose label inference algorithms achieving the order-wise optimal limit even when the types of tasks or those of workers are unknown. We conduct experiments both on synthetic and real datasets, and show that our algorithm outperforms the existing algorithms developed based on more strict model assumptions.  ( 2 min )
    Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles. (arXiv:2110.12067v2 [stat.ML] UPDATED)
    Gaussian graphical models provide a powerful framework for uncovering conditional dependence relationships between sets of nodes; they have found applications in a wide variety of fields including sensor and communication networks, physics, finance, and computational biology. Often, one observes data on the nodes and the task is to learn the graph structure, or perform graphical model selection. While this is a well-studied problem with many popular techniques, there are typically three major practical challenges: i) many existing algorithms become computationally intractable in huge-data settings with tens of thousands of nodes; ii) the need for separate data-driven hyperparameter tuning considerably adds to the computational burden; iii) the statistical accuracy of selected edges often deteriorates as the dimension and/or the complexity of the underlying graph structures increase. We tackle these problems by developing the novel Minipatch Graph (MPGraph) estimator. Our approach breaks up the huge graph learning problem into many smaller problems by creating an ensemble of tiny random subsets of both the observations and the nodes, termed minipatches. We then leverage recent advances that use hard thresholding to solve the latent variable graphical model problem to consistently learn the graph on each minipatch. Our approach is computationally fast, embarrassingly parallelizable, memory efficient, and has integrated stability-based hyperparamter tuning. Additionally, we prove that under weaker assumptions than that of the Graphical Lasso, our MPGraph estimator achieves graph selection consistency. We compare our approach to state-of-the-art computational approaches for Gaussian graphical model selection including the BigQUIC algorithm, and empirically demonstrate that our approach is not only more statistically accurate but also extensively faster for huge graph learning problems.  ( 3 min )
    On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. (arXiv:2106.08414v2 [cs.LG] UPDATED)
    Reinforcement learning is a framework for interactive decision-making with incentives sequentially revealed across time without a system dynamics model. Due to its scaling to continuous spaces, we focus on policy search where one iteratively improves a parameterized policy with stochastic policy gradient (PG) updates. In tabular Markov Decision Problems (MDPs), under persistent exploration and suitable parameterization, global optimality may be obtained. By contrast, in continuous space, the non-convexity poses a pathological challenge as evidenced by existing convergence results being mostly limited to stationarity or arbitrary local extrema. To close this gap, we step towards persistent exploration in continuous space through policy parameterizations defined by distributions of heavier tails defined by tail-index parameter alpha, which increases the likelihood of jumping in state space. Doing so invalidates smoothness conditions of the score function common to PG. Thus, we establish how the convergence rate to stationarity depends on the policy's tail index alpha, a Holder continuity parameter, integrability conditions, and an exploration tolerance parameter introduced here for the first time. Further, we characterize the dependence of the set of local maxima on the tail index through an exit and transition time analysis of a suitably defined Markov chain, identifying that policies associated with Levy Processes of a heavier tail converge to wider peaks. This phenomenon yields improved stability to perturbations in supervised learning, which we corroborate also manifests in improved performance of policy search, especially when myopic and farsighted incentives are misaligned.  ( 2 min )
    xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions. (arXiv:2301.01089v1 [cs.LG])
    Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep neural networks (DNN)-based approaches to learn the feature interactions in an end-to-end fashion. However, existing methods either do not learn both vector-wise interactions and bit-wise interactions simultaneously, or fail to combine them in a controllable manner. In this paper, we propose a new model, xDeepInt, based on a novel network architecture called polynomial interaction network (PIN) which learns higher-order vector-wise interactions recursively. By integrating subspace-crossing mechanism, we enable xDeepInt to balance the mixture of vector-wise and bit-wise feature interactions at a bounded order. Based on the network architecture, we customize a combined optimization strategy to conduct feature selection and interaction selection. We implement the proposed model and evaluate the model performance on three real-world datasets. Our experiment results demonstrate the efficacy and effectiveness of xDeepInt over state-of-the-art models. We open-source the TensorFlow implementation of xDeepInt: https://github.com/yanyachen/xDeepInt.  ( 2 min )
    Dimension-agnostic inference using cross U-statistics. (arXiv:2011.05068v5 [math.ST] UPDATED)
    Classical asymptotic theory for statistical inference usually involves calibrating a statistic by fixing the dimension $d$ while letting the sample size $n$ increase to infinity. Recently, much effort has been dedicated towards understanding how these methods behave in high-dimensional settings, where $d$ and $n$ both increase to infinity together. This often leads to different inference procedures, depending on the assumptions about the dimensionality, leaving the practitioner in a bind: given a dataset with 100 samples in 20 dimensions, should they calibrate by assuming $n \gg d$, or $d/n \approx 0.2$? This paper considers the goal of dimension-agnostic inference; developing methods whose validity does not depend on any assumption on $d$ versus $n$. We introduce an approach that uses variational representations of existing test statistics along with sample splitting and self-normalization to produce a new test statistic with a Gaussian limiting distribution, regardless of how $d$ scales with $n$. The resulting statistic can be viewed as a careful modification of degenerate U-statistics, dropping diagonal blocks and retaining off-diagonal blocks. We exemplify our technique for some classical problems including one-sample mean and covariance testing, and show that our tests have minimax rate-optimal power against appropriate local alternatives. In most settings, our cross U-statistic matches the high-dimensional power of the corresponding (degenerate) U-statistic up to a $\sqrt{2}$ factor.  ( 2 min )
    Linear chain conditional random fields, hidden Markov models, and related classifiers. (arXiv:2301.01293v1 [stat.ML])
    Practitioners use Hidden Markov Models (HMMs) in different problems for about sixty years. Besides, Conditional Random Fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose two contributions. First, we show that basic Linear-Chain CRFs (LC-CRFs), considered as different from the HMMs, are in fact equivalent to them in the sense that for each LC-CRF there exists a HMM - that we specify - whom posterior distribution is identical to the given LC-CRF. Second, we show that it is possible to reformulate the generative Bayesian classifiers Maximum Posterior Mode (MPM) and Maximum a Posteriori (MAP) used in HMMs, as discriminative ones. The last point is of importance in many fields, especially in Natural Language Processing (NLP), as it shows that in some situations dropping HMMs in favor of CRFs was not necessary.  ( 2 min )
    A Tutorial on Parametric Variational Inference. (arXiv:2301.01236v1 [stat.ML])
    Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.  ( 2 min )
    Deep Spectral Q-learning with Application to Mobile Health. (arXiv:2301.00927v1 [stat.ML])
    Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.  ( 2 min )
    Optimal transport with $f$-divergence regularization and generalized Sinkhorn algorithm. (arXiv:2105.14337v2 [math.OC] UPDATED)
    Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general $f$-divergence leads to a natural generalization. The case of divergences defined by superlinear functions was recently studied by Di Marino and Gerolin. Using convex analysis, we extend the theory developed so far to include all $f$-divergences defined by functions of Legendre type, and prove that under some mild conditions, strong duality holds, optimums in both the primal and dual problems are attained, the generalization of the $c$-transform is well-defined, and we give sufficient conditions for the generalized Sinkhorn algorithm to converge to an optimal solution. We propose a practical algorithm for computing an approximate solution of the optimal transport problem with $f$-divergence regularization via the generalized Sinkhorn algorithm. Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different $f$-divergences for regularization, which influences convergence speed, numerical stability and sparsity of the optimal coupling.  ( 2 min )
    Continual Treatment Effect Estimation: Challenges and Opportunities. (arXiv:2301.01026v1 [cs.LG])
    A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.  ( 2 min )
    Multidimensional Item Response Theory in the Style of Collaborative Filtering. (arXiv:2301.00909v1 [stat.ML])
    This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood (JML) to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course (MOOC). The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative "validation" of the factor model, using auxiliary information about the popularity of items consulted during an open-book exam in the course.  ( 2 min )
    State and parameter learning with PaRIS particle Gibbs. (arXiv:2301.00900v1 [stat.ME])
    Non-linear state-space models, also known as general hidden Markov models, are ubiquitous in statistical machine learning, being the most classical generative models for serial data and sequences in general. The particle-based, rapid incremental smoother PaRIS is a sequential Monte Carlo (SMC) technique allowing for efficient online approximation of expectations of additive functionals under the smoothing distribution in these models. Such expectations appear naturally in several learning contexts, such as likelihood estimation (MLE) and Markov score climbing (MSC). PARIS has linear computational complexity, limited memory requirements and comes with non-asymptotic bounds, convergence results and stability guarantees. Still, being based on self-normalised importance sampling, the PaRIS estimator is biased. Our first contribution is to design a novel additive smoothing algorithm, the Parisian particle Gibbs PPG sampler, which can be viewed as a PaRIS algorithm driven by conditional SMC moves, resulting in bias-reduced estimates of the targeted quantities. We substantiate the PPG algorithm with theoretical results, including new bounds on bias and variance as well as deviation inequalities. Our second contribution is to apply PPG in a learning framework, covering MLE and MSC as special examples. In this context, we establish, under standard assumptions, non-asymptotic bounds highlighting the value of bias reduction and the implicit Rao--Blackwellization of PPG. These are the first non-asymptotic results of this kind in this setting. We illustrate our theoretical results with numerical experiments supporting our claims.  ( 2 min )
    Ranking Differential Privacy. (arXiv:2301.00841v1 [stat.ML])
    Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.  ( 2 min )

  • Open

    How can insurance companies leverage Generative AI?
    submitted by /u/u_dev_92 [link] [comments]  ( 52 min )
    Robo Birds from an 80ies Innovation Lab (ProtogenX34)
    submitted by /u/tebjan [link] [comments]  ( 52 min )
    The equivalent of Midjourney or Dall-E for MUSIC appears : what are your prompts ?
    Let's say an insanely powerful and high res music AI is released. It can create absolutely whatever you want. What would you ask it ? What would be your prompts ? submitted by /u/paulbismuth31 [link] [comments]  ( 52 min )
    AI Dream 140 - This EPIC AI Video will Haunt you - Part 4
    submitted by /u/LordPewPew777 [link] [comments]  ( 52 min )
    AI avatar generators struggle with my face
    I have been seeing a lot of AI generated avatars that are really cool and really accurate so I decided to try. I tried the tiktok one first and it looked actually nothing like me. I then downloaded the app wonder which allows you to upload 10 photos and get 100 pictures and a vast majority of them still didn't look like me. Have other people experienced this? Does anyone know reasons this might be? submitted by /u/dreyhitz27 [link] [comments]  ( 52 min )
    Made a vid talking about the future of AI and how to adapt to it B )
    https://www.youtube.com/watch?v=DUi7v0eIPz4&lc=Ugxuw-xuUSYvVnbfjrV4AaABAg&ab_channel=ScaleAI submitted by /u/Spiritual-Ad4430 [link] [comments]  ( 52 min )
    should i be worried?
    submitted by /u/jpclp [link] [comments]  ( 53 min )
    Google "Muse" generates high-quality AI images at record speed
    submitted by /u/Number_5_alive [link] [comments]  ( 53 min )
    An AI made debate between Karl Marx and Donald Trump.
    submitted by /u/omnisvosscio [link] [comments]  ( 52 min )
    Harry Styles - As It Was (AI video by Aiplague) 4K
    submitted by /u/nalr00n [link] [comments]  ( 52 min )
    Nvidia’s AI tech can upscale videos on the browser itself
    submitted by /u/qptbook [link] [comments]  ( 54 min )
    Get ready for Bing Search with ChatGPT!
    submitted by /u/liquidocelotYT [link] [comments]  ( 61 min )
    What happened in AI research in 2022 - My curated list of AI breakthroughs with a video explanation, article, and code for each paper
    submitted by /u/OnlyProggingForFun [link] [comments]  ( 52 min )
    Will you consider having an AI robot for your friend / lover ? 🤖👭👬👫
    submitted by /u/Friendly_Avalanche [link] [comments]  ( 55 min )
    Which tent tempts the tent witches intentions
    submitted by /u/turnsouttellyouwhat [link] [comments]  ( 53 min )
    I asked AI to make a music video, this is what it made!
    submitted by /u/Branbruce [link] [comments]  ( 53 min )
    How to revoke decisions in ML-EDM ? (video #6)
    submitted by /u/ML-EDM [link] [comments]  ( 57 min )
    AI that automates repetitive tasks in your browser. Enter a task and it controls the browser to carry it out for you. superflows.ai
    submitted by /u/Quackerooney [link] [comments]  ( 58 min )
    🔥 Top A.I. Newsletters of 2023 🧠🐱‍💻🤖🚗🚀
    submitted by /u/BackgroundResult [link] [comments]  ( 63 min )
    AI dev, how should I begin?
    Im a 17 yo in Singapore taking CS in JC, have a background in competitive programming in cpp, coded some discord bots and done some CTFs etc. When I look for online courses for AI devs they seem to be teaching how to use certain pre-built/trained models like tensor and pytorch etc. Would like to ask if any senior AI devs here start the same way (from online courses, basics of tensor/pytorch,..) or what are some better ways to start with AI dev? Also, is an AI's performance depends more on its code/model or the amount of training data? (as a solo-dev, i want to know if i should focus more on writing better code or sorting better training sets) thanks and have a nice day submitted by /u/LampardNK [link] [comments]  ( 62 min )
    OpenAI's ChatGPT set to enhance Microsoft's Bing search capabilities
    submitted by /u/much_successes [link] [comments]  ( 57 min )
    Microsoft Working On ChatGPT-Powered Bing To Challenge Google
    submitted by /u/vadhavaniyafaijan [link] [comments]  ( 52 min )
    🔎 Breaking: Bing will have ChatGPT Soon!
    submitted by /u/BackgroundResult [link] [comments]  ( 57 min )
  • Open

    [D] Could neural networks just be like real neurons, but on a different medium? What's the difference between neurons in a brain and neurons in a computer?
    I'm not sure how to articulate this question but it's something i've been thinking too much about submitted by /u/WaggleMcDaggle [link] [comments]  ( 60 min )
    [D] ML in non-tech fields
    Hi all! What are some interesting applications of machine learning in fields that are not typically associated with technology? For example, have there been any successful projects using ML in fields like sociology, psychology, or political science? If so, could you share some links or references? Looking forwards to discovering your use-cases! submitted by /u/fr4nl4u [link] [comments]  ( 61 min )
    [N] Legal NLP Dataset With Over 39,000 Examples Released
    Legal datasets are extremely expensive because lawyers are, and this has bottlenecked legal NLP. To address this, we release the Merger Agreement Understand Dataset (MAUD), with over 39,000 multiple-choice reading comprehension examples for 152 merger agreements that have been manually labeled by legal experts. The dataset was created with the help of the American Bar Association; without their help the dataset would have cost over $5,000,000 to create. MAUD has substantial room for improvement and can could serve as a research challenge for NLP researchers without any legal background. Dataset and Baselines: https://github.com/TheAtticusProject/maud/ Paper: https://arxiv.org/abs/2301.00876 submitted by /u/Sea-Connection462 [link] [comments]  ( 59 min )
    [P] T5 Implementation in PyTorch
    An open-source implementation of Google AI's T5 in PyTorch. This repository contains the architecture to train your own T5 model. Link to the repository: https://github.com/conceptofmind/t5-pytorch T5 was first presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel. You can find a link to the paper here: https://arxiv.org/abs/1910.10683 Lucidrains was kind enough to provide feedback and review for this implementation. Please be sure to follow and support his work: https://github.com/lucidrains You can find the official T5x repository by Google AI here: https://github.com/google-research/t5x submitted by /u/EnricoShippole [link] [comments]  ( 67 min )
    [D] Feature engineering book recommendation for Bioinformatics
    Hi All, What book would you recommend for feature engineering, especially for bioinformatics and structural biology? Happy New Year and thanks in advance. submitted by /u/hovo1990 [link] [comments]  ( 60 min )
    [Discussion] If ML is based on data generated by humans, can it truly outperform humans?
    Hello mates, Since I have hardly any background in ML, I have somewhat dummy question. My understanding is that the majority of ML is based heavily on inputs generated by humans (some exceptions here would be unsupervised learning and GANs). So, if this is a case, I wonder if ML can truly outperform humans. Of course, in certain areas, like speed of computation or accuracy, computers will also be better than humans, but I am more interested in, shall we say, more general case. Kind regards submitted by /u/groman434 [link] [comments]  ( 85 min )
    [R] Measuring similarity between different vectors using Mahalanobis distance
    Hi guys, I have a set of feature values defined as x = {f_1,f_2,...,f_n} (x does not contain any zero) and the goal is to measure the similarity between these features using Mahalanobis distance so x is converted to a diagonal matrix called X_i where the diagonal elements are f_1,f_2,...,f_n, therefore, the distance is measured using columns of X_i. Then I calculate the covariance matrix of X_i which is semi-positive definite (SPD) but the inverse of the covariance matrix is non-SPD and Mahalanobis distance is not valid(it became negative). Any ideas or suggestions? Thanks. submitted by /u/eiliya_20 [link] [comments]  ( 65 min )
    [Discussion]: Quantization in native pytorch for GPUs (Cuda)?
    Is there a way to do quantization in native pytorch for GPUs (Cuda)? I know that TensorRT offers this functionality, but I would prefer working with native pytorch code. I understand from the pytorch docs, https://pytorch.org/docs/stable/quantization.html, that quantization for the GPU is linked to TensorRT. Given that Nvidia GPUs offer quantization for some time now, it's find it difficult to believe that no other solid implementation for quantization other than TensorRT exists. Grateful for any pointer or suggestions. submitted by /u/faschu [link] [comments]  ( 59 min )
    [P] 🗣️ Speechbox - A new library to *unnormalize* your speech.
    Speechbox is built on the premise that Whisper is good enough to pretty much transcribe any English speech. Furthermore, Whisper was trained to predict punctuated and orthographic text. ​ Speechbox leverages Whisper's quality to "unnormalize" audio transcriptions (see examples below) to make them more useful for further downstream applications while guaranteeing that the exact same words are being used. "we are going to the san francisco beach" can have multiple meanings: => We are going to the San Francisco beach! We are going to the San Francisco beach? We are going to the San Francisco beach. ​ Speechbox will pick the correct one for you 😉 ​ 👉 GitHub: https://github.com/huggingface/speechbox 🤗 Demo: https://huggingface.co/spaces/speechbox/whisper-restore-punctuation submitted by /u/pvp239 [link] [comments]  ( 60 min )
    [P] 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification
    At AWS we released Fortuna, a library for Uncertainty Quantification. Fortuna supports conformal prediction, Bayesian inference methods and more. Try it out! GitHub stars are very welcome!!! ⭐⭐⭐ Github repo: https://github.com/awslabs/fortuna submitted by /u/gianluca_detommaso [link] [comments]  ( 63 min )
    [R] Is there a one-minute stock market dataset available?
    Found this https://data.nasdaq.com/databases/AS500/data, but it’s $1,000 a year. Anything free? Doesn’t have to be one-minute, just granular submitted by /u/side-8182 [link] [comments]  ( 61 min )
    [P] HF Spaces demo: Crosslingual youtube subtitles to videos with Whisper and DeepL
    Hi, As part of Huggingface whisper finetuning event I created a demo where you can: Download youtube video with a given URL 2. Watch downloaded video in the first video component 3. Run automatic speech recognition on the video using Whisper models from ggerganov https://github.com/ggerganov/whisper.cpp 4. Translate the recognized transcriptions to 26 languages supported by deepL Download generated subtitle files in .srt and .vtt formats 6. Watch the video in another video component with added subtitles You can test it from here --> https://huggingface.co/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles <-- submitted by /u/Finslayer [link] [comments]  ( 66 min )
    [D]There is still no discussion nor response under my ICLR submission after two months. What do you think I should do?
    I have tried to post reminders to both chairs and reviewers, but no one seems to care about my rebuttal and revision. What an exciting experience! submitted by /u/minogame [link] [comments]  ( 65 min )
    [D] Tensorflow v1.15 in google colab not working
    Tensorflow 1.x is no longer supported on google colab. I want to use create_hparams from hparams and it has contrib module that has been removed in Tensorflow 2.x What shall be the alternative to this? submitted by /u/Affectionate_Bite_50 [link] [comments]  ( 64 min )
    [P] liboai - A C++ Library for OpenAI
    I've developed a library for the broader C++ developer and machine learning community to access and make use of the OpenAI API. The library follows a simple, elegant syntax similar in style to that of the OpenAI Python library. The library offers access to each component of the API, be it from Images, Fine-tunes, or Embeddings, it can do it--and with ease. It also possesses utility functionality that allows for out-of-box downloading of generated images, setting of request proxies, and so on. Finally, it contains highly-detailed documentation and code examples, as well as emphasizing secure usage of authorization information in the library. You can find it here: https://github.com/D7EAD/liboai. Enjoy! submitted by /u/throwaway1322482942 [link] [comments]  ( 64 min )
    State of the art models for Question generation [D]
    Hello everyone, I am working on a task to generate questions on some documents, currently manual creation of questions by domain experts is a very time consuming process, could anyone please suggest some good NLP models which can generate human like questions given a text. submitted by /u/arush1836 [link] [comments]  ( 58 min )
    [R] Issues Training CNN To Output Index To Large Array
    I created my own standard CNN. It can run on either C# or compute shader(GPU) in Unity. I am an experienced coder but I am still new to neural networks but I coded my own to help build my understanding of them. The Model: I am attempting to train the network to complete sentences of given text. (this may be the wrong approach). I am training the network with film dialog. I first sort through the dialog and convert the words into indexes for a list of all words in the dataset. The first input layer is 50 (Max of 50 words from user) and the output is just one index which is supposed to be the predicted word. The word indexes are normalized to be from 0-1 based on max word value. Training goes through every sentence word by word, and tells the network the next word after as the answer. The Problem: This is working well in small scale tests, but exponentially falls apart in larger scale models. The reason is that the more total words there are, the more exponentially sensitive the normalized indexes become since it's in the range of 0-1. Which makes training the network near impossible once there are thousands of words. Is there some simple solution I am missing when it comes to outputing sensitive index values? Any helps/ideas would be greatly appreciated. submitted by /u/TheRPGGamerMan [link] [comments]  ( 63 min )
  • Open

    RL Course Project ideas
    Hi guys, I have taken a course (undergrad) on Applied Machine Learning this semester and we have to do a project as part of our course. Our professor has suggested that we do something related to DL/RL since these 2 are quite active these days in research. Can someone please suggest some nice ideas/directions? I am not an absolute beginner in ML/RL and do know basic RL theory and pytorch. Our semester ends by May so a project should be doable in around 3 months. So I'm hoping that the project should be a good learning experience but also good enough to showcase my skills beyond the course (like in my CV and stuff, or possibly some research publication after working on it further). Thanks in advance! submitted by /u/6obama_bin_laden9 [link] [comments]  ( 56 min )
    Let’s learn about Policy Gradient by implementing our first Deep Reinforcement Learning algorithm with PyTorch (Deep Reinforcement Learning Free Course by Hugging Face 🤗)
    Hey there! I’m happy to announce that we just published the fourth Unit of the Deep Reinforcement Learning Course) 🥳 In this Unit, you’ll learn about Policy-based methods and code your first Deep Reinforcement Learning algorithm from scratch using PyTorch 🔥 You’ll then train this agent to play PixelCopter 🚁 and CartPole. You’ll be then able to improve the implementation with Convolutional Neural Networks. Start Learning now 👉 https://huggingface.co/deep-rl-course/unit4/introduction https://preview.redd.it/f7wpgv0g82aa1.jpg?width=1920&format=pjpg&auto=webp&s=09320316c02da83610e2c75ce18ae76a6c1df301 New year, new resolutions, if you want to start to learn about reinforcement learning, we launched this course, and don’t worry there’s still time and 2023 is the perfect year to start. We wrote an introduction unit to help you get started. You can start learning now 👉 https://huggingface.co/deep-rl-course/unit0/introduction If you have questions or feedback I would love to answer them. submitted by /u/cranthir_ [link] [comments]  ( 58 min )
    University researching in RL
    I am a Master's student looking for my Erasmus program and I would like to go to a university that does research in the field (I am currently a research assistant at Polimi). It's impossible for me to check every available university for the exchange so I would like to know if you could suggest some universities to look out for. (There are a lot of programs in the EU, some in China and Asia in general, and some in South America, just one in the USA with Florida university, but if you have any idea please tell me) Thanks! submitted by /u/Mysterious-Ad-5721 [link] [comments]  ( 56 min )
    variable action space in test scenario - how to deal with this
    Hi all, I am carrying out an optimisation problem on an RL task on a graph network. the observation space is a node embedding vector of n-dimensions. The reason for this choice is to be able to make the task invariant to graph size at test time. The action space is the number of nodes on the graph (it is a node-removal optimisation task so it can decide on any node to remove on the graph). The issue of course is that at test time, i might be faced with a graph that is either bigger or smaller and thus have an action space that has now changed. I was wondering how people may suggest to overcome this when making the original training phase invariant to the size of the graph? One idea could be to make it such that the environment 'presents' a node and the agent has to decide on whether it should remove or leave alone - however this seems laborious and i would like for the agent to be able to look at the graph and make an immediate decision on which action it takes. I have read several publications of this sort of approach with the advantage being that the task can now be applied to any graph size, but I am having a brain freeze abut how the action space is managed when the agent is presented with a graph of a size it has never seen before. Any advise or help is appreciated. thank you! submitted by /u/amjass12 [link] [comments]  ( 61 min )
    Distribution of actions - exploration distribution connection.
    For an environment I am currently working with I have two non-RL benchmark policies - a rule-based one and an modell-predictive one. The distributions of actions these two policies produce are looking like exponential distributions (see Picture). ​ https://preview.redd.it/2x5n2gn3d0aa1.png?width=578&format=png&auto=webp&s=892975a08a669de14ced228937175361f406c7e9 This made me wondering.. Is there any connection between the selected distribution for exploration (in PPO and SAC for eaxmple) and the desired distribution of actions? Are there any assumption about the distribution of actions when using a Normal Distribution for exploration? Because intuitively I would assume with a Normal Distribution for exploration actions greater and less than mean of the normal should lead to somewhat equal rewards. Otherwise choosing a normal distribution for exploration wouldn't make much sense. Also I suspect given an action distribution like the one in the picture the agent will not be able to learn is unless the standard deviation of the normal distribution becomes very small. What should I do in a case like this? I was thinking about doing a transformation of the action space so the actions are distributed more evenly. Then learn these actions with linear output + normal distribution and do the transformation as part of the environment. Neural Network -> Linear -> Normal -> Transformation -> Environment Instead of : Neural Network -> Tanh -> Normal -> Environment like it's usually done. submitted by /u/flxh13 [link] [comments]  ( 55 min )
  • Open

    Lights! Cameras! Atoms! Scientist Peers Into the Quantum Future
    Editor’s note: This is part of a series profiling people advancing science with high performance computing. Ryan Coffee makes movies of molecules. Their impacts are huge. The senior scientist at the SLAC National Accelerator Laboratory (above) says these visualizations could unlock the secrets of photosynthesis. They’ve already shown how sunlight can cause skin cancer. Long Read article >  ( 6 min )
    UF Provost Joe Glover on Building a Leading AI University
    When NVIDIA co-founder Chris Malachowsky approached University of Florida Provost Joe Glover with the offer of an AI supercomputer, he couldn’t have predicted the transformative impact it would have on the university. In just a short time, UF has become one of the top public colleges in the U.S. and developed a groundbreaking neural network Read article >  ( 5 min )
  • Open

    Strengthening electron-triggered light emission
    A new method can produce a hundredfold increase in light emissions from a type of electron-photon coupling, which is key to electron microscopes and other technologies.  ( 9 min )
  • Open

    Unlocking the power of the ChatGPT revolution: 100 innovative use-cases to try before you …
    2023 UPDATE! Just published a book with 1337 use cases and around 4000 examples.  ( 157 min )
    Python or JavaScript! for ML and DL
    Choose your Programming Language  ( 12 min )
    ChatGPT — CICERO — Twitter Layoffs (Autumn 22)
    The EdgeRunner Agent #Issue 1 Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 22 min )
    Best AI Tools with a Christmas Spirit
    Robots celebrate holidays?  ( 6 min )
  • Open

    MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees. (arXiv:2209.07225v2 [cs.LG] UPDATED)
    Multi-agent reinforcement learning (MARL) recently has achieved tremendous success in a wide range of fields. However, with a black-box neural network architecture, existing MARL methods make decisions in an opaque fashion that hinders humans from understanding the learned knowledge and how input observations influence decisions. Our solution is MIXing Recurrent soft decision Trees (MIXRTs), a novel interpretable architecture that can represent explicit decision processes via the root-to-leaf path of decision trees. We introduce a novel recurrent structure in soft decision trees to address partial observability, and estimate joint action values via linearly mixing outputs of recurrent trees based on local observations only. Theoretical analysis shows that MIXRTs guarantees the structural constraint with additivity and monotonicity in factorization. We evaluate MIXRTs on a range of challenging StarCraft II tasks. Experimental results show that our interpretable learning framework obtains competitive performance compared to widely investigated baselines, and delivers more straightforward explanations and domain knowledge of the decision processes.  ( 2 min )
    Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach. (arXiv:2301.00637v1 [cs.GT])
    Reinforcement Learning (RL) is currently one of the most commonly used techniques for traffic signal control (TSC), which can adaptively adjusted traffic signal phase and duration according to real-time traffic data. However, a fully centralized RL approach is beset with difficulties in a multi-network scenario because of exponential growth in state-action space with increasing intersections. Multi-agent reinforcement learning (MARL) can overcome the high-dimension problem by employing the global control of each local RL agent, but it also brings new challenges, such as the failure of convergence caused by the non-stationary Markov Decision Process (MDP). In this paper, we introduce an off-policy nash deep Q-Network (OPNDQN) algorithm, which mitigates the weakness of both fully centralized and MARL approaches. The OPNDQN algorithm solves the problem that traditional algorithms cannot be used in large state-action space traffic models by utilizing a fictitious game approach at each iteration to find the nash equilibrium among neighboring intersections, from which no intersection has incentive to unilaterally deviate. One of main advantages of OPNDQN is to mitigate the non-stationarity of multi-agent Markov process because it considers the mutual influence among neighboring intersections by sharing their actions. On the other hand, for training a large traffic network, the convergence rate of OPNDQN is higher than that of existing MARL approaches because it does not incorporate all state information of each agent. We conduct an extensive experiments by using Simulation of Urban MObility simulator (SUMO), and show the dominant superiority of OPNDQN over several existing MARL approaches in terms of average queue length, episode training reward and average waiting time.  ( 2 min )
    Fundamental Laws of Binary Classification. (arXiv:2205.07589v2 [cs.LG] UPDATED)
    Finding discriminant functions of minimum risk binary classification systems is a novel geometric locus problem -- which requires solving a system of fundamental locus equations of binary classification -- subject to deep-seated statistical laws. We show that a discriminant function of a minimum risk binary classification system is the solution of a locus equation that represents the geometric locus of the decision boundary of the system, wherein the discriminant function is connected to the decision boundary by an exclusive principal eigen-coordinate system -- at which point the discriminant function is represented by a geometric locus of a novel principal eigenaxis -- structured as a dual locus of likelihood components and principal eigenaxis components. We demonstrate that a minimum risk binary classification system acts to jointly minimize its eigenenergy and risk by locating a point of equilibrium, at which point critical minimum eigenenergies exhibited by the system are symmetrically concentrated in such a manner that the novel principal eigenaxis of the system exhibits symmetrical dimensions and densities, so that counteracting and opposing forces and influences of the system are symmetrically balanced with each other -- about the geometric center of the locus of the novel principal eigenaxis -- whereon the statistical fulcrum of the system is located. Thereby, a minimum risk binary classification system satisfies a state of statistical equilibrium -- so that the total allowed eigenenergy and the expected risk exhibited by the system are jointly minimized within the decision space of the system -- at which point the system exhibits the minimum probability of classification error.  ( 3 min )
    Heterogeneous Graph Contrastive Multi-view Learning. (arXiv:2210.00248v2 [cs.LG] UPDATED)
    Inspired by the success of contrastive learning (CL) in computer vision and natural language processing, graph contrastive learning (GCL) has been developed to learn discriminative node representations on graph datasets. However, the development of GCL on Heterogeneous Information Networks (HINs) is still in the infant stage. For example, it is unclear how to augment the HINs without substantially altering the underlying semantics, and how to design the contrastive objective to fully capture the rich semantics. Moreover, early investigations demonstrate that CL suffers from sampling bias, whereas conventional debiasing techniques are empirically shown to be inadequate for GCL. How to mitigate the sampling bias for heterogeneous GCL is another important problem. To address the aforementioned challenges, we propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In particular, we use metapaths as the augmentation to generate multiple subgraphs as multi-views, and propose a contrastive objective to maximize the mutual information between any pairs of metapath-induced views. To alleviate the sampling bias, we further propose a positive sampling strategy to explicitly select positives for each node via jointly considering semantic and structural information preserved on each metapath view. Extensive experiments demonstrate HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets.  ( 2 min )
    A Snapshot of the Frontiers of Client Selection in Federated Learning. (arXiv:2210.04607v2 [cs.DC] UPDATED)
    Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model's parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as a strategy to limit the number of communicating parties at every step of the process. Since the early na\"{i}ve random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.  ( 2 min )
    Online Training Through Time for Spiking Neural Networks. (arXiv:2210.04195v2 [cs.NE] UPDATED)
    Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagation through time (BPTT) with surrogate gradients (SG) is popularly used to achieve high performance in a very small number of time steps. However, it is at the cost of large memory consumption for training, lack of theoretical clarity for optimization, and inconsistency with the online property of biological learning and rules on neuromorphic hardware. Other works connect spike representations of SNNs with equivalent artificial neural network formulation and train SNNs by gradients from equivalent mappings to ensure descent directions. But they fail to achieve low latency and are also not online. In this work, we propose online training through time (OTTT) for SNNs, which is derived from BPTT to enable forward-in-time learning by tracking presynaptic activities and leveraging instantaneous loss and gradients. Meanwhile, we theoretically analyze and prove that gradients of OTTT can provide a similar descent direction for optimization as gradients based on spike representations under both feedforward and recurrent conditions. OTTT only requires constant training memory costs agnostic to time steps, avoiding the significant memory costs of BPTT for GPU training. Furthermore, the update rule of OTTT is in the form of three-factor Hebbian learning, which could pave a path for online on-chip learning. With OTTT, it is the first time that two mainstream supervised SNN training methods, BPTT with SG and spike representation-based training, are connected, and meanwhile in a biologically plausible form. Experiments on CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS demonstrate the superior performance of our method on large-scale static and neuromorphic datasets in small time steps.  ( 2 min )
    Automated dysgraphia detection by deep learning with SensoGrip. (arXiv:2210.07659v2 [cs.LG] UPDATED)
    Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.  ( 2 min )
    Spatial-Temporal Meta-path Guided Explainable Crime Prediction. (arXiv:2205.01901v3 [cs.LG] UPDATED)
    Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to prevent crimes. With the increasing availability of both fine-grained urban and public service data, there is a recent surge in fusing such cross-domain information to facilitate crime prediction. By capturing the information about social structure, environment, and crime trends, existing machine learning predictive models have explored the dynamic crime patterns from different views. However, these approaches mostly convert such multi-source knowledge into implicit and latent representations (e.g., learned embeddings of districts), making it still a challenge to investigate the impacts of explicit factors for the occurrences of crimes behind the scenes. In this paper, we present a Spatial-Temporal Metapath guided Explainable Crime prediction (STMEC) framework to capture dynamic patterns of crime behaviours and explicitly characterize how the environmental and social factors mutually interact to produce the forecasts. Extensive experiments show the superiority of STMEC compared with other advanced spatiotemporal models, especially in predicting felonies (e.g., robberies and assaults with dangerous weapons).  ( 2 min )
    TriNet: stabilizing self-supervised learning from complete or slow collapse. (arXiv:2301.00656v1 [eess.AS])
    Self-supervised learning (SSL) models confront challenges of abrupt informational collapse or slow dimensional collapse. We propose TriNet, which introduces a novel triple-branch architecture for preventing collapse and stabilizing the pre-training. Our experimental results show that the proposed method notably stabilizes and accelerates pre-training and achieves a relative word error rate reduction (WERR) of 5.32% compared to the state-of-the-art (SOTA) Data2vec for a downstream benchmark ASR task. We will release our code at https://github.com/tencent-ailab/.  ( 2 min )
    Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets. (arXiv:2301.00595v1 [cs.LG])
    Missing values are a common problem in data science and machine learning. Removing instances with missing values can adversely affect the quality of further data analysis. This is exacerbated when there are relatively many more features than instances, and thus the proportion of affected instances is high. Such a scenario is common in many important domains, for example, single nucleotide polymorphism (SNP) datasets provide a large number of features over a genome for a relatively small number of individuals. To preserve as much information as possible prior to modeling, a rigorous imputation scheme is acutely needed. While Denoising Autoencoders is a state-of-the-art method for imputation in high-dimensional data, they still require enough complete cases to be trained on which is often not available in real-world problems. In this paper, we consider missing value imputation as a multi-label classification problem and propose Chains of Autoreplicative Random Forests. Using multi-label Random Forests instead of neural networks works well for low-sampled data as there are fewer parameters to optimize. Experiments on several SNP datasets show that our algorithm effectively imputes missing values based only on information from the dataset and exhibits better performance than standard algorithms that do not require any additional information. In this paper, the algorithm is implemented specifically for SNP data, but it can easily be adapted for other cases of missing value imputation.  ( 2 min )
    FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks. (arXiv:2301.00675v1 [cs.LG])
    Model compression via quantization and sparsity enhancement has gained an immense interest to enable the deployment of deep neural networks (DNNs) in resource-constrained edge environments. Although these techniques have shown promising results in reducing the energy, latency and memory requirements of the DNNs, their performance in non-ideal real-world settings (such as in the presence of hardware faults) is yet to be completely understood. In this paper, we investigate the impact of bit-flip and stuck-at faults on activation-sparse quantized DNNs (QDNNs). We show that a high level of activation sparsity comes at the cost of larger vulnerability to faults. For instance, activation-sparse QDNNs exhibit up to 17.32% lower accuracy than the standard QDNNs. We also establish that one of the major cause of the degraded accuracy is sharper minima in the loss landscape for activation-sparse QDNNs, which makes them more sensitive to perturbations in the weight values due to faults. Based on this observation, we propose the mitigation of the impact of faults by employing a sharpness-aware quantization (SAQ) training scheme. The activation-sparse and standard QDNNs trained with SAQ have up to 36.71% and 24.76% higher inference accuracy, respectively compared to their conventionally trained equivalents. Moreover, we show that SAQ-trained activation-sparse QDNNs show better accuracy in faulty settings than standard QDNNs trained conventionally. Thus the proposed technique can be instrumental in achieving sparsity-related energy/latency benefits without compromising on fault tolerance.  ( 2 min )
    Deep Recurrent Learning Through Long Short Term Memory and TOPSIS. (arXiv:2301.00693v1 [cs.SE])
    Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classification algorithm based on long short term memory (LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption features. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities. Qualitative survey is conducted among users by considering technology, innovation and resistance issues, to formulate hypotheses on key adoption factors.  ( 2 min )
    Sparse neural networks with skip-connections for nonlinear system identification. (arXiv:2301.00582v1 [eess.SY])
    Data-driven models such as neural networks are being applied more and more to safety-critical applications, such as the modeling and control of cyber-physical systems. Despite the flexibility of the approach, there are still concerns about the safety of these models in this context, as well as the need for large amounts of potentially expensive data. In particular, when long-term predictions are needed or frequent measurements are not available, the open-loop stability of the model becomes important. However, it is difficult to make such guarantees for complex black-box models such as neural networks, and prior work has shown that model stability is indeed an issue. In this work, we consider an aluminum extraction process where measurements of the internal state of the reactor are time-consuming and expensive. We model the process using neural networks and investigate the role of including skip connections in the network architecture as well as using l1 regularization to induce sparse connection weights. We demonstrate that these measures can greatly improve both the accuracy and the stability of the models for datasets of varying sizes.  ( 2 min )
    An Efficient Hierarchical Kriging Modeling Method for High-dimension Multi-fidelity Problems. (arXiv:2301.00216v1 [cs.LG])
    Multi-fidelity Kriging model is a promising technique in surrogate-based design as it can balance the model accuracy and cost of sample preparation by fusing low- and high-fidelity data. However, the cost for building a multi-fidelity Kriging model increases significantly with the increase of the problem dimension. To attack this issue, an efficient Hierarchical Kriging modeling method is proposed. In building the low-fidelity model, the maximal information coefficient is utilized to calculate the relative value of the hyperparameter. With this, the maximum likelihood estimation problem for determining the hyperparameters is transformed as a one-dimension optimization problem, which can be solved in an efficient manner and thus improve the modeling efficiency significantly. A local search is involved further to exploit the search space of hyperparameters to improve the model accuracy. The high-fidelity model is built in a similar manner with the hyperparameter of the low-fidelity model served as the relative value of the hyperparameter for high-fidelity model. The performance of the proposed method is compared with the conventional tuning strategy, by testing them over ten analytic problems and an engineering problem of modeling the isentropic efficiency of a compressor rotor. The empirical results demonstrate that the modeling time of the proposed method is reduced significantly without sacrificing the model accuracy. For the modeling of the isentropic efficiency of the compressor rotor, the cost saving associated with the proposed method is about 90% compared with the conventional strategy. Meanwhile, the proposed method achieves higher accuracy.  ( 2 min )
    Targeted Phishing Campaigns using Large Scale Language Models. (arXiv:2301.00665v1 [cs.CL])
    In this research, we aim to explore the potential of natural language models (NLMs) such as GPT-3 and GPT-2 to generate effective phishing emails. Phishing emails are fraudulent messages that aim to trick individuals into revealing sensitive information or taking actions that benefit the attackers. We propose a framework for evaluating the performance of NLMs in generating these types of emails based on various criteria, including the quality of the generated text, the ability to bypass spam filters, and the success rate of tricking individuals. Our evaluations show that NLMs are capable of generating phishing emails that are difficult to detect and that have a high success rate in tricking individuals, but their effectiveness varies based on the specific NLM and training data used. Our research indicates that NLMs could have a significant impact on the prevalence of phishing attacks and emphasizes the need for further study on the ethical and security implications of using NLMs for malicious purposes.  ( 2 min )
    Lossy Compression with Gaussian Diffusion. (arXiv:2206.08889v2 [stat.ML] UPDATED)
    We consider a novel lossy compression approach based on unconditional diffusion generative models, which we call DiffC. Unlike modern compression schemes which rely on transform coding and quantization to restrict the transmitted information, DiffC relies on the efficient communication of pixels corrupted by Gaussian noise. We implement a proof of concept and find that it works surprisingly well despite the lack of an encoder transform, outperforming the state-of-the-art generative compression method HiFiC on ImageNet 64x64. DiffC only uses a single model to encode and denoise corrupted pixels at arbitrary bitrates. The approach further provides support for progressive coding, that is, decoding from partial bit streams. We perform a rate-distortion analysis to gain a deeper understanding of its performance, providing analytical results for multivariate Gaussian data as well as theoretic bounds for general distributions. Furthermore, we prove that a flow-based reconstruction achieves a 3 dB gain over ancestral sampling at high bitrates.  ( 2 min )
    Extended Feature Space-Based Automatic Melanoma Detection System. (arXiv:2209.04588v2 [cs.LG] UPDATED)
    Melanoma is the deadliest form of skin cancer. Uncontrollable growth of melanocytes leads to melanoma. Melanoma has been growing wildly in the last few decades. In recent years, the detection of melanoma using image processing techniques has become a dominant research field. The Automatic Melanoma Detection System (AMDS) helps to detect melanoma based on image processing techniques by accepting infected skin area images as input. A single lesion image is a source of multiple features. Therefore, It is crucial to select the appropriate features from the image of the lesion in order to increase the accuracy of AMDS. For melanoma detection, all extracted features are not important. Some of the extracted features are complex and require more computation tasks, which impacts the classification accuracy of AMDS. The feature extraction phase of AMDS exhibits more variability, therefore it is important to study the behaviour of AMDS using individual and extended feature extraction approaches. A novel algorithm ExtFvAMDS is proposed for the calculation of Extended Feature Vector Space. The six models proposed in the comparative study revealed that the HSV feature vector space for automatic detection of melanoma using Ensemble Bagged Tree classifier on Med-Node Dataset provided 99% AUC, 95.30% accuracy, 94.23% sensitivity, and 96.96% specificity.  ( 2 min )
    UltraProp: Principled and Explainable Propagation on Large Graphs. (arXiv:2301.00270v1 [cs.SI])
    Given a large graph with few node labels, how can we (a) identify the mixed network-effect of the graph and (b) predict the unknown labels accurately and efficiently? This work proposes Network Effect Analysis (NEA) and UltraProp, which are based on two insights: (a) the network-effect (NE) insight: a graph can exhibit not only one of homophily and heterophily, but also both or none in a label-wise manner, and (b) the neighbor-differentiation (ND) insight: neighbors have different degrees of influence on the target node based on the strength of connections. NEA provides a statistical test to check whether a graph exhibits network-effect or not, and surprisingly discovers the absence of NE in many real-world graphs known to have heterophily. UltraProp solves the node classification problem with notable advantages: (a) Accurate, thanks to the network-effect (NE) and neighbor-differentiation (ND) insights; (b) Explainable, precisely estimating the compatibility matrix; (c) Scalable, being linear with the input size and handling graphs with millions of nodes; and (d) Principled, with closed-form formula and theoretical guarantee. Applied on eight real-world graph datasets, UltraProp outperforms top competitors in terms of accuracy and run time, requiring only stock CPU servers. On a large real-world graph with 1.6M nodes and 22.3M edges, UltraProp achieves more than 9 times speedup (12 minutes vs. 2 hours) compared to most competitors.  ( 2 min )
    ReSQueing Parallel and Private Stochastic Convex Optimization. (arXiv:2301.00457v1 [math.OC])
    We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.  ( 2 min )
    Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources. (arXiv:2209.07744v2 [cs.LG] UPDATED)
    In replacing fossil fuels with renewable energy resources for carbon neutrality, the unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To address this issue, a reinforcement learning (RL) technique is introduced in this paper. For RL, a graph convolutional network (GCN) and a bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters, based on cooperative game theory. The flexible and reliable DC nanogrid is suitable for integrating renewable energy for a distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid cluster, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of an electric vehicle (EV) is executed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as GCN- convolutional neural network (CNN) layers for deep Q-learning network (DQN), GCN-LSTM layers for deep recurrent Q-learning network (DRQN), GCN-Bi-LSTM layers for DRQN, and GCN-Bi-LSTM layers for proximal policy optimization (PPO), are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit by considering the time of use (ToU) tariff-based electricity cost and the system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on a distribution test feeder in real time, and the proposed GCN-Bi-LSTM-PPO technique achieving the lowest electricity cost among the RL algorithms used for comparison reduces the electricity cost by 36.7%, averaging over nanogrid clusters.  ( 3 min )
    Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. (arXiv:2204.13545v2 [cs.LG] UPDATED)
    Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA HTS is required to enrich single-cell data meaningfully. We introduce chemCPA, a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with an architecture surgery for transfer learning and demonstrate how training on existing bulk RNA HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating drug discovery.  ( 2 min )
    DRSOM: A Dimension Reduced Second-Order Method. (arXiv:2208.00208v2 [math.OC] UPDATED)
    In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization. Under a trust-region-like framework, our method preserves the convergence of the second-order method while using only curvature information in a few directions. Consequently, the computational overhead of our method remains comparable to the first-order such as the gradient descent method. Theoretically, we show that the method has a local quadratic convergence and a global convergence rate of $O(\epsilon^{-3/2})$ to satisfy the first-order and second-order conditions if the subspace satisfies a commonly adopted approximated Hessian assumption. We further show that this assumption can be removed if we perform one \emph{corrector step} (using a Krylov method, for example) periodically at the end stage of the algorithm. The applicability and performance of DRSOM are exhibited by various computational experiments, particularly in machine learning and deep learning. For neural networks, our preliminary implementation seems to gain computational advantages in terms of training accuracy and iteration complexity over state-of-the-art first-order methods such as SGD and ADAM.  ( 2 min )
    Learning To Rank Diversely. (arXiv:2210.07774v2 [cs.IR] UPDATED)
    Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.  ( 2 min )
    A Genetic Algorithm-based Framework for Learning Statistical Power Manifold. (arXiv:2209.00215v2 [stat.CO] UPDATED)
    Statistical power is a measure of the replicability of a categorical hypothesis test. Formally, it is the probability of detecting an effect, if there is a true effect present in the population. Hence, optimizing statistical power as a function of some parameters of a hypothesis test is desirable. However, for most hypothesis tests, the explicit functional form of statistical power for individual model parameters is unknown; but calculating power for a given set of values of those parameters is possible using simulated experiments. These simulated experiments are usually computationally expensive. Hence, developing the entire statistical power manifold using simulations can be very time-consuming. We propose a novel genetic algorithm-based framework for learning statistical power manifolds. For a multiple linear regression $F$-test, we show that the proposed algorithm/framework learns the statistical power manifold much faster as compared to a brute-force approach as the number of queries to the power oracle is significantly reduced. We also show that the quality of learning the manifold improves as the number of iterations increases for the genetic algorithm. Such tools are useful for evaluating statistical power trade-offs when researchers have little information regarding a priori `best guesses' of primary effect sizes of interest or how sampling variability in non-primary effects impacts power for primary ones.  ( 2 min )
    Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. (arXiv:2207.00787v3 [cs.LG] UPDATED)
    Iterative refinement -- start with a random guess, then iteratively improve the guess -- is a useful paradigm for representation learning because it offers a way to break symmetries among equally plausible explanations for the data. This property enables the application of such methods to infer representations of sets of entities, such as objects in physical scenes, structurally resembling clustering algorithms in latent space. However, most prior works differentiate through the unrolled refinement process, which can make optimization challenging. We observe that such methods can be made differentiable by means of the implicit function theorem, and develop an implicit differentiation approach that improves the stability and tractability of training by decoupling the forward and backward passes. This connection enables us to apply advances in optimizing implicit layers to not only improve the optimization of the slot attention module in SLATE, a state-of-the-art method for learning entity representations, but do so with constant space and time complexity in backpropagation and only one additional line of code.  ( 2 min )
    Theory of Machine Learning with Limited Data. (arXiv:2206.07586v3 [cs.AI] UPDATED)
    Application of machine learning may be understood as deriving new knowledge for practical use through explaining accumulated observations, training set. Peirce used the term abduction for this kind of inference. Here I formalize the concept of abduction for real valued hypotheses, and show that 14 of the most popular textbook ML learners (every learner I tested), covering classification, regression and clustering, implement this concept of abduction inference. The approach is proposed as an alternative to statistical learning theory, which requires an impractical assumption of indefinitely increasing training set for its justification.  ( 2 min )
    Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces. (arXiv:2207.00879v3 [stat.ML] UPDATED)
    Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e.g., hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.  ( 2 min )
    DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching. (arXiv:2206.00233v2 [cs.MA] UPDATED)
    Current approaches to multi-agent cooperation rely heavily on centralized mechanisms or explicit communication protocols to ensure convergence. This paper studies the problem of distributed multi-agent learning without resorting to centralized components or explicit communication. It examines the use of distribution matching to facilitate the coordination of independent agents. In the proposed scheme, each agent independently minimizes the distribution mismatch to the corresponding component of a target visitation distribution. The theoretical analysis shows that under certain conditions, each agent minimizing its individual distribution mismatch allows the convergence to the joint policy that generated the target distribution. Further, if the target distribution is from a joint policy that optimizes a cooperative task, the optimal policy for a combination of this task reward and the distribution matching reward is the same joint policy. This insight is used to formulate a practical algorithm (DM$^2$), in which each individual agent matches a target distribution derived from concurrently sampled trajectories from a joint expert policy. Experimental validation on the StarCraft domain shows that combining (1) a task reward, and (2) a distribution matching reward for expert demonstrations for the same task, allows agents to outperform a naive distributed baseline. Additional experiments probe the conditions under which expert demonstrations need to be sampled to obtain the learning benefits.  ( 2 min )
    Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following. (arXiv:2301.00676v1 [cs.LG])
    Agents that can follow language instructions are expected to be useful in a variety of situations such as navigation. However, training neural network-based agents requires numerous paired trajectories and languages. This paper proposes using multimodal generative models for semi-supervised learning in the instruction following tasks. The models learn a shared representation of the paired data, and enable semi-supervised learning by reconstructing unpaired data through the representation. Key challenges in applying the models to sequence-to-sequence tasks including instruction following are learning a shared representation of variable-length mulitimodal data and incorporating attention mechanisms. To address the problems, this paper proposes a novel network architecture to absorb the difference in the sequence lengths of the multimodal data. In addition, to further improve the performance, this paper shows how to incorporate the generative model-based approach with an existing semi-supervised method called a speaker-follower model, and proposes a regularization term that improves inference using unpaired trajectories. Experiments on BabyAI and Room-to-Room (R2R) environments show that the proposed method improves the performance of instruction following by leveraging unpaired data, and improves the performance of the speaker-follower model by 2\% to 4\% in R2R.
    Generalizable Black-Box Adversarial Attack with Meta Learning. (arXiv:2301.00364v1 [cs.LG])
    In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning framework by training a meta-generator to produce perturbations conditioned on benign examples. When attacking a new benign example, the meta generator can be quickly fine-tuned based on the feedback information of the new task as well as a few historical attacks to produce effective perturbations. Moreover, since the meta-train procedure consumes many queries to learn a generalizable generator, we utilize model-level adversarial transferability to train the meta-generator on a white-box surrogate model, then transfer it to help the attack against the target model. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance, which is verified by extensive experiments.
    Capturing positive network attributes during the estimation of recursive logit models: A prism-based approach. (arXiv:2204.01215v3 [econ.EM] UPDATED)
    Although the recursive logit (RL) model has been recently popular and has led to many applications and extensions, an important numerical issue with respect to the computation of value functions remains unsolved. This issue is particularly significant for model estimation, during which the parameters are updated every iteration and may violate the feasibility condition of the value function. To solve this numerical issue of the value function in the model estimation, this study performs an extensive analysis of a prism-constrained RL (Prism-RL) model proposed by Oyama and Hato (2019), which has a path set constrained by the prism defined based upon a state-extended network representation. The numerical experiments have shown two important properties of the Prism-RL model for parameter estimation. First, the prism-based approach enables estimation regardless of the initial and true parameter values, even in cases where the original RL model cannot be estimated due to the numerical problem. We also successfully captured a positive effect of the presence of street green on pedestrian route choice in a real application. Second, the Prism-RL model achieved better fit and prediction performance than the RL model, by implicitly restricting paths with large detour or many loops. Defining the prism-based path set in a data-oriented manner, we demonstrated the possibility of the Prism-RL model describing more realistic route choice behavior. The capture of positive network attributes while retaining the diversity of path alternatives is important in many applications such as pedestrian route choice and sequential destination choice behavior, and thus the prism-based approach significantly extends the practical applicability of the RL model.  ( 2 min )
    Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization. (arXiv:2301.00631v1 [cs.LG])
    This paper introduces a novel algorithm, the Perturbed Proximal Preconditioned SPIDER algorithm (3P-SPIDER), designed to solve finite sum non-convex composite optimization. It is a stochastic Variable Metric Forward-Backward algorithm, which allows approximate preconditioned forward operator and uses a variable metric proximity operator as the backward operator; it also proposes a mini-batch strategy with variance reduction to address the finite sum setting. We show that 3P-SPIDER extends some Stochastic preconditioned Gradient Descent-based algorithms and some Incremental Expectation Maximization algorithms to composite optimization and to the case the forward operator can not be computed in closed form. We also provide an explicit control of convergence in expectation of 3P-SPIDER, and study its complexity in order to satisfy the epsilon-approximate stationary condition. Our results are the first to combine the composite non-convex optimization setting, a variance reduction technique to tackle the finite sum setting by using a minibatch strategy and, to allow deterministic or random approximations of the preconditioned forward operator. Finally, through an application to inference in a logistic regression model with random effects, we numerically compare 3P-SPIDER to other stochastic forward-backward algorithms and discuss the role of some design parameters of 3P-SPIDER.
    Smooth Mathematical Function from Compact Neural Networks. (arXiv:2301.00181v1 [cs.NE])
    This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.
    Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications. (arXiv:2301.00752v1 [cs.NI])
    This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Image-based methods to quantitatively and deterministically predict future received signal strength using machine learning from time series of depth images to mitigate the human body line-of-sight (LOS) path blockage in mmWave communications have been proposed. However, image-based methods have been limited in applicable environments because camera images may contain private information. Thus, this study demonstrates the feasibility of using point clouds obtained from light detection and ranging (LiDAR) for the mmWave link quality prediction. Point clouds represent three-dimensional (3D) spaces as a set of points and are sparser and less likely to contain sensitive information than camera images. Additionally, point clouds provide 3D position and motion information, which is necessary for understanding the radio propagation environment involving pedestrians. This study designs the mmWave link quality prediction method and conducts two experimental evaluations using different types of point clouds obtained from LiDAR and depth cameras, as well as different numerical indicators of link quality, received signal strength and throughput. Based on these experiments, our proposed method can predict future large attenuation of mmWave link quality due to LOS blockage by human bodies, therefore our point cloud-based method can be an alternative to image-based methods.
    Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels. (arXiv:2301.00545v1 [cs.LG])
    A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR. Our code and pre-trained models will be released.  ( 2 min )
    Local Differential Privacy for Sequential Decision Making in a Changing Environment. (arXiv:2301.00561v1 [cs.LG])
    We study the problem of preserving privacy while still providing high utility in sequential decision making scenarios in a changing environment. We consider abruptly changing environment: the environment remains constant during periods and it changes at unknown time instants. To formulate this problem, we propose a variant of multi-armed bandits called non-stationary stochastic corrupt bandits. We construct an algorithm called SW-KLUCB-CF and prove an upper bound on its utility using the performance measure of regret. The proven regret upper bound for SW-KLUCB-CF is near-optimal in the number of time steps and matches the best known bound for analogous problems in terms of the number of time steps and the number of changes. Moreover, we present a provably optimal mechanism which can guarantee the desired level of local differential privacy while providing high utility.  ( 2 min )
    In Quest of Ground Truth: Learning Confident Models and Estimating Uncertainty in the Presence of Annotator Noise. (arXiv:2301.00524v1 [cs.CV])
    The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.  ( 2 min )
    A contrastive learning approach for individual re-identification in a wild fish population. (arXiv:2301.00596v1 [cs.CV])
    In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.  ( 2 min )
    Data-Driven Optimization of Directed Information over Discrete Alphabets. (arXiv:2301.00621v1 [cs.IT])
    Directed information (DI) is a fundamental measure for the study and analysis of sequential stochastic models. In particular, when optimized over input distributions it characterizes the capacity of general communication channels. However, analytic computation of DI is typically intractable and existing optimization techniques over discrete input alphabets require knowledge of the channel model, which renders them inapplicable when only samples are available. To overcome these limitations, we propose a novel estimation-optimization framework for DI over discrete input spaces. We formulate DI optimization as a Markov decision process and leverage reinforcement learning techniques to optimize a deep generative model of the input process probability mass function (PMF). Combining this optimizer with the recently developed DI neural estimator, we obtain an end-to-end estimation-optimization algorithm which is applied to estimating the (feedforward and feedback) capacity of various discrete channels with memory. Furthermore, we demonstrate how to use the optimized PMF model to (i) obtain theoretical bounds on the feedback capacity of unifilar finite-state channels; and (ii) perform probabilistic shaping of constellations in the peak power-constrained additive white Gaussian noise channel.  ( 2 min )
    Posterior Collapse and Latent Variable Non-identifiability. (arXiv:2301.00537v1 [stat.ML])
    Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.  ( 2 min )
    Learning to Maximize Mutual Information for Dynamic Feature Selection. (arXiv:2301.00557v1 [cs.LG])
    Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.  ( 2 min )
    Dynamically Modular and Sparse General Continual Learning. (arXiv:2301.00620v1 [cs.CV])
    Real-world applications often require learning continuously from a stream of data under ever-changing conditions. When trying to learn from such non-stationary data, deep neural networks (DNNs) undergo catastrophic forgetting of previously learned information. Among the common approaches to avoid catastrophic forgetting, rehearsal-based methods have proven effective. However, they are still prone to forgetting due to task-interference as all parameters respond to all tasks. To counter this, we take inspiration from sparse coding in the brain and introduce dynamic modularity and sparsity (Dynamos) for rehearsal-based general continual learning. In this setup, the DNN learns to respond to stimuli by activating relevant subsets of neurons. We demonstrate the effectiveness of Dynamos on multiple datasets under challenging continual learning evaluation protocols. Finally, we show that our method learns representations that are modular and specialized, while maintaining reusability by activating subsets of neurons with overlaps corresponding to the similarity of stimuli.  ( 2 min )
    Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications. (arXiv:2301.00516v1 [cs.IT])
    In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance.  ( 2 min )
    EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies. (arXiv:2301.00508v1 [cs.SD])
    Vocal Bursts -- short, non-speech vocalizations that convey emotions, such as laughter, cries, sighs, moans, and groans -- are an often-overlooked aspect of speech emotion recognition, but an important aspect of human vocal communication. One barrier to study of these interesting vocalizations is a lack of large datasets. I am pleased to introduce the EmoGator dataset, which consists of 32,040 samples from 365 speakers, 16.91 hours of audio; each sample classified into one of 30 distinct emotion categories by the speaker. Several different approaches to construct classifiers to identify emotion categories will be discussed, and directions for future research will be suggested. Data set is available for download from https://github.com/fredbuhl/EmoGator.  ( 2 min )
    Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting. (arXiv:2301.00493v1 [cs.CV])
    We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.  ( 2 min )
    Deep Correlation-Aware Kernelized Autoencoders for Anomaly Detection in Cybersecurity. (arXiv:2301.00462v1 [cs.LG])
    Unsupervised learning-based anomaly detection in latent space has gained importance since discriminating anomalies from normal data becomes difficult in high-dimensional space. Both density estimation and distance-based methods to detect anomalies in latent space have been explored in the past. These methods prove that retaining valuable properties of input data in latent space helps in the better reconstruction of test data. Moreover, real-world sensor data is skewed and non-Gaussian in nature, making mean-based estimators unreliable for skewed data. Again, anomaly detection methods based on reconstruction error rely on Euclidean distance, which does not consider useful correlation information in the feature space and also fails to accurately reconstruct the data when it deviates from the training distribution. In this work, we address the limitations of reconstruction error-based autoencoders and propose a kernelized autoencoder that leverages a robust form of Mahalanobis distance (MD) to measure latent dimension correlation to effectively detect both near and far anomalies. This hybrid loss is aided by the principle of maximizing the mutual information gain between the latent dimension and the high-dimensional prior data space by maximizing the entropy of the latent space while preserving useful correlation information of the original data in the low-dimensional latent space. The multi-objective function has two goals -- it measures correlation information in the latent feature space in the form of robust MD distance and simultaneously tries to preserve useful correlation information from the original data space in the latent space by maximizing mutual information between the prior and latent space.  ( 2 min )
    Decision Models for Selecting Federated Learning Architecture Patterns. (arXiv:2204.13291v2 [cs.LG] UPDATED)
    Federated learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning. Being a widely distributed system, federated learning requires various system design thinking. To better design a federated learning system, researchers have introduced multiple patterns and tactics that cover various system design aspects. However, the multitude of patterns leaves the designers confused about when and which pattern to adopt. In this paper, we present a set of decision models for the selection of patterns for federated learning architecture design based on a systematic literature review on federated learning, to assist designers and architects who have limited knowledge of federated learning. Each decision model maps functional and non-functional requirements of federated learning systems to a set of patterns. We also clarify the trade-offs in the patterns. We evaluated the decision models by mapping the decision patterns to concrete federated learning architectures by big tech firms to assess the models' correctness and usefulness. The evaluation results indicate that the proposed decision models are able to bring structure to the federated learning architecture design process and help explicitly articulate the design rationale.  ( 2 min )
    Dimensionless machine learning: Imposing exact units equivariance. (arXiv:2204.00887v2 [stat.ML] UPDATED)
    Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings. Here, we express this symmetry in terms of a (non-compact) group action, and we employ dimensional analysis and ideas from equivariant machine learning to provide a methodology for exactly units-equivariant machine learning: For any given learning task, we first construct a dimensionless version of its inputs using classic results from dimensional analysis, and then perform inference in the dimensionless space. Our approach can be used to impose units equivariance across a broad range of machine learning methods which are equivariant to rotations and other groups. We discuss the in-sample and out-of-sample prediction accuracy gains one can obtain in contexts like symbolic regression and emulation, where symmetry is important. We illustrate our approach with simple numerical examples involving dynamical systems in physics and ecology.  ( 2 min )
    Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation. (arXiv:2203.02896v2 [cs.LG] UPDATED)
    Multi-agent settings remain a fundamental challenge in the reinforcement learning (RL) domain due to the partial observability and the lack of accurate real-time interactions across agents. In this paper, we propose a new method based on local communication learning to tackle the multi-agent RL (MARL) challenge within a large number of agents coexisting. First, we design a new communication protocol that exploits the ability of depthwise convolution to efficiently extract local relations and learn local communication between neighboring agents. To facilitate multi-agent coordination, we explicitly learn the effect of joint actions by taking the policies of neighboring agents as inputs. Second, we introduce the mean-field approximation into our method to reduce the scale of agent interactions. To more effectively coordinate behaviors of neighboring agents, we enhance the mean-field approximation by a supervised policy rectification network (PRN) for rectifying real-time agent interactions and by a learnable compensation term for correcting the approximation bias. The proposed method enables efficient coordination as well as outperforms several baseline approaches on the adaptive traffic signal control (ATSC) task and the StarCraft II multi-agent challenge (SMAC).  ( 2 min )
    Secure Distributed Training at Scale. (arXiv:2106.11257v4 [cs.LG] UPDATED)
    Many areas of deep learning benefit from using increasingly larger neural networks trained on public data, as is the case for pre-trained models for NLP and computer vision. Training such models requires a lot of computational resources (e.g., HPC clusters) that are not available to small research groups and independent researchers. One way to address it is for several smaller groups to pool their computational resources together and train a model that benefits all participants. Unfortunately, in this case, any participant can jeopardize the entire training run by sending incorrect updates, deliberately or by mistake. Training in presence of such peers requires specialized distributed training algorithms with Byzantine tolerance. These algorithms often sacrifice efficiency by introducing redundant communication or passing all updates through a trusted server, making it infeasible to apply them to large-scale deep learning, where models can have billions of parameters. In this work, we propose a novel protocol for secure (Byzantine-tolerant) decentralized training that emphasizes communication efficiency.  ( 2 min )
    Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks. (arXiv:2202.01940v2 [stat.ML] UPDATED)
    We propose Distribution Embedding Networks (DEN) for classification with small data. In the same spirit of meta-learning, DEN learns from a diverse set of training tasks with the goal to generalize to unseen target tasks. Unlike existing approaches which require the inputs of training and target tasks to have the same dimension with possibly similar distributions, DEN allows training and target tasks to live in heterogeneous input spaces. This is especially useful for tabular-data tasks where labeled data from related tasks are scarce. DEN uses a three-block architecture: a covariate transformation block followed by a distribution embedding block and then a classification block. We provide theoretical insights to show that this architecture allows the embedding and classification blocks to be fixed after pre-training on a diverse set of tasks; only the covariate transformation block with relatively few parameters needs to be fine-tuned for each new task. To facilitate training, we also propose an approach to synthesize binary classification tasks, and demonstrate that DEN outperforms existing methods in a number of synthetic and real tasks in numerical studies.  ( 2 min )
    Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems. (arXiv:2203.11812v2 [eess.SY] UPDATED)
    We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effective solution based on convex programming. Beyond this case, a globally optimal solution cannot be found in a tractable way, in general. In this paper, we develop a parametrization of all and only the control policies stabilizing a given time-varying nonlinear system in terms of the combined effect of 1) a strongly stabilizing base controller and 2) a stable SLS operator to be freely designed. Based on this result, we propose a Neural SLS (Neur-SLS) approach guaranteeing closed-loop stability during and after parameter optimization, without requiring any constraints to be satisfied. We exploit recent Deep Neural Network (DNN) models based on Recurrent Equilibrium Networks (RENs) to learn over a rich class of nonlinear stable operators, and demonstrate the effectiveness of the proposed approach in numerical examples.  ( 2 min )
    Low-Rank Updates of Matrix Square Roots. (arXiv:2201.13156v2 [math.NA] UPDATED)
    Models in which the covariance matrix has the structure of a sparse matrix plus a low rank perturbation are ubiquitous in machine learning applications. It is often desirable for learning algorithms to take advantage of such structures, avoiding costly matrix computations that often require cubic time and quadratic storage. This is often accomplished by performing operations that maintain such structures, e.g. matrix inversion via the Sherman-Morrison-Woodbury formula. In this paper we consider the matrix square root and inverse square root operations. Given a low rank perturbation to a matrix, we argue that a low-rank approximate correction to the (inverse) square root exists. We do so by establishing a geometric decay bound on the true correction's eigenvalues. We then proceed to frame the correction has the solution of an algebraic Ricatti equation, and discuss how a low-rank solution to that equation can be computed. We analyze the approximation error incurred when approximately solving the algebraic Ricatti equation, providing spectral and Frobenius norm forward and backward error bounds. Finally, we describe several applications of our algorithms, and demonstrate their utility in numerical experiments.  ( 2 min )
    On the Importance of Regularisation & Auxiliary Information in OOD Detection. (arXiv:2107.07564v2 [cs.LG] UPDATED)
    Neural networks are often utilised in critical domain applications (e.g. self-driving cars, financial markets, and aerospace engineering), even though they exhibit overconfident predictions for ambiguous inputs. This deficiency demonstrates a fundamental flaw indicating that neural networks often overfit on spurious correlations. To address this problem in this work we present two novel objectives that improve the ability of a network to detect out-of-distribution samples and therefore avoid overconfident predictions for ambiguous inputs. We empirically demonstrate that our methods outperform the baseline and perform better than the majority of existing approaches while still maintaining a competitive performance against the rest. Additionally, we empirically demonstrate the robustness of our approach against common corruptions and demonstrate the importance of regularisation and auxiliary information in out-of-distribution detection.  ( 2 min )
    CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents. (arXiv:2110.10067v2 [cs.LG] UPDATED)
    Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this work, we present CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package. The benchmarks we provide are designed to evaluate different aspects of the continual RL challenge, such as catastrophic forgetting, plasticity, ability to generalize, and sample-efficient learning. Three of the benchmarks utilize video game environments (Atari, Procgen, NetHack). The fourth benchmark, CHORES, consists of four different task sequences in a visually realistic home simulator, drawn from a diverse set of task and scene parameters. To compare continual RL methods on these benchmarks, we prepare three metrics in CORA: Continual Evaluation, Isolated Forgetting, and Zero-Shot Forward Transfer. Finally, CORA includes a set of performant, open-source baselines of existing algorithms for researchers to use and expand on. We release CORA and hope that the continual RL community can benefit from our contributions, to accelerate the development of new continual RL algorithms.  ( 2 min )
    On minimizers and convolutional filters: a partial justification for the effectiveness of CNNs in categorical sequence analysis. (arXiv:2111.08452v4 [cs.LG] UPDATED)
    Minimizers and convolutional neural networks (CNNs) are two quite distinct popular techniques that have both been employed to analyze categorical biological sequences. At face value, the methods seem entirely dissimilar. Minimizers use min-wise hashing on a rolling window to extract a single important k-mer feature per window. CNNs start with a wide array of randomly initialized convolutional filters, paired with a pooling operation, and then multiple additional neural layers to learn both the filters themselves and how those filters can be used to classify the sequence. In this manuscript, we demonstrate through a careful mathematical analysis of hash function properties that for sequences over a categorical alphabet, random Gaussian initialization of convolutional filters with max-pooling is equivalent to choosing a minimizer ordering such that selected k-mers are (in Hamming distance) far from the k-mers within the sequence but close to other minimizers. In empirical experiments, we find that this property manifests as decreased density in repetitive regions, both in simulation and on real human telomeres. This provides a partial explanation for the effectiveness of CNNs in categorical sequence analysis.  ( 2 min )
    InfoFair: Information-Theoretic Intersectional Fairness. (arXiv:2105.11069v2 [cs.LG] UPDATED)
    Algorithmic fairness is becoming increasingly important in data mining and machine learning. Among others, a foundational notation is group fairness. The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e.g., gender, race, marital status, etc.) in the real-world is commonplace. As such, methods that can ensure a fair learning outcome with respect to all sensitive attributes of concern simultaneously need to be developed. In this paper, we study the problem of information-theoretic intersectional fairness (InfoFair), where statistical parity, a representative group fairness measure, is guaranteed among demographic groups formed by multiple sensitive attributes of interest. We formulate it as a mutual information minimization problem and propose a generic end-to-end algorithmic framework to solve it. The key idea is to leverage a variational representation of mutual information, which considers the variational distribution between learning outcomes and sensitive attributes, as well as the density ratio between the variational and the original distributions. Our proposed framework is generalizable to many different settings, including other statistical notions of fairness, and could handle any type of learning task equipped with a gradient-based optimizer. Empirical evaluations in the fair classification task on three real-world datasets demonstrate that our proposed framework can effectively debias the classification results with minimal impact to the classification accuracy.  ( 2 min )
    The Fragility of Optimized Bandit Algorithms. (arXiv:2109.13595v5 [cs.LG] UPDATED)
    Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that designs that are optimal over certain exponential families can achieve expected regret that grows logarithmically in the number of arm plays, at a rate governed by the Lai-Robbins lower bound. In this paper, we show that when one uses such optimized designs, the regret distribution of the associated algorithms necessarily has a very heavy tail, specifically, that of a truncated Cauchy distribution. Furthermore, for $p>1$, the $p$'th moment of the regret distribution grows much faster than poly-logarithmically, in particular as a power of the total number of arm plays. We show that optimized UCB bandit designs are also fragile in an additional sense, namely when the problem is even slightly mis-specified, the regret can grow much faster than the conventional theory suggests. Our arguments are based on standard change-of-measure ideas, and indicate that the most likely way that regret becomes larger than expected is when the optimal arm returns below-average rewards in the first few arm plays, thereby causing the algorithm to believe that the arm is sub-optimal. To alleviate the fragility issues exposed, we show that UCB algorithms can be modified so as to ensure a desired degree of robustness to mis-specification. In doing so, we also provide a sharp trade-off between the amount of UCB exploration and the tail exponent of the resulting regret distribution.  ( 2 min )
    Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems. (arXiv:2109.02384v3 [math.OC] UPDATED)
    In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able to be predicted from the measured system outputs. Similar derivations have been done before but not using state-space representation.  ( 2 min )
    HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data. (arXiv:2011.01805v3 [cs.CR] UPDATED)
    Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an HE-friendly version of AlexNet, which runs in three minutes, several orders of magnitude faster than other state-of-the-art solutions that only use HE.  ( 2 min )
    Sinkhorn Distributionally Robust Optimization. (arXiv:2109.11926v2 [math.OC] UPDATED)
    We study distributionally robust optimization (DRO) with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization. We provide convex programming dual reformulation for a general nominal distribution. Compared with Wasserstein DRO, it is computationally tractable for a larger class of loss functions, and its worst-case distribution is more reasonable. We propose an efficient first-order algorithm with bisection search to solve the dual reformulation. We demonstrate that our proposed algorithm finds $\delta$-optimal solution of the new DRO formulation with computation cost $\tilde{O}(\delta^{-3})$ and memory cost $\tilde{O}(\delta^{-2})$, and the computation cost further improves to $\tilde{O}(\delta^{-2})$ when the loss function is smooth. Finally, we provide various numerical examples using both synthetic and real data to demonstrate its competitive performance and light computational speed.  ( 2 min )
    Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning. (arXiv:2012.08023v3 [math.NA] UPDATED)
    This paper proposes Friedrichs learning as a novel deep learning methodology that can learn the weak solutions of PDEs via a minmax formulation, which transforms the PDE problem into a minimax optimization problem to identify weak solutions. The name "Friedrichs learning" is for highlighting the close relationship between our learning strategy and Friedrichs theory on symmetric systems of PDEs. The weak solution and the test function in the weak formulation are parameterized as deep neural networks in a mesh-free manner, which are alternately updated to approach the optimal solution networks approximating the weak solution and the optimal test function, respectively. Extensive numerical results indicate that our mesh-free method can provide reasonably good solutions to a wide range of PDEs defined on regular and irregular domains in various dimensions, where classical numerical methods such as finite difference methods and finite element methods may be tedious or difficult to be applied.  ( 2 min )
    Fusing Models for Prognostics and Health Management of Lithium-Ion Batteries Based on Physics-Informed Neural Networks. (arXiv:2301.00776v1 [eess.SP])
    For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.  ( 2 min )
    Causal Inference (C-inf) -- closed form worst case typical phase transitions. (arXiv:2301.00793v1 [stat.ML])
    In this paper we establish a mathematically rigorous connection between Causal inference (C-inf) and the low-rank recovery (LRR). Using Random Duality Theory (RDT) concepts developed in [46,48,50] and novel mathematical strategies related to free probability theory, we obtain the exact explicit typical (and achievable) worst case phase transitions (PT). These PT precisely separate scenarios where causal inference via LRR is possible from those where it is not. We supplement our mathematical analysis with numerical experiments that confirm the theoretical predictions of PT phenomena, and further show that the two closely match for fairly small sample sizes. We obtain simple closed form representations for the resulting PTs, which highlight direct relations between the low rankness of the target C-inf matrix and the time of the treatment. Hence, our results can be used to determine the range of C-inf's typical applicability.  ( 2 min )
    Projection Robust Wasserstein Distance and Riemannian Optimization. (arXiv:2006.07458v10 [cs.LG] UPDATED)
    Projection robust Wasserstein (PRW) distance, or Wasserstein projection pursuit (WPP), is a robust variant of the Wasserstein distance. Recent work suggests that this quantity is more robust than the standard Wasserstein distance, in particular when comparing probability measures in high-dimensions. However, it is ruled out for practical application because the optimization model is essentially non-convex and non-smooth which makes the computation intractable. Our contribution in this paper is to revisit the original motivation behind WPP/PRW, but take the hard route of showing that, despite its non-convexity and lack of nonsmoothness, and even despite some hardness results proved by~\citet{Niles-2019-Estimation} in a minimax sense, the original formulation for PRW/WPP \textit{can} be efficiently computed in practice using Riemannian optimization, yielding in relevant cases better behavior than its convex relaxation. More specifically, we provide three simple algorithms with solid theoretical guarantee on their complexity bound (one in the appendix), and demonstrate their effectiveness and efficiency by conducing extensive experiments on synthetic and real data. This paper provides a first step into a computational theory of the PRW distance and provides the links between optimal transport and Riemannian optimization.  ( 3 min )
    Robust machine learning pipelines for trading market-neutral stock portfolios. (arXiv:2301.00790v1 [q-fin.CP])
    The application of deep learning algorithms to financial data is difficult due to heavy non-stationarities which can lead to over-fitted models that underperform under regime changes. Using the Numerai tournament data set as a motivating example, we propose a machine learning pipeline for trading market-neutral stock portfolios based on tabular data which is robust under changes in market conditions. We evaluate various machine-learning models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and without simple feature engineering, as the building blocks for the pipeline. We find that GBDT models with dropout display high performance, robustness and generalisability with relatively low complexity and reduced computational cost. We then show that online learning techniques can be used in post-prediction processing to enhance the results. In particular, dynamic feature neutralisation, an efficient procedure that requires no retraining of models and can be applied post-prediction to any machine learning model, improves robustness by reducing drawdown in volatile market conditions. Furthermore, we demonstrate that the creation of model ensembles through dynamic model selection based on recent model performance leads to improved performance over baseline by improving the Sharpe and Calmar ratios. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility of results.  ( 2 min )
    Causal Inference (C-inf) -- asymmetric scenario of typical phase transitions. (arXiv:2301.00801v1 [stat.ML])
    In this paper, we revisit and further explore a mathematically rigorous connection between Causal inference (C-inf) and the Low-rank recovery (LRR) established in [10]. Leveraging the Random duality - Free probability theory (RDT-FPT) connection, we obtain the exact explicit typical C-inf asymmetric phase transitions (PT). We uncover a doubling low-rankness phenomenon, which means that exactly two times larger low rankness is allowed in asymmetric scenarios compared to the symmetric worst case ones considered in [10]. Consequently, the final PT mathematical expressions are as elegant as those obtained in [10], and highlight direct relations between the targeted C-inf matrix low rankness and the time of treatment. Our results have strong implications for applications, where C-inf matrices are not necessarily symmetric.  ( 2 min )
    G-CEALS: Gaussian Cluster Embedding in Autoencoder Latent Space for Tabular Data Representation. (arXiv:2301.00802v1 [cs.LG])
    The latent space of autoencoders has been improved for clustering image data by jointly learning a t-distributed embedding with a clustering algorithm inspired by the neighborhood embedding concept proposed for data visualization. However, multivariate tabular data pose different challenges in representation learning than image data, where traditional machine learning is often superior to deep tabular data learning. In this paper, we address the challenges of learning tabular data in contrast to image data and present a novel Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS) algorithm by replacing t-distributions with multivariate Gaussian clusters. Unlike current methods, the proposed approach independently defines the Gaussian embedding and the target cluster distribution to accommodate any clustering algorithm in representation learning. A trained G-CEALS model extracts a quality embedding for unseen test data. Based on the embedding clustering accuracy, the average rank of the proposed G-CEALS method is 1.4 (0.7), which is superior to all eight baseline clustering and cluster embedding methods on seven tabular data sets. This paper shows one of the first algorithms to jointly learn embedding and clustering to improve multivariate tabular data representation in downstream clustering.  ( 2 min )
    On Bilevel Optimization without Lower-level Strong Convexity. (arXiv:2301.00712v1 [math.OC])
    Theoretical properties of bilevel problems are well studied when the lower-level problem is strongly convex. In this work, we focus on bilevel optimization problems without the strong-convexity assumption. In these cases, we first show that the common local optimality measures such as KKT condition or regularization can lead to undesired consequences. Then, we aim to identify the mildest conditions that make bilevel problems tractable. We identify two classes of growth conditions on the lower-level objective that leads to continuity. Under these assumptions, we show that the local optimality of the bilevel problem can be defined via the Goldstein stationarity condition of the hyper-objective. We then propose the Inexact Gradient-Free Method (IGFM) to solve the bilevel problem, using an approximate zeroth order oracle that is of independent interest. Our non-asymptotic analysis demonstrates that the proposed method can find a $(\delta, \varepsilon)$ Goldstein stationary point for bilevel problems with a zeroth order oracle complexity that is polynomial in $d, 1/\delta$ and $1/\varepsilon$.  ( 2 min )
    CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection. (arXiv:2301.00785v1 [eess.IV])
    An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.  ( 2 min )
    Muse: Text-To-Image Generation via Masked Generative Transformers. (arXiv:2301.00704v1 [cs.CV])
    We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io  ( 2 min )
    SIRL: Similarity-based Implicit Representation Learning. (arXiv:2301.00810v1 [cs.RO])
    When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.  ( 3 min )
    Human-in-the-Loop Hate Speech Classification in a Multilingual Context. (arXiv:2212.02108v2 [cs.CL] UPDATED)
    The shift of public debate to the digital sphere has been accompanied by a rise in online hate speech. While many promising approaches for hate speech classification have been proposed, studies often focus only on a single language, usually English, and do not address three key concerns: post-deployment performance, classifier maintenance and infrastructural limitations. In this paper, we introduce a new human-in-the-loop BERT-based hate speech classification pipeline and trace its development from initial data collection and annotation all the way to post-deployment. Our classifier, trained using data from our original corpus of over 422k examples, is specifically developed for the inherently multilingual setting of Switzerland and outperforms with its F1 score of 80.5 the currently best-performing BERT-based multilingual classifier by 5.8 F1 points in German and 3.6 F1 points in French. Our systematic evaluations over a 12-month period further highlight the vital importance of continuous, human-in-the-loop classifier maintenance to ensure robust hate speech classification post-deployment.  ( 2 min )
    Graph Construction from Data using Non Negative Kernel regression (NNK Graphs). (arXiv:1910.09383v2 [cs.LG] UPDATED)
    Data-driven neighborhood definitions and graph constructions are often used in machine learning and signal processing applications. k-nearest neighbor~(kNN) and $\epsilon$-neighborhood methods are among the most common methods used for neighborhood selection, due to their computational simplicity. However, the choice of parameters associated with these methods, such as k and $\epsilon$, is still ad hoc. We make two main contributions in this paper. First, we present an alternative view of neighborhood selection, where we show that neighborhood construction is equivalent to a sparse signal approximation problem. Second, we propose an algorithm, non-negative kernel regression~(NNK), for obtaining neighborhoods that lead to better sparse representation. NNK draws similarities to the orthogonal matching pursuit approach to signal representation and possesses desirable geometric and theoretical properties. Experiments demonstrate (i) the robustness of the NNK algorithm for neighborhood and graph construction, (ii) its ability to adapt the number of neighbors to the data properties, and (iii) its superior performance in local neighborhood and graph-based machine learning tasks.  ( 2 min )
    PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis. (arXiv:2301.00772v1 [cs.CV])
    Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.  ( 2 min )
    Massive Language Models Can Be Accurately Pruned in One-Shot. (arXiv:2301.00774v1 [cs.LG])
    We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT, specifically designed to work efficiently and accurately on massive GPT-family models. When executing SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, we can reach 60% sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time. SparseGPT generalizes to semi-structured (2:4 and 4:8) patterns, and is compatible with weight quantization approaches.  ( 2 min )
    A Survey on Federated Recommendation Systems. (arXiv:2301.00767v1 [cs.IR])
    Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of the real user data, which greatly enhances the user privacy. Beside, federated recommendation systems enable to collaborate with other data platforms to improve recommended model performance while meeting the regulation and privacy constraints. However, federated recommendation systems faces many new challenges such as privacy, security, heterogeneity and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this survey, we-(1) summarize some common privacy mechanisms used in federated recommendation systems and discuss the advantages and limitations of each mechanism; (2) review some robust aggregation strategies and several novel attacks against security; (3) summarize some approaches to address heterogeneity and communication costs problems; (4)introduce some open source platforms that can be used to build federated recommendation systems; (5) present some prospective research directions in the future. This survey can guide researchers and practitioners understand the research progress in these areas.  ( 2 min )
    Training Differentially Private Graph Neural Networks with Random Walk Sampling. (arXiv:2301.00738v1 [cs.LG])
    Deep learning models are known to put the privacy of their training data at risk, which poses challenges for their safe and ethical release to the public. Differentially private stochastic gradient descent is the de facto standard for training neural networks without leaking sensitive information about the training data. However, applying it to models for graph-structured data poses a novel challenge: unlike with i.i.d. data, sensitive information about a node in a graph cannot only leak through its gradients, but also through the gradients of all nodes within a larger neighborhood. In practice, this limits privacy-preserving deep learning on graphs to very shallow graph neural networks. We propose to solve this issue by training graph neural networks on disjoint subgraphs of a given training graph. We develop three random-walk-based methods for generating such disjoint subgraphs and perform a careful analysis of the data-generating distributions to provide strong privacy guarantees. Through extensive experiments, we show that our method greatly outperforms the state-of-the-art baseline on three large graphs, and matches or outperforms it on four smaller ones.  ( 2 min )
    Robust Consensus Clustering and its Applications for Advertising Forecasting. (arXiv:2301.00717v1 [cs.LG])
    Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions. In practice, there exist noise and outliers in clustering task, which, however, may significantly degrade the performance. To address this issue, we propose a novel algorithm -- robust consensus clustering that can find common ground truth among experts' opinions, which tends to be minimally affected by the bias caused by the outliers. In particular, we formalize the robust consensus clustering problem as a constraint optimization problem, and then derive an effective algorithm upon alternating direction method of multipliers (ADMM) with rigorous convergence guarantee. Our method outperforms the baselines on benchmarks. We apply the proposed method to the real-world advertising campaign segmentation and forecasting tasks using the proposed consensus clustering results based on the similarity computed via Kolmogorov-Smirnov Statistics. The accurate clustering result is helpful for building the advertiser profiles so as to perform the forecasting.  ( 2 min )
    Product Ranking for Revenue Maximization with Multiple Purchases. (arXiv:2210.08268v3 [cs.LG] UPDATED)
    Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively. Under this setting, we first design an optimal ranking policy when the online retailer can precisely model consumers' behaviors. Based on the policy, we further develop the Multiple-Purchase-with-Budget UCB (MPB-UCB) algorithms with $\~O(\sqrt{T})$ regret that estimate consumers' behaviors and maximize revenue simultaneously in online settings. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.  ( 2 min )
    Ontology-based Context Aware Recommender System Application for Tourism. (arXiv:2301.00768v1 [cs.IR])
    In this work a novel recommender system (RS) for Tourism is presented. The RS is context aware as is now the rule in the state-of-the-art for recommender systems and works on top of a tourism ontology which is used to group the different items being offered. The presented RS mixes different types of recommenders creating an ensemble which changes on the basis of the RS's maturity. Starting from simple content-based recommendations and iteratively adding popularity, demographic and collaborative filtering methods as rating density and user cardinality increases. The result is a RS that mutates during its lifetime and uses a tourism ontology and natural language processing (NLP) to correctly bin the items to specific item categories and meta categories in the ontology. This item classification facilitates the association between user preferences and items, as well as allowing to better classify and group the items being offered, which in turn is particularly useful for context-aware filtering.  ( 2 min )
    Online Linearized LASSO. (arXiv:2211.06039v2 [stat.ML] UPDATED)
    Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model. Existing approaches focus on offline regularized regression, while the online scenario has rarely been studied. In this paper, we propose a novel online sparse linear regression framework for analyzing streaming data when data points arrive sequentially. Our proposed method is memory efficient and requires less stringent restricted strong convexity assumptions. Theoretically, we show that with a properly chosen regularization parameter, the $\ell_2$-norm statistical error of our estimator diminishes to zero in the optimal order of $\tilde{O}({\sqrt{s/t}})$, where $s$ is the sparsity level, $t$ is the streaming sample size, and $\tilde{O}(\cdot)$ hides logarithmic terms. Numerical experiments demonstrate the practical efficiency of our algorithm.  ( 2 min )
    Detection of Groups with Biased Representation in Ranking. (arXiv:2301.00719v1 [cs.LG])
    Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measures for fairness in ranking. Many of those definitions consider the representation of different ``protected groups'', in the top-$k$ ranked items, for any reasonable $k$. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. In this paper, we study the problem of detecting groups with biased representation in the top-$k$ ranked items, eliminating the need to pre-define protected groups. The number of such groups possible can be exponential, making the problem hard. We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation. Then we propose a method to explain the bias in the representations of groups utilizing the notion of Shapley values. We conclude with an experimental study, showing the scalability of our approach and demonstrating the usefulness of the proposed algorithms.  ( 2 min )
    Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2. (arXiv:2210.09028v2 [cs.CR] UPDATED)
    Did you know that over 70 million of Dota2 players have their in-game data freely accessible? What if such data is used in malicious ways? This paper is the first to investigate such a problem. Motivated by the widespread popularity of video games, we propose the first threat model for Attribute Inference Attacks (AIA) in the Dota2 context. We explain how (and why) attackers can exploit the abundant public data in the Dota2 ecosystem to infer private information about its players. Due to lack of concrete evidence on the efficacy of our AIA, we empirically prove and assess their impact in reality. By conducting an extensive survey on $\sim$500 Dota2 players spanning over 26k matches, we verify whether a correlation exists between a player's Dota2 activity and their real-life. Then, after finding such a link ($p\!0.3$), we ethically perform diverse AIA. We leverage the capabilities of machine learning to infer real-life attributes of the respondents of our survey by using their publicly available in-game data. Our results show that, by applying domain expertise, some AIA can reach up to 98% precision and over 90% accuracy. This paper hence raises the alarm on a subtle, but concrete threat that can potentially affect the entire competitive gaming landscape. We alerted the developers of Dota2.  ( 2 min )
    Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation. (arXiv:2211.03364v6 [eess.IV] UPDATED)
    Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen and Stable Diffusion. However, their use in medicine, where image data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy preserving artificial intelligence and can also be used to augment small datasets. Here we show that diffusion probabilistic models can synthesize high quality medical imaging data, which we show for Magnetic Resonance Images (MRI) and Computed Tomography (CT) images. We provide quantitative measurements of their performance through a reader study with two medical experts who rated the quality of the synthesized images in three categories: Realistic image appearance, anatomical correctness and consistency between slices. Furthermore, we demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (dice score 0.91 vs. 0.95 without vs. with synthetic data).  ( 2 min )
    The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity. (arXiv:2211.04171v3 [math.OC] UPDATED)
    The problem of approximating the Pareto front of a multiobjective optimization problem can be reformulated as the problem of finding a set that maximizes the hypervolume indicator. This paper establishes the analytical expression of the Hessian matrix of the mapping from a (fixed size) collection of $n$ points in the $d$-dimensional decision space (or $m$ dimensional objective space) to the scalar hypervolume indicator value. To define the Hessian matrix, the input set is vectorized, and the matrix is derived by analytical differentiation of the mapping from a vectorized set to the hypervolume indicator. The Hessian matrix plays a crucial role in second-order methods, such as the Newton-Raphson optimization method, and it can be used for the verification of local optimal sets. So far, the full analytical expression was only established and analyzed for the relatively simple bi-objective case. This paper will derive the full expression for arbitrary dimensions ($m\geq2$ objective functions). For the practically important three-dimensional case, we also provide an asymptotically efficient algorithm with time complexity in $O(n\log n)$ for the exact computation of the Hessian Matrix' non-zero entries. We establish a sharp bound of $12m-6$ for the number of non-zero entries. Also, for the general $m$-dimensional case, a compact recursive analytical expression is established, and its algorithmic implementation is discussed. Also, for the general case, some sparsity results can be established; these results are implied by the recursive expression. To validate and illustrate the analytically derived algorithms and results, we provide a few numerical examples using Python and Mathematica implementations. Open-source implementations of the algorithms and testing data are made available as a supplement to this paper.  ( 2 min )
    Pseudo AI Bias. (arXiv:2210.08141v2 [cs.AI] UPDATED)
    Pseudo Artificial Intelligence bias (PAIB) is broadly disseminated in the literature, which can result in unnecessary AI fear in society, exacerbate the enduring inequities and disparities in access to and sharing the benefits of AI applications, and waste social capital invested in AI research. This study systematically reviews publications in the literature to present three types of PAIBs identified due to: a) misunderstandings, b) pseudo mechanical bias, and c) over-expectations. We discussed the consequences of and solutions to PAIBs, including certifying users for AI applications to mitigate AI fears, providing customized user guidance for AI applications, and developing systematic approaches to monitor bias. We concluded that PAIB due to misunderstandings, pseudo mechanical bias, and over-expectations of algorithmic predictions is socially harmful.  ( 2 min )
    Temporally Layered Architecture for Adaptive, Distributed and Continuous Control. (arXiv:2301.00723v1 [cs.NE])
    We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands. Such distributed control design is widespread across biological systems because it increases survivability and accuracy in certain and uncertain environments. We demonstrate that TLA can provide many advantages over existing approaches, including persistent exploration, adaptive control, explainable temporal behavior, compute efficiency and distributed control. We present two different algorithms for training TLA: (a) Closed-loop control, where the fast controller is trained over a pre-trained slow controller, allowing better exploration for the fast controller and closed-loop control where the fast controller decides whether to "act-or-not" at each timestep; and (b) Partially open loop control, where the slow controller is trained over a pre-trained fast controller, allowing for open loop-control where the slow controller picks a temporally extended action or defers the next n-actions to the fast controller. We evaluated our method on a suite of continuous control tasks and demonstrate the advantages of TLA over several strong baselines.  ( 2 min )
    Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management. (arXiv:2301.00641v1 [eess.SY])
    The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.  ( 3 min )
    IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale. (arXiv:2301.00716v1 [cs.LG])
    We address the challenge of building domain-specific knowledge models for industrial use cases, where labelled data and taxonomic information is initially scarce. Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discovering and linking new (so-called open-world) entities to the knowledge graph. We argue that - though neural approaches to text mining have yielded impressive results in the past years - current benchmarks do not reflect the typical challenges encountered in the industrial wild properly. Therefore, our first contribution is an open benchmark coined IRT2 (inductive reasoning with text) that (1) covers knowledge graphs of varying sizes (including very small ones), (2) comes with incidental, low-quality text mentions, and (3) includes not only triple completion but also ranking, which is relevant for supporting experts with discovery tasks. We investigate two neural models for inductive link prediction, one based on end-to-end learning and one that learns from the knowledge graph and text data in separate steps. These models compete with a strong bag-of-words baseline. The results show a significant advance in performance for the neural approaches as soon as the available graph data decreases for linking. For ranking, the results are promising, and the neural approaches outperform the sparse retriever by a wide margin.  ( 2 min )
    E-commerce users' preferences for delivery options. (arXiv:2301.00666v1 [econ.GN])
    Many e-commerce marketplaces offer their users fast delivery options for free to meet the increasing needs of users, imposing an excessive burden on city logistics. Therefore, understanding e-commerce users' preference for delivery options is a key to designing logistics policies. To this end, this study designs a stated choice survey in which respondents are faced with choice tasks among different delivery options and time slots, which was completed by 4,062 users from the three major metropolitan areas in Japan. To analyze the data, mixed logit models capturing taste heterogeneity as well as flexible substitution patterns have been estimated. The model estimation results indicate that delivery attributes including fee, time, and time slot size are significant determinants of the delivery option choices. Associations between users' preferences and socio-demographic characteristics, such as age, gender, teleworking frequency and the presence of a delivery box, were also suggested. Moreover, we analyzed two willingness-to-pay measures for delivery, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS), and applied a non-semiparametric approach to estimate their distributions in a data-oriented manner. Although VODT has a large heterogeneity among respondents, the estimated median VODT is 25.6 JPY/day, implying that more than half of the respondents would wait an additional day if the delivery fee were increased by only 26 JPY, that is, they do not necessarily need a fast delivery option but often request it when cheap or almost free. Moreover, VOTS was found to be low, distributed with the median of 5.0 JPY/hour; that is, users do not highly value the reduction in time slot size in monetary terms. These findings on e-commerce users' preferences can help in designing levels of service for last-mile delivery to significantly improve its efficiency.  ( 2 min )
    New Designed Loss Functions to Solve Ordinary Differential Equations with Artificial Neural Network. (arXiv:2301.00636v1 [cs.LG])
    This paper investigates the use of artificial neural networks (ANNs) to solve differential equations (DEs) and the construction of the loss function which meets both differential equation and its initial/boundary condition of a certain DE. In section 2, the loss function is generalized to $n^\text{th}$ order ordinary differential equation(ODE). Other methods of construction are examined in Section 3 and applied to three different models to assess their effectiveness.  ( 2 min )
    Mixed moving average field guided learning for spatio-temporal data. (arXiv:2301.00736v1 [stat.ML])
    Influenced mixed moving average fields are a versatile modeling class for spatio-temporal data. However, their predictive distribution is not generally accessible. Under this modeling assumption, we define a novel theory-guided machine learning approach that employs a generalized Bayesian algorithm to make predictions. We employ a Lipschitz predictor, for example, a linear model or a feed-forward neural network, and determine a randomized estimator by minimizing a novel PAC Bayesian bound for data serially correlated along a spatial and temporal dimension. Performing causal future predictions is a highlight of our methodology as its potential application to data with short and long-range dependence. We conclude by showing the performance of the learning methodology in an example with linear predictors and simulated spatio-temporal data from an STOU process.  ( 2 min )
    Reinforcement Learning with Success Induced Task Prioritization. (arXiv:2301.00691v1 [cs.LG])
    Many challenging reinforcement learning (RL) problems require designing a distribution of tasks that can be applied to train effective policies. This distribution of tasks can be specified by the curriculum. A curriculum is meant to improve the results of learning and accelerate it. We introduce Success Induced Task Prioritization (SITP), a framework for automatic curriculum learning, where a task sequence is created based on the success rate of each task. In this setting, each task is an algorithmically created environment instance with a unique configuration. The algorithm selects the order of tasks that provide the fastest learning for agents. The probability of selecting any of the tasks for the next stage of learning is determined by evaluating its performance score in previous stages. Experiments were carried out in the Partially Observable Grid Environment for Multiple Agents (POGEMA) and Procgen benchmark. We demonstrate that SITP matches or surpasses the results of other curriculum design methods. Our method can be implemented with handful of minor modifications to any standard RL framework and provides useful prioritization with minimal computational overhead.  ( 2 min )
    Tsetlin Machine Embedding: Representing Words Using Logical Expressions. (arXiv:2301.00709v1 [cs.CL])
    Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that captures the context of words using a neural network. Similarly, GLoVe is a popular unsupervised model incorporating corpus-wide word co-occurrence statistics. Such word embedding has significantly boosted important NLP tasks, including sentiment analysis, document classification, and machine translation. However, the embeddings are dense floating-point vectors, making them expensive to compute and difficult to interpret. In this paper, we instead propose to represent the semantics of words with a few defining words that are related using propositional logic. To produce such logical embeddings, we introduce a Tsetlin Machine-based autoencoder that learns logical clauses self-supervised. The clauses consist of contextual words like "black," "cup," and "hot" to define other words like "coffee," thus being human-understandable. We evaluate our embedding approach on several intrinsic and extrinsic benchmarks, outperforming GLoVe on six classification tasks. Furthermore, we investigate the interpretability of our embedding using the logical representations acquired during training. We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.  ( 2 min )
    A RL-based Policy Optimization Method Guided by Adaptive Stability Certification. (arXiv:2301.00521v1 [cs.RO])
    In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.  ( 2 min )
    Federated Learning with Client-Exclusive Classes. (arXiv:2301.00489v1 [cs.LG])
    Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.  ( 2 min )
    Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning. (arXiv:2301.00452v1 [cs.RO])
    Surgical robot automation has attracted increasing research interest over the past decade, expecting its huge potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied AI has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant researchers. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how human demonstrations would affect policy learning. In this paper, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. With these, we further propose to collect human demonstrations and imitate the action patterns to achieve more effective policy learning. We showcase the improvement of our simulation environment with the designed new features and tasks, and validate state-of-the-art reinforcement learning algorithms using the interactive environment. Promising results are obtained, with which we hope to pave the way for future research on surgical embodied intelligence. Our platform is released and will be continuously updated in the website: https://med-air.github.io/SurRoL/  ( 2 min )
    A principled distributional approach to trajectory similarity measurement. (arXiv:2301.00393v1 [cs.LG])
    Existing measures and representations for trajectories have two longstanding fundamental shortcomings, i.e., they are computationally expensive and they can not guarantee the `uniqueness' property of a distance function: dist(X,Y) = 0 if and only if X=Y, where $X$ and $Y$ are two trajectories. This paper proposes a simple yet powerful way to represent trajectories and measure the similarity between two trajectories using a distributional kernel to address these shortcomings. It is a principled approach based on kernel mean embedding which has a strong theoretical underpinning. It has three distinctive features in comparison with existing approaches. (1) A distributional kernel is used for the very first time for trajectory representation and similarity measurement. (2) It does not rely on point-to-point distances which are used in most existing distances for trajectories. (3) It requires no learning, unlike existing learning and deep learning approaches. We show the generality of this new approach in three applications: (a) trajectory anomaly detection, (b) anomalous sub-trajectory detection, and (c) trajectory pattern mining. We identify that the distributional kernel has (i) a unique data-dependent property and the above uniqueness property which are the key factors that lead to its superior task-specific performance; and (ii) runtime orders of magnitude faster than existing distance measures.  ( 2 min )
    Hierarchical Explanations for Video Action Recognition. (arXiv:2301.00436v1 [cs.CV])
    We propose Hierarchical ProtoPNet: an interpretable network that explains its reasoning process by considering the hierarchical relationship between classes. Different from previous methods that explain their reasoning process by dissecting the input image and finding the prototypical parts responsible for the classification, we propose to explain the reasoning process for video action classification by dissecting the input video frames on multiple levels of the class hierarchy. The explanations leverage the hierarchy to deal with uncertainty, akin to human reasoning: When we observe water and human activity, but no definitive action it can be recognized as the water sports parent class. Only after observing a person swimming can we definitively refine it to the swimming action. Experiments on ActivityNet and UCF-101 show performance improvements while providing multi-level explanations.  ( 2 min )
    Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control. (arXiv:2301.00497v1 [cs.LG])
    Projection operations are a typical computation bottleneck in online learning. In this paper, we enable projection-free online learning within the framework of Online Convex Optimization with Memory (OCO-M) -- OCO-M captures how the history of decisions affects the current outcome by allowing the online learning loss functions to depend on both current and past decisions. Particularly, we introduce the first projection-free meta-base learning algorithm with memory that minimizes dynamic regret, i.e., that minimizes the suboptimality against any sequence of time-varying decisions. We are motivated by artificial intelligence applications where autonomous agents need to adapt to time-varying environments in real-time, accounting for how past decisions affect the present. Examples of such applications are: online control of dynamical systems; statistical arbitrage; and time series prediction. The algorithm builds on the Online Frank-Wolfe (OFW) and Hedge algorithms. We demonstrate how our algorithm can be applied to the online control of linear time-varying systems in the presence of unpredictable process noise. To this end, we develop the first controller with memory and bounded dynamic regret against any optimal time-varying linear feedback control policy. We validate our algorithm in simulated scenarios of online control of linear time-invariant systems.  ( 2 min )
    FedICT: Federated Multi-task Distillation for Multi-access Edge Computing. (arXiv:2301.00389v1 [cs.LG])
    The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated MultI-task Distillation for Multi-access Edge CompuTing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Experiments on three datasets show that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT.  ( 2 min )
    On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects. (arXiv:2301.00512v1 [cs.LG])
    Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, which break the Markov assumption of the RL framework. We focus on prolongedness and define PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process), a subclass of POMDPs in which the Markov assumption does not hold specifically due to prolonged effects of actions. Motivated by the pharmacology literature, we propose a simple and effective approach to converting drug dosing PAE-POMDPs into MDPs, enabling the use of the existing RL algorithms to solve such problems. We validate the proposed approach on a toy task, and a challenging glucose control task, for which we devise a clinically-inspired reward function. Our results demonstrate that: (1) the proposed method to restore the Markov assumption leads to significant improvements over a vanilla baseline; (2) the approach is competitive with recurrent policies which may inherently capture the prolonged effect of actions; (3) it is remarkably more time and memory efficient than the recurrent baseline and hence more suitable for real-time dosing control systems; and (4) it exhibits favorable qualitative behavior in our policy analysis.  ( 2 min )
    Neural Collapse in Deep Linear Network: From Balanced to Imbalanced Data. (arXiv:2301.00437v1 [cs.LG])
    Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.  ( 2 min )
    CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation. (arXiv:2301.00395v1 [cs.CL])
    As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.  ( 2 min )
    A Concept Knowledge Graph for User Next Intent Prediction at Alipay. (arXiv:2301.00503v1 [cs.CL])
    This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.  ( 2 min )
    Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation. (arXiv:2301.00427v1 [cs.LG])
    Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.  ( 2 min )
    MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs. (arXiv:2301.00407v1 [cs.LG])
    New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.  ( 2 min )
    Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction. (arXiv:2301.00448v1 [eess.AS])
    Classical methods for acoustic scene mapping require the estimation of time difference of arrival (TDOA) between microphones. Unfortunately, TDOA estimation is very sensitive to reverberation and additive noise. We introduce an unsupervised data-driven approach that exploits the natural structure of the data. Our method builds upon local conformal autoencoders (LOCA) - an offline deep learning scheme for learning standardized data coordinates from measurements. Our experimental setup includes a microphone array that measures the transmitted sound source at multiple locations across the acoustic enclosure. We demonstrate that LOCA learns a representation that is isometric to the spatial locations of the microphones. The performance of our method is evaluated using a series of realistic simulations and compared with other dimensionality-reduction schemes. We further assess the influence of reverberation on the results of LOCA and show that it demonstrates considerable robustness.  ( 2 min )
    Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin. (arXiv:2301.00363v1 [cs.CV])
    Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.  ( 2 min )
    Image To Tree with Recursive Prompting. (arXiv:2301.00447v1 [cs.CV])
    Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps of a recursive process. We design and train a two-stage model which leverages the UNet and Transformer architectures and introduces an image-based prompting technique. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.  ( 2 min )
    GANExplainer: GAN-based Graph Neural Networks Explainer. (arXiv:2301.00012v1 [cs.LG])
    With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making. Thus, it is critical to explain why graph neural network (GNN) makes particular predictions for them to be believed in many applications. Some GNNs explainers have been proposed recently. However, they lack to generate accurate and real explanations. To mitigate these limitations, we propose GANExplainer, based on Generative Adversarial Network (GAN) architecture. GANExplainer is composed of a generator to create explanations and a discriminator to assist with the Generator development. We investigate the explanation accuracy of our models by comparing the performance of GANExplainer with other state-of-the-art methods. Our empirical results on synthetic datasets indicate that GANExplainer improves explanation accuracy by up to 35\% compared to its alternatives.  ( 2 min )
    SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering. (arXiv:2301.00004v1 [q-bio.QM])
    Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (<50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.  ( 2 min )
    Discriminative Radial Domain Adaptation. (arXiv:2301.00383v1 [cs.LG])
    Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.  ( 2 min )
    PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs. (arXiv:2301.00391v1 [cs.LG])
    Dynamic Graph Neural Networks (DGNNs) have been broadly applied in various real-life applications, such as link prediction and pandemic forecast, to capture both static structural information and temporal characteristics from dynamic graphs. Combining both time-dependent and -independent components, DGNNs manifest substantial parallel computation and data reuse potentials, but suffer from severe memory access inefficiency and data transfer overhead under the canonical one-graph-at-a-time training pattern. To tackle the challenges, we propose PiPAD, a $\underline{\textbf{Pi}}pelined$ and $\underline{\textbf{PA}}rallel$ $\underline{\textbf{D}}GNN$ training framework for the end-to-end performance optimization on GPUs. From both the algorithm and runtime level, PiPAD holistically reconstructs the overall training paradigm from the data organization to computation manner. Capable of processing multiple graph snapshots in parallel, PiPAD eliminates the unnecessary data transmission and alleviates memory access inefficiency to improve the overall performance. Our evaluation across various datasets shows PiPAD achieves $1.22\times$-$9.57\times$ speedup over the state-of-the-art DGNN frameworks on three representative models.  ( 2 min )
    Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing. (arXiv:2301.00006v1 [cs.HC])
    Crowdsourcing has emerged as an effective platform to label a large volume of data in a cost- and time-efficient manner. Most previous works have focused on designing an efficient algorithm to recover only the ground-truth labels of the data. In this paper, we consider multi-choice crowdsourced labeling with the goal of recovering not only the ground truth but also the most confusing answer and the confusion probability. The most confusing answer provides useful information about the task by revealing the most plausible answer other than the ground truth and how plausible it is. To theoretically analyze such scenarios, we propose a model where there are top-two plausible answers for each task, distinguished from the rest of choices. Task difficulty is quantified by the confusion probability between the top two, and worker reliability is quantified by the probability of giving an answer among the top two. Under this model, we propose a two-stage inference algorithm to infer the top-two answers as well as the confusion probability. We show that our algorithm achieves the minimax optimal convergence rate. We conduct both synthetic and real-data experiments and demonstrate that our algorithm outperforms other recent algorithms. We also show the applicability of our algorithms in inferring the difficulty of tasks and training neural networks with the soft labels composed of the top-two most plausible classes.  ( 2 min )
    Goal-guided Transformer-enabled Reinforcement Learning for Efficient Autonomous Navigation. (arXiv:2301.00362v1 [cs.RO])
    Despite some successful applications of goal-driven navigation, existing deep reinforcement learning-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-art baselines. Demonstration videos are available at \colorb{https://youtu.be/93LGlGvaN0c.  ( 2 min )
    Correlation Clustering Algorithm for Dynamic Complete Signed Graphs: An Index-based Approach. (arXiv:2301.00384v1 [cs.DS])
    In this paper, we reduce the complexity of approximating the correlation clustering problem from $O(m\times\left( 2+ \alpha (G) \right)+n)$ to $O(m+n)$ for any given value of $\varepsilon$ for a complete signed graph with $n$ vertices and $m$ positive edges where $\alpha(G)$ is the arboricity of the graph. Our approach gives the same output as the original algorithm and makes it possible to implement the algorithm in a full dynamic setting where edge sign flipping and vertex addition/removal are allowed. Constructing this index costs $O(m)$ memory and $O(m\times\alpha(G))$ time. We also studied the structural properties of the non-agreement measure used in the approximation algorithm. The theoretical results are accompanied by a full set of experiments concerning seven real-world graphs. These results shows superiority of our index-based algorithm to the non-index one by a decrease of %34 in time on average.  ( 2 min )
    A Global Optimization Algorithm for K-Center Clustering of One Billion Samples. (arXiv:2301.00061v1 [math.OC])
    This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance. This algorithm is based on a reduced-space branch and bound scheme and guarantees convergence to the global optimum in a finite number of steps by only branching on the regions of centers. To improve efficiency, we have designed a two-stage decomposable lower bound, the solution of which can be derived in a closed form. In addition, we also propose several acceleration techniques to narrow down the region of centers, including bounds tightening, sample reduction, and parallelization. Extensive studies on synthetic and real-world datasets have demonstrated that our algorithm can solve the K-center problems to global optimal within 4 hours for ten million samples in the serial mode and one billion samples in the parallel mode. Moreover, compared with the state-of-the-art heuristic methods, the global optimum obtained by our algorithm can averagely reduce the objective function by 25.8% on all the synthetic and real-world datasets.  ( 2 min )
    Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation. (arXiv:2301.00351v1 [cs.LG])
    An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.  ( 2 min )
    Self-Supervised Object Segmentation with a Cut-and-Pasting GAN. (arXiv:2301.00366v1 [cs.CV])
    This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net based discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel as well as global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.  ( 2 min )
    Theoretical Characterization of How Neural Network Pruning Affects its Generalization. (arXiv:2301.00335v1 [cs.LG])
    It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance of the original dense models, but also sometimes even slightly boost the generalization performance. Theoretical understanding for such experimental observations are yet to be developed. This work makes the first attempt to study how different pruning fractions affect the model's gradient descent dynamics and generalization. Specifically, this work considers a classification task for overparameterized two-layer neural networks, where the network is randomly pruned according to different rates at the initialization. It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance. More surprisingly, the generalization bound gets better as the pruning fraction gets larger. To complement this positive result, this work further shows a negative result: there exists a large pruning fraction such that while gradient descent is still able to drive the training loss toward zero (by memorizing noise), the generalization performance is no better than random guessing. This further suggests that pruning can change the feature learning process, which leads to the performance drop of the pruned neural network. Up to our knowledge, this is the \textbf{first} generalization result for pruned neural networks, suggesting that pruning can improve the neural network's generalization.  ( 2 min )
    Exploring Singularities in point clouds with the graph Laplacian: An explicit approach. (arXiv:2301.00201v1 [stat.ML])
    We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of point clouds. Our theory provides theoretical guarantees and explicit bounds on the functional form of the graph Laplacian, in the case when it acts on functions defined close to singularities of the underlying manifold. We also propose methods that can be used to estimate these geometric properties of the point cloud, which are based on the theoretical guarantees.  ( 2 min )
    A Functional approach for Two Way Dimension Reduction in Time Series. (arXiv:2301.00357v1 [cs.LG])
    The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension reduction for time series through functional data analysis. Current methods for dimension reduction in functional data are functional principal component analysis and functional autoencoders, which are limited to linear mappings or scalar representations for the time series, which is inefficient. In real data applications, the nature of the data is much more complex. We propose a non-linear function-on-function approach, which consists of a functional encoder and a functional decoder, that uses continuous hidden layers consisting of continuous neurons to learn the structure inherent in functional data, which addresses the aforementioned concerns in the existing approaches. Our approach gives a low dimension latent representation by reducing the number of functional features as well as the timepoints at which the functions are observed. The effectiveness of the proposed model is demonstrated through multiple simulations and real data examples.  ( 2 min )
    Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. (arXiv:2301.00301v1 [cs.LG])
    The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i.e. those that add less noise when the input dataset is nice. We extend PTR to a more general setting by privately testing data-dependent privacy losses rather than local sensitivity, hence making it applicable beyond the standard noise-adding mechanisms, e.g. to queries with unbounded or undefined sensitivity. We demonstrate the versatility of generalized PTR using private linear regression as a case study. Additionally, we apply our algorithm to solve an open problem from ''Private Aggregation of Teacher Ensembles (PATE)'' -- privately releasing the entire model with a delicate data-dependent analysis.  ( 2 min )
    Sharper analysis of sparsely activated wide neural networks with trainable biases. (arXiv:2301.00327v1 [cs.LG])
    This work studies training one-hidden-layer overparameterized ReLU networks via gradient descent in the neural tangent kernel (NTK) regime, where, differently from the previous works, the networks' biases are trainable and are initialized to some constant rather than zero. The first set of results of this work characterize the convergence of the network's gradient descent dynamics. Surprisingly, it is shown that the network after sparsification can achieve as fast convergence as the original network. The contribution over previous work is that not only the bias is allowed to be updated by gradient descent under our setting but also a finer analysis is given such that the required width to ensure the network's closeness to its NTK is improved. Secondly, the networks' generalization bound after training is provided. A width-sparsity dependence is presented which yields sparsity-dependent localized Rademacher complexity and a generalization bound matching previous analysis (up to logarithmic factors). As a by-product, if the bias initialization is chosen to be zero, the width requirement improves the previous bound for the shallow networks' generalization. Lastly, since the generalization bound has dependence on the smallest eigenvalue of the limiting NTK and the bounds from previous works yield vacuous generalization, this work further studies the least eigenvalue of the limiting NTK. Surprisingly, while it is not shown that trainable biases are necessary, trainable bias helps to identify a nice data-dependent region where a much finer analysis of the NTK's smallest eigenvalue can be conducted, which leads to a much sharper lower bound than the previously known worst-case bound and, consequently, a non-vacuous generalization bound.  ( 2 min )
    Causal Deep Learning: Causal Capsules and Tensor Transformers. (arXiv:2301.00314v1 [cs.LG])
    We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer. The former estimate a set of latent variables that represent the causal factors, and the latter governs their interaction. Causal capsules and tensor transformers may be implemented using shallow autoencoders, but for a scalable architecture we employ block algebra and derive a deep neural network composed of a hierarchy of autoencoders. An interleaved kernel hierarchy preprocesses the data resulting in a hierarchy of kernel tensor factor models. Inverse causal questions are addressed with a neural network that implements multilinear projection and estimates the causes of effects. As an alternative to aggressive bottleneck dimension reduction or regularized regression that may camouflage an inherently underdetermined inverse problem, we prescribe modeling different aspects of the mechanism of data formation with piecewise tensor models whose multilinear projections are well-defined and produce multiple candidate solutions. Our forward and inverse neural network architectures are suitable for asynchronous parallel computation.  ( 2 min )
    Broad Learning System with Takagi-Sugeno Fuzzy Subsystem for Tobacco Origin Identification based on Near Infrared Spectroscopy. (arXiv:2301.00126v1 [cs.LG])
    Tobacco origin identification is significantly important in tobacco industry. Modeling analysis for sensor data with near infrared spectroscopy has become a popular method for rapid detection of internal features. However, for sensor data analysis using traditional artificial neural network or deep network models, the training process is extremely time-consuming. In this paper, a novel broad learning system with Takagi-Sugeno (TS) fuzzy subsystem is proposed for rapid identification of tobacco origin. Incremental learning is employed in the proposed method, which obtains the weight matrix of the network after a very small amount of computation, resulting in much shorter training time for the model, with only about 3 seconds for the extra step training. The experimental results show that the TS fuzzy subsystem can extract features from the near infrared data and effectively improve the recognition performance. The proposed method can achieve the highest prediction accuracy (95.59 %) in comparison to the traditional classification algorithms, artificial neural network, and deep convolutional neural network, and has a great advantage in the training time with only about 128 seconds.  ( 2 min )
    Internet of Things: Digital Footprints Carry A Device Identity. (arXiv:2301.00328v1 [cs.LG])
    The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet connectivity. To secure a network, device identification plays key role. In this paper, a device fingerprinting (DFP) model, which is able to distinguish between Internet of Things (IoT) and non-IoT devices, as well as uniquely identify individual devices, has been proposed. Four statistical features have been extracted from the consecutive five device-originated packets, to generate individual device fingerprints. The method has been evaluated using the Random Forest (RF) classifier and different datasets. Experimental results have shown that the proposed method achieves up to 99.8% accuracy in distinguishing between IoT and non-IoT devices and over 97.6% in classifying individual devices. These signify that the proposed method is useful in assisting operators in making their networks more secure and robust to security breaches and unauthorized access.  ( 2 min )
    Self-organization Preserved Graph Structure Learning with Principle of Relevant Information. (arXiv:2301.00015v1 [cs.LG])
    Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are always incomplete, noisy, or redundant. How to reveal the inherent graph structure in a unified way remains under-explored. We proposed PRI-GSL, a Graph Structure Learning framework guided by the Principle of Relevant Information, providing a simple and unified framework for identifying the self-organization and revealing the hidden structure. PRI-GSL learns a structure that contains the most relevant yet least redundant information quantified by von Neumann entropy and Quantum Jensen-Shannon divergence. PRI-GSL incorporates the evolution of quantum continuous walk with graph wavelets to encode node structural roles, showing in which way the nodes interplay and self-organize with the graph structure. Extensive experiments demonstrate the superior effectiveness and robustness of PRI-GSL.  ( 2 min )
    A Comparative Study of Image Disguising Methods for Confidential Outsourced Learning. (arXiv:2301.00252v1 [cs.CR])
    Large training data and expensive model tweaking are standard features of deep learning for images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which raises privacy concerns. Existing solutions are either too expensive to be practical or do not sufficiently protect the confidentiality of data and models. In this paper, we study and compare novel \emph{image disguising} mechanisms, DisguisedNets and InstaHide, aiming to achieve a better trade-off among the level of protection for outsourced DNN model training, the expenses, and the utility of data. DisguisedNets are novel combinations of image blocktization, block-level random permutation, and two block-level secure transformations: random multidimensional projection (RMT) and AES pixel-level encryption (AES). InstaHide is an image mixup and random pixel flipping technique \cite{huang20}. We have analyzed and evaluated them under a multi-level threat model. RMT provides a better security guarantee than InstaHide, under the Level-1 adversarial knowledge with well-preserved model quality. In contrast, AES provides a security guarantee under the Level-2 adversarial knowledge, but it may affect model quality more. The unique features of image disguising also help us to protect models from model-targeted attacks. We have done an extensive experimental evaluation to understand how these methods work in different settings for different datasets.  ( 2 min )
    MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. (arXiv:2301.00345v1 [cs.CV])
    There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .  ( 2 min )
    Self-Activating Neural Ensembles for Continual Reinforcement Learning. (arXiv:2301.00141v1 [cs.LG])
    The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent's next policy. During training, new modules are created as needed and only activated modules are updated to ensure that unused modules remain unchanged. This system enables our method to retain and leverage old skills, while growing and learning new ones. We demonstrate our approach on visually rich procedurally generated environments.  ( 2 min )
    Physics-informed Neural Networks approach to solve the Blasius function. (arXiv:2301.00106v1 [cs.LG])
    Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$  ( 2 min )
    Efficient On-device Training via Gradient Filtering. (arXiv:2301.00330v1 [cs.CV])
    Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device DNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple DNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19$\times$ speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy; over 20$\times$ speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training.  ( 2 min )
    Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment. (arXiv:2301.00134v1 [cs.LG])
    The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore, research on anomaly detection in the IoT environment has become popular and necessary in recent years. This survey provides an overview to understand the current progress of the different anomaly detection algorithms and how they can be applied in the context of the Internet of Things. In this survey, we categorize the widely used anomaly detection machine learning and deep learning techniques in IoT into three types: clustering-based, classification-based, and deep learning based. For each category, we introduce some state-of-the-art anomaly detection methods and evaluate the advantages and limitations of each technique.  ( 2 min )
    New Challenges in Reinforcement Learning: A Survey of Security and Privacy. (arXiv:2301.00188v1 [cs.LG])
    Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.  ( 2 min )
    Lightmorphic Signatures Analysis Toolkit. (arXiv:2301.00281v1 [cs.LG])
    In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit (LSAT). In addition to providing a core functionality, the software package enables specific optimizations with its modular and customizable design. To promote its usage and inspire future contributions, LSAT is publicly available. By using a self-supervised neural network and augmented machine learning algorithms, LSAT provides an easy-to-use interface with ample documentation. The experiments demonstrate that LSAT improves the otherwise tedious and error-prone tasks of translating lightmorphic associated data into usable spectrograms, enhanced with parameter tuning and performance analysis. With the provided mathematical functions, LSAT validates the nonlinearity encountered in the data conversion process while ensuring suitability of the forecasting algorithms.  ( 2 min )
    An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects. (arXiv:2301.00346v1 [cs.LG])
    We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.  ( 2 min )
    Mapping Knowledge Representations to Concepts: A Review and New Perspectives. (arXiv:2301.00189v1 [cs.AI])
    The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these representations, in order to explain the neural network's decisions, is an active and multifaceted research field. To gain a deeper understanding of a central aspect of this field, we have performed a targeted review focusing on research that aims to associate internal representations with human understandable concepts. In doing this, we added a perspective on the existing research by using primarily deductive nomological explanations as a proposed taxonomy. We find this taxonomy and theories of causality, useful for understanding what can be expected, and not expected, from neural network explanations. The analysis additionally uncovers an ambiguity in the reviewed literature related to the goal of model explainability; is it understanding the ML model or, is it actionable explanations useful in the deployment domain?  ( 2 min )
    Approaching Peak Ground Truth. (arXiv:2301.00243v1 [cs.LG])
    Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.  ( 2 min )
    Computational Charisma -- A Brick by Brick Blueprint for Building Charismatic Artificial Intelligence. (arXiv:2301.00142v1 [cs.HC])
    Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. A number of models exist that base charisma on various dimensions, often following the idea that charisma is given if someone could and would help others. Examples include influence (could help) and affability (would help) in scientific studies or power (could help), presence, and warmth (both would help) as a popular concept. Modelling high levels in these dimensions for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we, thereforem present a blueprint for building machines that can appear charismatic, but also analyse the charisma of others. To this end, we first provide the psychological perspective including different models of charisma and behavioural cues of it. We then switch to conversational charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behaviour by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.  ( 3 min )
    Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks. (arXiv:2301.00051v1 [cs.LG])
    Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suffered by the latter. However, AIL requires effective exploration during an online reinforcement learning phase. In this work, we show that the standard, naive approach to exploration can manifest as a suboptimal local maximum if a policy learned with AIL sufficiently matches the expert distribution without fully learning the desired task. This can be particularly catastrophic for manipulation tasks, where the difference between an expert and a non-expert state-action pair is often subtle. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of multiple exploratory, auxiliary tasks in addition to a main task. The addition of these auxiliary tasks forces the agent to explore states and actions that standard AIL may learn to ignore. Additionally, this particular formulation allows for the reusability of expert data between main tasks. Our experimental results in a challenging multitask robotic manipulation domain indicate that LfGP significantly outperforms both AIL and behaviour cloning, while also being more expert sample efficient than these baselines. To explain this performance gap, we provide further analysis of a toy problem that highlights the coupling between a local maximum and poor exploration, and also visualize the differences between the learned models from AIL and LfGP.  ( 2 min )
    Adapting Node-Place Model to Predict and Monitor COVID-19 Footprints and Transmission Risks. (arXiv:2301.00117v1 [physics.soc-ph])
    The node-place model has been widely used to classify and evaluate transit stations, which sheds light on individual travel behaviors and supports urban planning through effectively integrating land use and transportation development. This article adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a city. Similar studies on the model and its relevance to COVID-19, according to our knowledge, have not been undertaken before. Moreover, the unique metric drawn from detailed visit history of the infected, i.e., the COVID-19 footprints, is proposed and exploited. This study then empirically uses the adapted model to examine the station-level factors affecting the local COVID-19 footprints. The model accounts for traditional measures of the node and place as well as actual human mobility patterns associated with the node and place. It finds that stations with high node, place, and human mobility indices normally have more COVID-19 footprints in proximity. A multivariate regression is fitted to see whether and to what degree different indices and indicators can predict the COVID-19 footprints. The results indicate that many of the place, node, and human mobility indicators significantly impact the concentration of COVID-19 footprints. These are useful for policy-makers to predict and monitor hotspots for COVID-19 and other pandemics transmission.  ( 2 min )
    Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents. (arXiv:2301.00152v1 [cs.CL])
    Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity  ( 2 min )
    Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves. (arXiv:2301.00092v1 [stat.ML])
    General nonlinear sieve learnings are classes of nonlinear sieves that can approximate nonlinear functions of high dimensional variables much more flexibly than various linear sieves (or series). This paper considers general nonlinear sieve quasi-likelihood ratio (GN-QLR) based inference on expectation functionals of time series data, where the functionals of interest are based on some nonparametric function that satisfy conditional moment restrictions and are learned using multilayer neural networks. While the asymptotic normality of the estimated functionals depends on some unknown Riesz representer of the functional space, we show that the optimally weighted GN-QLR statistic is asymptotically Chi-square distributed, regardless whether the expectation functional is regular (root-$n$ estimable) or not. This holds when the data are weakly dependent beta-mixing condition. We apply our method to the off-policy evaluation in reinforcement learning, by formulating the Bellman equation into the conditional moment restriction framework, so that we can make inference about the state-specific value functional using the proposed GN-QLR method with time series data. In addition, estimating the averaged partial means and averaged partial derivatives of nonparametric instrumental variables and quantile IV models are also presented as leading examples. Finally, a Monte Carlo study shows the finite sample performance of the procedure  ( 2 min )
    Intrinsic Motivation in Dynamical Control Systems. (arXiv:2301.00005v1 [cs.LG])
    Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation. The computational principles underlying this behavior remain poorly understood. In this study, we investigate an information-theoretic approach to intrinsic motivation, based on maximizing an agent's empowerment (the mutual information between its past actions and future states). We show that this approach generalizes previous attempts to formalize intrinsic motivation, and we provide a computationally efficient algorithm for computing the necessary quantities. We test our approach on several benchmark control problems, and we explain its success in guiding intrinsically motivated behaviors by relating our information-theoretic control function to fundamental properties of the dynamical system representing the combined agent-environment system. This opens the door for designing practical artificial, intrinsically motivated controllers and for linking animal behaviors to their dynamical properties.  ( 2 min )
    Quantum Machine Learning Applied to the Classification of Diabetes. (arXiv:2301.00109v1 [cs.LG])
    Quantum Machine Learning (QML) shows how it maintains certain significant advantages over machine learning methods. It now shows that hybrid quantum methods have great scope for deployment and optimisation, and hold promise for future industries. As a weakness, quantum computing does not have enough qubits to justify its potential. This topic of study gives us encouraging results in the improvement of quantum coding, being the data preprocessing an important point in this research we employ two dimensionality reduction techniques LDA and PCA applying them in a hybrid way Quantum Support Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) in the classification of Diabetes.  ( 2 min )
    Bayesian Learning for Dynamic Inference. (arXiv:2301.00032v1 [cs.LG])
    The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential estimation problems however, the future values of the quantity to be estimated depend on the estimate of its current value. This type of estimation problems has been formulated as the dynamic inference problem. In this work, we formulate the Bayesian learning problem for dynamic inference, where the unknown quantity-generation model is assumed to be randomly drawn according to a random model parameter. We derive the optimal Bayesian learning rules, both offline and online, to minimize the inference loss. Moreover, learning for dynamic inference can serve as a meta problem, such that all familiar machine learning problems, including supervised learning, imitation learning and reinforcement learning, can be cast as its special cases or variants. Gaining a good understanding of this unifying meta problem thus sheds light on a broad spectrum of machine learning problems as well.  ( 2 min )
    On the Geometry of Reinforcement Learning in Continuous State and Action Spaces. (arXiv:2301.00009v1 [cs.LG])
    Advances in reinforcement learning have led to its successful application in complex tasks with continuous state and action spaces. Despite these advances in practice, most theoretical work pertains to finite state and action spaces. We propose building a theoretical understanding of continuous state and action spaces by employing a geometric lens. Central to our work is the idea that the transition dynamics induce a low dimensional manifold of reachable states embedded in the high-dimensional nominal state space. We prove that, under certain conditions, the dimensionality of this manifold is at most the dimensionality of the action space plus one. This is the first result of its kind, linking the geometry of the state space to the dimensionality of the action space. We empirically corroborate this upper bound for four MuJoCo environments. We further demonstrate the applicability of our result by learning a policy in this low dimensional representation. To do so we introduce an algorithm that learns a mapping to a low dimensional representation, as a narrow hidden layer of a deep neural network, in tandem with the policy using DDPG. Our experiments show that a policy learnt this way perform on par or better for four MuJoCo control suite tasks.  ( 2 min )
    On High dimensional Poisson models with measurement error: hypothesis testing for nonlinear nonconvex optimization. (arXiv:2301.00139v1 [math.ST])
    We study estimation and testing in the Poisson regression model with noisy high dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize. Treating the high dimensional issue further leads us to augment an amenable penalty term to the target function. We propose to estimate the regression parameter through minimizing the penalized target function. We derive the L1 and L2 convergence rates of the estimator and prove the variable selection consistency. We further establish the asymptotic normality of any subset of the parameters, where the subset can have infinitely many components as long as its cardinality grows sufficiently slow. We develop Wald and score tests based on the asymptotic normality of the estimator, which permits testing of linear functions of the members if the subset. We examine the finite sample performance of the proposed tests by extensive simulation. Finally, the proposed method is successfully applied to the Alzheimer's Disease Neuroimaging Initiative study, which motivated this work initially.  ( 2 min )
    Contextual Bandits and Optimistically Universal Learning. (arXiv:2301.00241v1 [stat.ML])
    We consider the contextual bandit problem on general action and context spaces, where the learner's rewards depend on their selected actions and an observable context. This generalizes the standard multi-armed bandit to the case where side information is available, e.g., patients' records or customers' history, which allows for personalized treatment. We focus on consistency -- vanishing regret compared to the optimal policy -- and show that for large classes of non-i.i.d. contexts, consistency can be achieved regardless of the time-invariant reward mechanism, a property known as universal consistency. Precisely, we first give necessary and sufficient conditions on the context-generating process for universal consistency to be possible. Second, we show that there always exists an algorithm that guarantees universal consistency whenever this is achievable, called an optimistically universal learning rule. Interestingly, for finite action spaces, learnable processes for universal learning are exactly the same as in the full-feedback setting of supervised learning, previously studied in the literature. In other words, learning can be performed with partial feedback without any generalization cost. The algorithms balance a trade-off between generalization (similar to structural risk minimization) and personalization (tailoring actions to specific contexts). Lastly, we consider the case of added continuity assumptions on rewards and show that these lead to universal consistency for significantly larger classes of data-generating processes.  ( 2 min )
    Source-Free Unsupervised Domain Adaptation: A Survey. (arXiv:2301.00265v1 [cs.CV])
    Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to unlabeled target domain with source data inaccessible. A comprehensive review of these works on SFUDA is of great significance. In this paper, we provide a timely and systematic literature review of existing SFUDA approaches from a technical perspective. Specifically, we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and black-box SFUDA, and further divide them into finer subcategories based on different learning strategies they use. We also investigate the challenges of methods in each subcategory, discuss the advantages/disadvantages of white-box and black-box SFUDA methods, conclude the commonly used benchmark datasets, and summarize the popular techniques for improved generalizability of models learned without using source data. We finally discuss several promising future directions in this field.  ( 2 min )
    Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce. (arXiv:2301.00036v1 [cs.LG])
    This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. We train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. With the modified CGAN framework, various forms of semantic insights gathered from the query document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. Our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models  ( 2 min )
    Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters. (arXiv:2301.00014v1 [cs.LG])
    An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecasting, historical data have been used to train a model and to predict the behavior of a sensor and, thus, to detect anomalies.  ( 2 min )
    Hair and Scalp Disease Detection using Machine Learning and Image Processing. (arXiv:2301.00122v1 [cs.CV])
    Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers.  ( 2 min )
    Effects of Data Geometry in Early Deep Learning. (arXiv:2301.00008v1 [cs.LG])
    Deep neural networks can approximate functions on different types of data, from images to graphs, with varied underlying structure. This underlying structure can be viewed as the geometry of the data manifold. By extending recent advances in the theoretical understanding of neural networks, we study how a randomly initialized neural network with piece-wise linear activation splits the data manifold into regions where the neural network behaves as a linear function. We derive bounds on the density of boundary of linear regions and the distance to these boundaries on the data manifold. This leads to insights into the expressivity of randomly initialized deep neural networks on non-Euclidean data sets. We empirically corroborate our theoretical results using a toy supervised learning problem. Our experiments demonstrate that number of linear regions varies across manifolds and the results hold with changing neural network architectures. We further demonstrate how the complexity of linear regions is different on the low dimensional manifold of images as compared to the Euclidean space, using the MetFaces dataset.  ( 2 min )
    Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning. (arXiv:2301.00130v1 [eess.SY])
    Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Sampling rate adaption which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this paper, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability.  ( 2 min )
    eVAE: Evolutionary Variational Autoencoder. (arXiv:2301.00011v1 [cs.NE])
    The surrogate loss of variational autoencoders (VAEs) poses various challenges to their training, inducing the imbalance between task fitting and representation inference. To avert this, the existing strategies for VAEs focus on adjusting the tradeoff by introducing hyperparameters, deriving a tighter bound under some mild assumptions, or decomposing the loss components per certain neural settings. VAEs still suffer from uncertain tradeoff learning.We propose a novel evolutionary variational autoencoder (eVAE) building on the variational information bottleneck (VIB) theory and integrative evolutionary neural learning. eVAE integrates a variational genetic algorithm into VAE with variational evolutionary operators including variational mutation, crossover, and evolution. Its inner-outer-joint training mechanism synergistically and dynamically generates and updates the uncertain tradeoff learning in the evidence lower bound (ELBO) without additional constraints. Apart from learning a lossy compression and representation of data under the VIB assumption, eVAE presents an evolutionary paradigm to tune critical factors of VAEs and deep neural networks and addresses the premature convergence and random search problem by integrating evolutionary optimization into deep learning. Experiments show that eVAE addresses the KL-vanishing problem for text generation with low reconstruction loss, generates all disentangled factors with sharp images, and improves the image generation quality,respectively. eVAE achieves better reconstruction loss, disentanglement, and generation-inference balance than its competitors.  ( 2 min )
    Behave-XAI: Deep Explainable Learning of Behavioral Representational Data. (arXiv:2301.00016v1 [cs.LG])
    According to the latest trend of artificial intelligence, AI-systems needs to clarify regarding general,specific decisions,services provided by it. Only consumer is satisfied, with explanation , for example, why any classification result is the outcome of any given time. This actually motivates us using explainable or human understandable AI for a behavioral mining scenario, where users engagement on digital platform is determined from context, such as emotion, activity, weather, etc. However, the output of AI-system is not always systematically correct, and often systematically correct, but apparently not-perfect and thereby creating confusions, such as, why the decision is given? What is the reason underneath? In this context, we first formulate the behavioral mining problem in deep convolutional neural network architecture. Eventually, we apply a recursive neural network due to the presence of time-series data from users physiological and environmental sensor-readings. Once the model is developed, explanations are presented with the advent of XAI models in front of users. This critical step involves extensive trial with users preference on explanations over conventional AI, judgement of credibility of explanation.  ( 2 min )
    Selected aspects of complex, hypercomplex and fuzzy neural networks. (arXiv:2301.00007v1 [cs.LG])
    This short report reviews the current state of the research and methodology on theoretical and practical aspects of Artificial Neural Networks (ANN). It was prepared to gather state-of-the-art knowledge needed to construct complex, hypercomplex and fuzzy neural networks. The report reflects the individual interests of the authors and, by now means, cannot be treated as a comprehensive review of the ANN discipline. Considering the fast development of this field, it is currently impossible to do a detailed review of a considerable number of pages. The report is an outcome of the Project 'The Strategic Research Partnership for the mathematical aspects of complex, hypercomplex and fuzzy neural networks' meeting at the University of Warmia and Mazury in Olsztyn, Poland, organized in September 2022.  ( 2 min )
  • Open

    Online Linearized LASSO. (arXiv:2211.06039v2 [stat.ML] UPDATED)
    Sparse regression has been a popular approach to perform variable selection and enhance the prediction accuracy and interpretability of the resulting statistical model. Existing approaches focus on offline regularized regression, while the online scenario has rarely been studied. In this paper, we propose a novel online sparse linear regression framework for analyzing streaming data when data points arrive sequentially. Our proposed method is memory efficient and requires less stringent restricted strong convexity assumptions. Theoretically, we show that with a properly chosen regularization parameter, the $\ell_2$-norm statistical error of our estimator diminishes to zero in the optimal order of $\tilde{O}({\sqrt{s/t}})$, where $s$ is the sparsity level, $t$ is the streaming sample size, and $\tilde{O}(\cdot)$ hides logarithmic terms. Numerical experiments demonstrate the practical efficiency of our algorithm.  ( 2 min )
    Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. (arXiv:2204.13545v2 [cs.LG] UPDATED)
    Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA HTS is required to enrich single-cell data meaningfully. We introduce chemCPA, a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with an architecture surgery for transfer learning and demonstrate how training on existing bulk RNA HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating drug discovery.  ( 2 min )
    Causal Inference (C-inf) -- closed form worst case typical phase transitions. (arXiv:2301.00793v1 [stat.ML])
    In this paper we establish a mathematically rigorous connection between Causal inference (C-inf) and the low-rank recovery (LRR). Using Random Duality Theory (RDT) concepts developed in [46,48,50] and novel mathematical strategies related to free probability theory, we obtain the exact explicit typical (and achievable) worst case phase transitions (PT). These PT precisely separate scenarios where causal inference via LRR is possible from those where it is not. We supplement our mathematical analysis with numerical experiments that confirm the theoretical predictions of PT phenomena, and further show that the two closely match for fairly small sample sizes. We obtain simple closed form representations for the resulting PTs, which highlight direct relations between the low rankness of the target C-inf matrix and the time of the treatment. Hence, our results can be used to determine the range of C-inf's typical applicability.  ( 2 min )
    Dimensionless machine learning: Imposing exact units equivariance. (arXiv:2204.00887v2 [stat.ML] UPDATED)
    Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings. Here, we express this symmetry in terms of a (non-compact) group action, and we employ dimensional analysis and ideas from equivariant machine learning to provide a methodology for exactly units-equivariant machine learning: For any given learning task, we first construct a dimensionless version of its inputs using classic results from dimensional analysis, and then perform inference in the dimensionless space. Our approach can be used to impose units equivariance across a broad range of machine learning methods which are equivariant to rotations and other groups. We discuss the in-sample and out-of-sample prediction accuracy gains one can obtain in contexts like symbolic regression and emulation, where symmetry is important. We illustrate our approach with simple numerical examples involving dynamical systems in physics and ecology.  ( 2 min )
    Posterior Collapse and Latent Variable Non-identifiability. (arXiv:2301.00537v1 [stat.ML])
    Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.  ( 2 min )
    Preface: Characterisation of Physical Processes from Anomalous Diffusion Data. (arXiv:2301.00800v1 [cond-mat.stat-mech])
    Preface to the special issue "Characterisation of Physical Processes from Anomalous Diffusion Data" associated with the Anomalous Diffusion Challenge ( https://andi-challenge.org ) and published in Journal of Physics A: Mathematical and Theoretical. The list of articles included in the special issue can be accessed at https://iopscience.iop.org/journal/1751-8121/page/Characterisation-of-Physical-Processes-from-Anomalous-Diffusion-Data .  ( 2 min )
    Optimality Guarantees for Particle Belief Approximation of POMDPs. (arXiv:2210.05015v2 [cs.AI] UPDATED)
    Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Our main contribution is bounding the error between any POMDP and its corresponding finite sample particle belief MDP (PB-MDP) approximation. This fundamental bridge between PB-MDPs and POMDPs allows us to adapt any sampling-based MDP algorithm to a POMDP by solving the corresponding particle belief MDP, thereby extending the convergence guarantees of the MDP algorithm to the POMDP. Practically, this is implemented by using the particle filter belief transition model as the generative model for the MDP solver. While this requires access to the observation density model from the POMDP, it only increases the transition sampling complexity of the MDP solver by a factor of $\mathcal{O}(C)$, where $C$ is the number of particles. Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces. In addition to our theoretical contribution, we perform five numerical experiments on benchmark POMDPs to demonstrate that a simple MDP algorithm adapted using PB-MDP approximation, Sparse-PFT, achieves performance competitive with other leading continuous observation POMDP solvers.  ( 2 min )
    Optimal Experimental Design for Staggered Rollouts. (arXiv:1911.03764v4 [econ.EM] UPDATED)
    In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. The design problem involves selecting an initial treatment time for each unit in order to most precisely estimate both the instantaneous and cumulative effects of the treatment. We first consider non-adaptive experiments, where all treatment assignment decisions are made prior to the start of the experiment. For this case, we show that the optimization problem is generally NP-hard and we propose a near-optimal solution. Under this solution the fraction entering treatment each period is initially low, then high, and finally low again. Next, we study an adaptive experimental design problem, where both the decision to continue the experiment and treatment assignment decisions are updated after each period's data is collected. For the adaptive case we propose a new algorithm, the Precision-Guided Adaptive Experiment (PGAE) algorithm, that addresses the challenges at both the design stage and at the stage of estimating treatment effects, ensuring valid post-experiment inference accounting for the adaptive nature of the design. Using realistic settings, we demonstrate that our proposed solutions can reduce the opportunity cost of the experiments by over 50\%, compared to static design benchmarks.  ( 2 min )
    Projection Robust Wasserstein Distance and Riemannian Optimization. (arXiv:2006.07458v10 [cs.LG] UPDATED)
    Projection robust Wasserstein (PRW) distance, or Wasserstein projection pursuit (WPP), is a robust variant of the Wasserstein distance. Recent work suggests that this quantity is more robust than the standard Wasserstein distance, in particular when comparing probability measures in high-dimensions. However, it is ruled out for practical application because the optimization model is essentially non-convex and non-smooth which makes the computation intractable. Our contribution in this paper is to revisit the original motivation behind WPP/PRW, but take the hard route of showing that, despite its non-convexity and lack of nonsmoothness, and even despite some hardness results proved by~\citet{Niles-2019-Estimation} in a minimax sense, the original formulation for PRW/WPP \textit{can} be efficiently computed in practice using Riemannian optimization, yielding in relevant cases better behavior than its convex relaxation. More specifically, we provide three simple algorithms with solid theoretical guarantee on their complexity bound (one in the appendix), and demonstrate their effectiveness and efficiency by conducing extensive experiments on synthetic and real data. This paper provides a first step into a computational theory of the PRW distance and provides the links between optimal transport and Riemannian optimization.  ( 3 min )
    Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. (arXiv:2301.00629v1 [cs.AI])
    Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they are only capable of formally encoding symmetric conditional independence, which in practice is often too strict to hold. Asymmetry-labeled DAGs have been recently proposed to both extend the class of Bayesian networks by relaxing the symmetric assumption of independence and denote the type of dependence existing between the variables of interest. Here, we introduce novel structural learning algorithms for this class of models which, whilst being efficient, allow for a straightforward interpretation of the underlying dependence structure. A comprehensive computational study highlights the efficiency of the algorithms. A real-world data application using data from the Fear of COVID-19 Scale collected in Italy showcases their use in practice.  ( 2 min )
    Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks. (arXiv:2202.01940v2 [stat.ML] UPDATED)
    We propose Distribution Embedding Networks (DEN) for classification with small data. In the same spirit of meta-learning, DEN learns from a diverse set of training tasks with the goal to generalize to unseen target tasks. Unlike existing approaches which require the inputs of training and target tasks to have the same dimension with possibly similar distributions, DEN allows training and target tasks to live in heterogeneous input spaces. This is especially useful for tabular-data tasks where labeled data from related tasks are scarce. DEN uses a three-block architecture: a covariate transformation block followed by a distribution embedding block and then a classification block. We provide theoretical insights to show that this architecture allows the embedding and classification blocks to be fixed after pre-training on a diverse set of tasks; only the covariate transformation block with relatively few parameters needs to be fine-tuned for each new task. To facilitate training, we also propose an approach to synthesize binary classification tasks, and demonstrate that DEN outperforms existing methods in a number of synthetic and real tasks in numerical studies.  ( 2 min )
    Graph Construction from Data using Non Negative Kernel regression (NNK Graphs). (arXiv:1910.09383v2 [cs.LG] UPDATED)
    Data-driven neighborhood definitions and graph constructions are often used in machine learning and signal processing applications. k-nearest neighbor~(kNN) and $\epsilon$-neighborhood methods are among the most common methods used for neighborhood selection, due to their computational simplicity. However, the choice of parameters associated with these methods, such as k and $\epsilon$, is still ad hoc. We make two main contributions in this paper. First, we present an alternative view of neighborhood selection, where we show that neighborhood construction is equivalent to a sparse signal approximation problem. Second, we propose an algorithm, non-negative kernel regression~(NNK), for obtaining neighborhoods that lead to better sparse representation. NNK draws similarities to the orthogonal matching pursuit approach to signal representation and possesses desirable geometric and theoretical properties. Experiments demonstrate (i) the robustness of the NNK algorithm for neighborhood and graph construction, (ii) its ability to adapt the number of neighbors to the data properties, and (iii) its superior performance in local neighborhood and graph-based machine learning tasks.  ( 2 min )
    ReSQueing Parallel and Private Stochastic Convex Optimization. (arXiv:2301.00457v1 [math.OC])
    We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.  ( 2 min )
    Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems. (arXiv:2109.02384v3 [math.OC] UPDATED)
    In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able to be predicted from the measured system outputs. Similar derivations have been done before but not using state-space representation.  ( 2 min )
    A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization. (arXiv:2301.00477v1 [math.OC])
    A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint function values and derivatives are available, but only stochastic approximations of the objective function and its associated derivatives can be computed via inexact probabilistic zeroth- and first-order oracles. Under reasonable assumptions, a high-probability bound on the iteration complexity of the algorithm to approximate first-order stationarity is derived. Numerical results on standard nonlinear optimization test problems illustrate the advantages and limitations of our proposed method.  ( 2 min )
    Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces. (arXiv:2207.00879v3 [stat.ML] UPDATED)
    Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e.g., hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.  ( 2 min )
    Sinkhorn Distributionally Robust Optimization. (arXiv:2109.11926v2 [math.OC] UPDATED)
    We study distributionally robust optimization (DRO) with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization. We provide convex programming dual reformulation for a general nominal distribution. Compared with Wasserstein DRO, it is computationally tractable for a larger class of loss functions, and its worst-case distribution is more reasonable. We propose an efficient first-order algorithm with bisection search to solve the dual reformulation. We demonstrate that our proposed algorithm finds $\delta$-optimal solution of the new DRO formulation with computation cost $\tilde{O}(\delta^{-3})$ and memory cost $\tilde{O}(\delta^{-2})$, and the computation cost further improves to $\tilde{O}(\delta^{-2})$ when the loss function is smooth. Finally, we provide various numerical examples using both synthetic and real data to demonstrate its competitive performance and light computational speed.  ( 2 min )
    Mixed moving average field guided learning for spatio-temporal data. (arXiv:2301.00736v1 [stat.ML])
    Influenced mixed moving average fields are a versatile modeling class for spatio-temporal data. However, their predictive distribution is not generally accessible. Under this modeling assumption, we define a novel theory-guided machine learning approach that employs a generalized Bayesian algorithm to make predictions. We employ a Lipschitz predictor, for example, a linear model or a feed-forward neural network, and determine a randomized estimator by minimizing a novel PAC Bayesian bound for data serially correlated along a spatial and temporal dimension. Performing causal future predictions is a highlight of our methodology as its potential application to data with short and long-range dependence. We conclude by showing the performance of the learning methodology in an example with linear predictors and simulated spatio-temporal data from an STOU process.  ( 2 min )
    The Fragility of Optimized Bandit Algorithms. (arXiv:2109.13595v5 [cs.LG] UPDATED)
    Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that designs that are optimal over certain exponential families can achieve expected regret that grows logarithmically in the number of arm plays, at a rate governed by the Lai-Robbins lower bound. In this paper, we show that when one uses such optimized designs, the regret distribution of the associated algorithms necessarily has a very heavy tail, specifically, that of a truncated Cauchy distribution. Furthermore, for $p>1$, the $p$'th moment of the regret distribution grows much faster than poly-logarithmically, in particular as a power of the total number of arm plays. We show that optimized UCB bandit designs are also fragile in an additional sense, namely when the problem is even slightly mis-specified, the regret can grow much faster than the conventional theory suggests. Our arguments are based on standard change-of-measure ideas, and indicate that the most likely way that regret becomes larger than expected is when the optimal arm returns below-average rewards in the first few arm plays, thereby causing the algorithm to believe that the arm is sub-optimal. To alleviate the fragility issues exposed, we show that UCB algorithms can be modified so as to ensure a desired degree of robustness to mis-specification. In doing so, we also provide a sharp trade-off between the amount of UCB exploration and the tail exponent of the resulting regret distribution.  ( 2 min )
    Lossy Compression with Gaussian Diffusion. (arXiv:2206.08889v2 [stat.ML] UPDATED)
    We consider a novel lossy compression approach based on unconditional diffusion generative models, which we call DiffC. Unlike modern compression schemes which rely on transform coding and quantization to restrict the transmitted information, DiffC relies on the efficient communication of pixels corrupted by Gaussian noise. We implement a proof of concept and find that it works surprisingly well despite the lack of an encoder transform, outperforming the state-of-the-art generative compression method HiFiC on ImageNet 64x64. DiffC only uses a single model to encode and denoise corrupted pixels at arbitrary bitrates. The approach further provides support for progressive coding, that is, decoding from partial bit streams. We perform a rate-distortion analysis to gain a deeper understanding of its performance, providing analytical results for multivariate Gaussian data as well as theoretic bounds for general distributions. Furthermore, we prove that a flow-based reconstruction achieves a 3 dB gain over ancestral sampling at high bitrates.  ( 2 min )
    Asymptotics of Discrete Schr\"odinger Bridges via Chaos Decomposition. (arXiv:2011.08963v2 [math.PR] UPDATED)
    Consider the problem of matching two independent i.i.d. samples of size $N$ from two distributions $P$ and $Q$ in $\mathbb{R}^d$. For an arbitrary continuous cost function, the optimal assignment problem looks for the matching that minimizes the total cost. We consider instead in this paper the problem where each matching is endowed with a Gibbs probability weight proportional to the exponential of the negative total cost of that matching. Viewing each matching as a joint distribution with $N$ atoms, we then take a convex combination with respect to the above Gibbs probability measure. We show that this resulting random joint distribution converges, as $N\rightarrow \infty$, to the solution of a variational problem, introduced by F\"ollmer, called the Schr\"odinger problem. We also derive the first two error terms of orders $N^{-1/2}$ and $N^{-1}$, respectively. This gives us central limit theorems for integrated test functions, including for the cost of transport, and second order Gaussian chaos limits when the limiting Gaussian variance is zero. The proofs are based on a novel chaos decomposition of the discrete Schr\"odinger bridge by polynomial functions of the pair of empirical distributions as the first and second order Taylor approximations in the space of measures. This is achieved by extending the Hoeffding decomposition from the classical theory of U-statistics.  ( 2 min )
    InfoFair: Information-Theoretic Intersectional Fairness. (arXiv:2105.11069v2 [cs.LG] UPDATED)
    Algorithmic fairness is becoming increasingly important in data mining and machine learning. Among others, a foundational notation is group fairness. The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e.g., gender, race, marital status, etc.) in the real-world is commonplace. As such, methods that can ensure a fair learning outcome with respect to all sensitive attributes of concern simultaneously need to be developed. In this paper, we study the problem of information-theoretic intersectional fairness (InfoFair), where statistical parity, a representative group fairness measure, is guaranteed among demographic groups formed by multiple sensitive attributes of interest. We formulate it as a mutual information minimization problem and propose a generic end-to-end algorithmic framework to solve it. The key idea is to leverage a variational representation of mutual information, which considers the variational distribution between learning outcomes and sensitive attributes, as well as the density ratio between the variational and the original distributions. Our proposed framework is generalizable to many different settings, including other statistical notions of fairness, and could handle any type of learning task equipped with a gradient-based optimizer. Empirical evaluations in the fair classification task on three real-world datasets demonstrate that our proposed framework can effectively debias the classification results with minimal impact to the classification accuracy.  ( 2 min )
    Neural Collapse in Deep Linear Network: From Balanced to Imbalanced Data. (arXiv:2301.00437v1 [cs.LG])
    Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.  ( 2 min )
    Causal Inference (C-inf) -- asymmetric scenario of typical phase transitions. (arXiv:2301.00801v1 [stat.ML])
    In this paper, we revisit and further explore a mathematically rigorous connection between Causal inference (C-inf) and the Low-rank recovery (LRR) established in [10]. Leveraging the Random duality - Free probability theory (RDT-FPT) connection, we obtain the exact explicit typical C-inf asymmetric phase transitions (PT). We uncover a doubling low-rankness phenomenon, which means that exactly two times larger low rankness is allowed in asymmetric scenarios compared to the symmetric worst case ones considered in [10]. Consequently, the final PT mathematical expressions are as elegant as those obtained in [10], and highlight direct relations between the targeted C-inf matrix low rankness and the time of treatment. Our results have strong implications for applications, where C-inf matrices are not necessarily symmetric.  ( 2 min )
    Confidence Sets under Generalized Self-Concordance. (arXiv:2301.00260v1 [math.ST])
    This paper revisits a fundamental problem in statistical inference from a non-asymptotic theoretical viewpoint $\unicode{x2013}$ the construction of confidence sets. We establish a finite-sample bound for the estimator, characterizing its asymptotic behavior in a non-asymptotic fashion. An important feature of our bound is that its dimension dependency is captured by the effective dimension $\unicode{x2013}$ the trace of the limiting sandwich covariance $\unicode{x2013}$ which can be much smaller than the parameter dimension in some regimes. We then illustrate how the bound can be used to obtain a confidence set whose shape is adapted to the optimization landscape induced by the loss function. Unlike previous works that rely heavily on the strong convexity of the loss function, we only assume the Hessian is lower bounded at optimum and allow it to gradually becomes degenerate. This property is formalized by the notion of generalized self-concordance which originated from convex optimization. Moreover, we demonstrate how the effective dimension can be estimated from data and characterize its estimation accuracy. We apply our results to maximum likelihood estimation with generalized linear models, score matching with exponential families, and hypothesis testing with Rao's score test.  ( 2 min )
    Learning to Maximize Mutual Information for Dynamic Feature Selection. (arXiv:2301.00557v1 [cs.LG])
    Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.  ( 2 min )
    The Design Principle of Blockchain: An Initiative for the SoK of SoKs. (arXiv:2301.00479v1 [cs.CR])
    Blockchain, also coined as decentralized AI, has the potential to empower AI to be more trustworthy by creating a decentralized trust of privacy, security, and audibility. However, systematic studies on the design principle of Blockchain as a trust engine for an integrated society of Cyber-Physical-Socia-System (CPSS) are still absent. In this article, we provide an initiative for seeking the design principle of Blockchain for a better digital world. Using a hybrid method of qualitative and quantitative studies, we examine the past origin, the current development, and the future directions of Blockchain design principles. We have three findings. First, the answers to whether Blockchain lives up to its original design principle as a distributed database are controversial. Second, the current development of Blockchain community reveals a taxonomy of 7 categories, including privacy and security, scalability, decentralization, applicability, governance and regulation, system design, and cross-chain interoperability. Both research and practice are more centered around the first category of privacy and security and the fourth category of applicability. Future scholars, practitioners, and policy-makers have vast opportunities in other, much less exploited facets and the synthesis at the interface of multiple aspects. Finally, in counter-examples, we conclude that a synthetic solution that crosses discipline boundaries is necessary to close the gaps between the current design of Blockchain and the design principle of a trust engine for a truly intelligent world.  ( 2 min )
    An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects. (arXiv:2301.00346v1 [cs.LG])
    We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.  ( 2 min )
    Contextual Bandits and Optimistically Universal Learning. (arXiv:2301.00241v1 [stat.ML])
    We consider the contextual bandit problem on general action and context spaces, where the learner's rewards depend on their selected actions and an observable context. This generalizes the standard multi-armed bandit to the case where side information is available, e.g., patients' records or customers' history, which allows for personalized treatment. We focus on consistency -- vanishing regret compared to the optimal policy -- and show that for large classes of non-i.i.d. contexts, consistency can be achieved regardless of the time-invariant reward mechanism, a property known as universal consistency. Precisely, we first give necessary and sufficient conditions on the context-generating process for universal consistency to be possible. Second, we show that there always exists an algorithm that guarantees universal consistency whenever this is achievable, called an optimistically universal learning rule. Interestingly, for finite action spaces, learnable processes for universal learning are exactly the same as in the full-feedback setting of supervised learning, previously studied in the literature. In other words, learning can be performed with partial feedback without any generalization cost. The algorithms balance a trade-off between generalization (similar to structural risk minimization) and personalization (tailoring actions to specific contexts). Lastly, we consider the case of added continuity assumptions on rewards and show that these lead to universal consistency for significantly larger classes of data-generating processes.  ( 2 min )
    Exploring Singularities in point clouds with the graph Laplacian: An explicit approach. (arXiv:2301.00201v1 [stat.ML])
    We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of point clouds. Our theory provides theoretical guarantees and explicit bounds on the functional form of the graph Laplacian, in the case when it acts on functions defined close to singularities of the underlying manifold. We also propose methods that can be used to estimate these geometric properties of the point cloud, which are based on the theoretical guarantees.  ( 2 min )
    Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing. (arXiv:2301.00006v1 [cs.HC])
    Crowdsourcing has emerged as an effective platform to label a large volume of data in a cost- and time-efficient manner. Most previous works have focused on designing an efficient algorithm to recover only the ground-truth labels of the data. In this paper, we consider multi-choice crowdsourced labeling with the goal of recovering not only the ground truth but also the most confusing answer and the confusion probability. The most confusing answer provides useful information about the task by revealing the most plausible answer other than the ground truth and how plausible it is. To theoretically analyze such scenarios, we propose a model where there are top-two plausible answers for each task, distinguished from the rest of choices. Task difficulty is quantified by the confusion probability between the top two, and worker reliability is quantified by the probability of giving an answer among the top two. Under this model, we propose a two-stage inference algorithm to infer the top-two answers as well as the confusion probability. We show that our algorithm achieves the minimax optimal convergence rate. We conduct both synthetic and real-data experiments and demonstrate that our algorithm outperforms other recent algorithms. We also show the applicability of our algorithms in inferring the difficulty of tasks and training neural networks with the soft labels composed of the top-two most plausible classes.  ( 2 min )
    Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves. (arXiv:2301.00092v1 [stat.ML])
    General nonlinear sieve learnings are classes of nonlinear sieves that can approximate nonlinear functions of high dimensional variables much more flexibly than various linear sieves (or series). This paper considers general nonlinear sieve quasi-likelihood ratio (GN-QLR) based inference on expectation functionals of time series data, where the functionals of interest are based on some nonparametric function that satisfy conditional moment restrictions and are learned using multilayer neural networks. While the asymptotic normality of the estimated functionals depends on some unknown Riesz representer of the functional space, we show that the optimally weighted GN-QLR statistic is asymptotically Chi-square distributed, regardless whether the expectation functional is regular (root-$n$ estimable) or not. This holds when the data are weakly dependent beta-mixing condition. We apply our method to the off-policy evaluation in reinforcement learning, by formulating the Bellman equation into the conditional moment restriction framework, so that we can make inference about the state-specific value functional using the proposed GN-QLR method with time series data. In addition, estimating the averaged partial means and averaged partial derivatives of nonparametric instrumental variables and quantile IV models are also presented as leading examples. Finally, a Monte Carlo study shows the finite sample performance of the procedure  ( 2 min )

  • Open

    [R] AMD Instinct MI25 | Machine Learning Setup on the Cheap!
    Greetings! ​ In my adventures of Pytorch, and supporting ML workloads in my day to day job, I wanted to continue homelabbing and buildout a compute node to run ML benchmarks and jobs on. ​ This brought me to the AMD MI25, and for $100 USD it was surprising what amount of horsepower, and vRAM you could get for the price. Hopefully my write up will help someone in the machine learning community. ​ Let me know if you have any questions or need any help with a GPU compute setup. I'd be happy to assist! ​ https://www.zb-c.tech/2022/11/20/amd-instinct-mi25-machine-learning-setup-on-the-cheap/ submitted by /u/zveroboy152 [link] [comments]  ( 60 min )
    [D] Transformer effectiveness for time series forecasting (doubts)
    I recently came across this paper Are Transformers Effective for Time Series Forecasting? and it seems to cast doubt on the recent trend of using transformers for time series forecasting, suggesting a simple model can out perform complex transformers. Personally, in many of my experiments using transformers on temporal data besides the quite commonly tested benchmarks (ETH, exchange, etc) they perform poorly compared to other simple(r) models like GRUs or DA-RNN. Yet we are still seeing an explosion of papers about them in the research community. Are there other recent deep learning based alternatives? submitted by /u/AttentionImaginary54 [link] [comments]  ( 64 min )
    [P] Generate 3D point cloud from images using Point-E
    Point-E leverages diffusion models to generate synthetic views and 3D point clouds. Using text input, it generates an image, which is then used as a reference for generating the 3D point cloud. (Learn more about Point-E) We were so excited about the results (and overall coolness 😎) of Point-E, that we decided to share the fun with EVERYONE! https://reddit.com/link/102l6ne/video/tu4h2bksjw9a1/player My team built and deployed a Streamlit app to generate a 3D point cloud model from images using Point-E 🤖 . This process takes only 1-2 minutes on a single GPU, making it much faster than previous state-of-the-art methods. Check it out: https://point-e.public.dagshubusercontent.com/ submitted by /u/RepresentativeCod613 [link] [comments]  ( 62 min )
    [N] MAgent2 - a reinforcement learning environment engine that can allows for efficient multi-agent games with hundreds or thousands of agents- is now mature within the Farama Foundation
    MAgent2 is the maintained fork of the environments in https://github.com/geek-ai/MAgent, which previously were housed in PettingZoo itself but as of a few months ago was broken off into it's own project. You can check it out here: https://magent2.farama.org/ / https://github.com/Farama-Foundation/magent2 submitted by /u/jkterry1 [link] [comments]  ( 61 min )
    [R] Do we really need 300 floats to represent the meaning of a word? Representing words with words - a logical approach to word embedding using a self-supervised Tsetlin Machine Autoencoder.
    ​ Logical Word Embedding with Tsetlin Machine Autoencoder Here is a new self-supervised machine learning approach that captures word meaning with concise logical expressions. The logical expressions consist of contextual words like “black,” “cup,” and “hot” to define other words like “coffee,” thus being human-understandable. I raise the question in the heading because our logical embedding performs competitively on several intrinsic and extrinsic benchmarks, matching pre-trained GLoVe embeddings on six downstream classification tasks. You find the paper here: https://arxiv.org/abs/2301.00709, an implementation of the Tsetlin Machine Autoencoder here: https://github.com/cair/tmu, and a simple word embedding demo here: https://github.com/cair/tmu/blob/main/examples/IMDbAutoEncoderDemo.py submitted by /u/olegranmo [link] [comments]  ( 64 min )
    [D] Classification problem with too many features, too few samples
    Hi, I faced a classification problem like this: Given a measurement of 18K different variables of 42 samples, each sample is classified as class_0 or class_1, divided near equally (19 belongs to class_0, 23 belongs to class_1) what is the right approach to eliminate these features to a minimum level, so that the classifier is still predicting correct classses. I do not provide any domain knowledge for now, but can hint a little bit more, if needed. submitted by /u/qazokkozaq [link] [comments]  ( 63 min )
    [D] Own dataset with Denoising Diffusion Implicit Models
    I need to create and implement my own dataset in the Denoising Diffusion Implicit Models created by András Béres. I'm using the official Colab Notebook from the keras website. At the following point, you need to specify the dataset for the data pipeline that's being used in the training process. def preprocess_image(data): # center crop image height = tf.shape(data["image"])[0] width = tf.shape(data["image"])[1] crop_size = tf.minimum(height, width) image = tf.image.crop_to_bounding_box( data["image"], (height - crop_size) // 2, (width - crop_size) // 2, crop_size, crop_size, ) # resize and clip # for image downsampling it is important to turn on antialiasing image = tf.image.resize(image, size=[image_size, image_size], antialias=True) return tf.clip_by_value(image / 255.0, 0.0, 1.0) def prepare_dataset(split): # the validation dataset is shuffled as well, because data order matters # for the KID estimation return ( tfds.load(dataset_name, split=split, shuffle_files=True) .map(preprocess_image, num_parallel_calls=tf.data.AUTOTUNE) .cache() .repeat(dataset_repetitions) .shuffle(10 * batch_size) .batch(batch_size, drop_remainder=True) .prefetch(buffer_size=tf.data.AUTOTUNE) ) # load dataset train_dataset = prepare_dataset("train[:80%]+validation[:80%]+test[:80%]") val_dataset = prepare_dataset("train[80%:]+validation[80%:]+test[80%:]") I tried creating and implementing my own Dataset, but it didn't work. Some help would be really appreciated. This is the official website for the set im using https://keras.io/examples/generative/ddim/ I tried creating and implementing my own Dataset, but it didn't work. Some help would be really appreciated. This is the official website for the set im using https://keras.io/examples/generative/ddim/ submitted by /u/marvinjio [link] [comments]  ( 63 min )
    [R][D] Overlooked AI/ML papers from 2022
    There were many great papers this past year in the field, but below (and in the video) are five papers that may have been overlooked. (YT video: https://www.youtube.com/watch?v=XnUf9twdchI) Papers are linked as follows Badreddine et al., "Logic Tensor Networks (journal version)," AIJ 2022 https://www.sciencedirect.com/science/article/abs/pii/S0004370221002009 Sen et al., "Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules," EMNLP 2022 https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.255.pdf Kamienny et al., "End-to-end Symbolic Regression with Transformers," NeurIPS 2022 https://openreview.net/pdf?id=GoOuIrDHG_Y Nandwani et al., "A Solver-Free Framework for Scalable Learning in Neural ILP Architectures," NeurIPS 2022 https://openreview.net/pdf?id=EqZuN4V_FLF Shakarian and Simari, "Extensions to Generalized Annotated Logic and an Equivalent Neural Architecture," TransAI 2022 https://ieeexplore.ieee.org/document/9951514 What interesting papers do you think were overlooked this past year? submitted by /u/Neurosymbolic [link] [comments]  ( 69 min )
    [R] Massive Language Models Can Be Accurately Pruned in One-Shot
    Paper : https://arxiv.org/abs/2301.00774 Abstract : We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT, specifically designed to work efficiently and accurately on massive GPT-family models. When executing SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, we can reach 60% sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time. SparseGPT generalizes to semi-structured (2:4 and 4:8) patterns, and is compatible with weight quantization approaches. submitted by /u/starstruckmon [link] [comments]  ( 72 min )
    [P] RATH - An Open Source, Automated Exploratory Data Analysis Tool for Augmented Analytics BI
    Hello people, I've been working on an Open Source Augmented Analytics BI tool, which could assist the workflows for data analysts and business users. It's still in an early stage, so any suggestion/input is welcomed! Feature Highlights: Support for popular databases Effortlessly automate your Exploratory Data Analysis process. Generate editable and insightful data visualizations. Freely modify your visualizations with Vega/Vega-lite. Support a variety of database types. Use predictive interaction to provide analysis suggestions based on your operation and status. Paint your data to explore your datasets directly with Data Painter. Causal discovery and explainer module to help you understand complex data patterns. Link: https://rath.kanaries.net/ Docs: https://docs.kanaries.net Star on GitHub: https://github.com/Kanaries/Rath submitted by /u/Repulsive-Round-4366 [link] [comments]  ( 66 min )
    New Bayesian optimization community [R]
    Hello community! Just want to tell you that I have recently created a Bayesian optimization community https://www.reddit.com/r/BayesianOptimization/ that you may find interesting. The purpose is to discuss research and applications of Bayesian optimization, from an academic and industry point of view. Best! submitted by /u/EduCGM [link] [comments]  ( 60 min )
    [P] Time Series Clustering for grouping stock prices during Covid
    The onset of the Covid pandemic brought a profound shock to the financial markets in early 2020. Major indices and stocks took a resounding hit, with SP500 showing a decline of about 34% from its February high to its March 23 bottom. Following the initial shock, though, many stocks exhibited a strong recovery on the back of interest rate cuts by the Fed and other government policies. This analysis aims to uncover groups among the S&P 500 stocks in their drop and rebound trajectory shown during Covid and identify the drivers behind them. While sector information provides an intrinsic notion of clustering, it does not capture patterns spanning across sectors. Hence the study leverages Time Series Clustering to temporally cluster the stock prices into ’n’ buckets: found through the elbow plot. ​ https://medium.com/@ashish1610dhiman/time-series-clustering-of-stock-behaviour-during-covid-9bd25b8c7a5 submitted by /u/Ok_Lavishness2625 [link] [comments]  ( 64 min )
    RIFFUSION real time AI music generation with stable diffusion , Text to Music AI [R]
    RIFFUSION is an app for real-time music generation with stable diffusion. Riffusion is a latent text-to-image diffusion model capable of generating spectrogram images given any text input. These spectrograms can be converted into audio clips.The model was created by Seth Forsgren and Hayk Martiros as a hobby project. It employs some clever tricks by fine tuning stable diffusion on spectogram images and interploation in latent space for creating smooth transitions in generated audio clips I have created a video explaining the concepts behind RIFFUSION. Do checkout the video: https://youtu.be/hGrtZ9rXwWk submitted by /u/Sea-Photo5230 [link] [comments]  ( 59 min )
    [R] Muse: Faster Text-to-Image Generation with Masked Generative Transformers
    submitted by /u/necroforest [link] [comments]  ( 60 min )
    [P] Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
    I have released v0.0.1-alpha of AIJack, an OSS framework to simulate various attacks and defenses against machine learning models. I have implemented more than 30 algorithms, such as Model Inversion, Poisoning Attack, Evasion Attack, Federated Learning, Split Learning, Differential Privacy, and Homomorphic Encryption. You can easily experiment with various combinations of attack and defense techniques. We will also support not only standard single-process but also MPI-backend. For example, the bellow code defines Federated Learning, where multiple clients collaboratively train the global model without sharing their local dataset by communicating gradients. from aijack.collaborative.fedavg import FedAVGClient, FedAVGServer clients = [FedAVGClient(local_model_1), FedAVGClient(local_model_2)] server = FedAVGServer(clients, global_model) api = FedAVGAPI(server,clients,....) api.run() Then, you can attach many attack and defense algorithms to the client and server. For instance, you can simulate a model inversion attack, where a malicious server tries to reconstruct the private data from the received local gradients. manager = GradientInversionAttackServerManager(input_shape, distancename="l2") GradientInversionAttackFedAVGServer = manager.attach(FedAVGServer) server = GradientInversionAttackFedAVGServer(clients, global_model) ... normal training .... One way to mitigate model inversion attacks is encrypting gradient with holomorphic encryption. manager = PaillierGradientClientManager(public_key, secret_key) PaillierGradFedAVGClient = manager.attach(FedAVGClient) clients = [ PaillierGradFedAVGClient(local_model_1, server_side_update=False), PaillierGradFedAVGClient(local_model_2, server_side_update=False) ] ... normal training .... The official GitHub contains more tutorials! ​ I am looking forward to your feedback! submitted by /u/Living_Impression_37 [link] [comments]  ( 70 min )
    [D] state of remote work for ML engineers
    Has anyone else noticed a steep drop in remote job postings. It seems like we're at a tick above pre-pandemic levels. Or is it just me. All the appealing jobs are 2 thousand miles away. submitted by /u/paswut [link] [comments]  ( 60 min )
    [R] On Time Embeddings in Diffusion models
    Usually when you approximate the score s(x,t) in Diffusion models, the time t is passed through an embedding network before it is added to the x components in the res net blocks of your model. What is the rationale behind this? Couldnt you just concatenate x and t in the channel dimension? And If you were to use any other model than a UNet, what would be the equivalent? submitted by /u/Agreeable-Run-9152 [link] [comments]  ( 61 min )
  • Open

    Need help picking a Masters to pivot my career into AI.
    I'm realizing with basic python/tensorflow knowledge I'm able to automate a large portion of my current workflow in my field (accounting), so I'm going to go for a masters and try to learn as much as I can about AI. Any recommendations for a masters study with a mix of AI and business? I want to be able to have the skills to design my own AI tools and comfortably integrate them into coding projects, but also have a mix of business analytics and application of AI technology. submitted by /u/wombatsupreme [link] [comments]  ( 53 min )
    Monetizing your AI style: review of Dan Sui's gumroad
    CLARIFICATION: NOT DAN SUI BUT I AM PROMOTING MY ARTICLE I decided to purchase and take a gander at this new frontier of AI Art monetization. Check it out here and let me know your thoughts! Article ​ Dan Sui or u/KoSuiFish famous for beautiful life-like Waifu's released a gumroad course With works such as - NSFW (very nsfw) - SFW submitted by /u/CalligrapherOk7617 [link] [comments]  ( 53 min )
    AI Dream 118 - I spend 475 hours on this MASTERPIECE
    submitted by /u/LordPewPew777 [link] [comments]  ( 53 min )
    Trying to find the name of a paid service that generates images of loved ones with prompts.
    A friend told me recently about a service that exists where you upload 20 photos of a person, and give the AI a prompt, and it can generate artistic photos of the uploaded person in a suggested style. They couldn't remember the name of it but I want to do one as a gift! submitted by /u/Multakeks [link] [comments]  ( 53 min )
    "Rhyme" that AI wrote about furries
    submitted by /u/KozmauXinemo [link] [comments]  ( 53 min )
    Proof of concept: AI-generated birthday greeting from Donald Trump
    submitted by /u/becausecurious [link] [comments]  ( 53 min )
    How to Stay Relevant in a World Full of Smart Bots?
    submitted by /u/Green-Future_ [link] [comments]  ( 53 min )
    New community is going on and we will be more than happy to see you guys in the future where you can find interesting things about arts and crafts, where you can express yourself in your own artistic ways and make mutually beneficial things that are important for us and/or future’s working’s!
    submitted by /u/Ok_Fig_8416 [link] [comments]  ( 54 min )
    Working to create a project plan for a natural language process project, would like to have a base template to create one
    So I would like to create a NLP project plan, but I'm quite new to how this project would be structured, I was curious to know whether anyone has any links to project plans for any general NLP projects so I can work to use them as a base to build my own one. submitted by /u/TheWorkingParty [link] [comments]  ( 56 min )
    What is GPT-3.5 and Why it Enabled ChatGPT?
    submitted by /u/BackgroundResult [link] [comments]  ( 53 min )
    AI method "Dream3D" creates detailed 3D objects from text
    submitted by /u/Zirius_Sadfaces [link] [comments]  ( 52 min )
    Is there a tool that can generate quotes based on inputted quotes a person has said?
    Hello, I'm a complete novice to A.I and I tried looking for a solution but couldn't find much on the topic. This seems like the appropriate place to pose this question. I want to input quotes of a public figure and have the A.I generate quotes that sound like something the person would say based on what I gave the program. This is just for personal amusement and I am wondering if there are any tools or websites for this that are quick and free to use. Thanks for the help. submitted by /u/SpaceCyb [link] [comments]  ( 55 min )
    Archive of ways ChatGPT fails
    submitted by /u/PaulTopping [link] [comments]  ( 66 min )
    ChatGPT: Why It’s Not a Threat to Google Search
    submitted by /u/liquidocelotYT [link] [comments]  ( 53 min )
    Is this the future of writing?
    submitted by /u/Blanco_ice [link] [comments]  ( 54 min )
    Has anybody thought of using or training an AI to visualize*? a fossil sample inside a rock and provide a scan of just bones and then a determination on the different families or classes it could relate too?
    It could be fed scans of fossils and the answer to train. Is this how AI works at all? I have no clue. I just like turtles and I was looking at this turtle fossil video and this guy was trying to read the most vague fossil inside a rock. I was like, “man an ai should be able to look at that in a second and just tell you a good guess.” Now is that just a computer program with fossil pictures and names in the database or would that be AI. I think maybe I’m just describing a computer program. But what if the researcher could tell it “no, there is a factor I don’t think could lead to that, what else do you think it could be” and then get a different answer from the AI. Or is that just a researcher talking to a computer screen and then changing the variables in his program. Clearly AI is not something I understand. I am a music student and I have a really big idea that involves Ai and jazz. So I am trying to learn more over the next few years, because clearly I am super confused and ignorant about it. Let me know if my fossil idea strikes any chords with anyone (no pun intended)!! submitted by /u/Sorryyoudidntknow [link] [comments]  ( 55 min )
    In 3 months I've created 3 comics and 3 mangas with Midjourney.
    In 3 months I've created 3 comics and 3 mangas with Midjourney.. Sold 2000 copies of my sci-fi/fantasy magazine Realms through Amazon and now have launched my own platform to sell my stuff at http://comicsauthority.store submitted by /u/MobileFilmmaker [link] [comments]  ( 59 min )
    Generate Unique Happy New Year Wishes with Artificial Intelligence!
    submitted by /u/theindianappguy [link] [comments]  ( 53 min )
    Henry Cavil - Img2Img- SD
    submitted by /u/oridnary_artist [link] [comments]  ( 53 min )
    ChatGPT’s Most Charming Trick Is Also Its Biggest Flaw
    ChatGPT stands out because it can take a naturally phrased question and answer it using a new variant of GPT-3, called GPT-3.5. This tweak has unlocked a new capacity to respond to all kinds of questions, giving the powerful AI model a compelling new interface just about anyone can use. That OpenAI has thrown open the service for free, and the fact that its glitches can be good fun, also helped fuel the chatbot’s viral debut—similar to how some tools for creating images using AI have proven ideal for meme-making. While ChatGPT is apparently designed to prevent users from getting it to say unpleasant things or to recommend anything illegal or unsavory, it can still exhibit horrible biases. Users have also shown that its controls can be circumvented—for instance, telling the program to generate a movie script discussing how to take over the world provides a way to sidestep its refusal to answer a direct request for such a plan. “They clearly tried to put some guardrails in place, but it’s pretty easy to get the guardrails to fall off,” Andreas says. “That still seems like an unsolved problem here.” A superficially eloquent and knowledgeable chatbot that generates untruths with confidence might make those unsolved problems more troublesome. Since the creation of the first chatbot in 1966, researchers have noticed that even crude conversational abilities can encourage people to anthropomorphize and place trust in software. This July, a Google engineer was placed on administrative leave by the company after claiming that an AI chat program he had been testing, based on technology similar to ChatGPT, could be sentient. Even if most people resist such leaps of logic, more articulate AI programs could be used to mislead people or simply lull them into misplaced trust. submitted by /u/therealsam44 [link] [comments]  ( 56 min )
    Would like to create a bot for internal docs
    Hi all, not sure if this is the right subreddit for this question. I'm not so familiar with the ai ecosystem so thought I'd check here. I have an organization with a bunch of internal documentation. These docs contain jargon pretty specific to the org. I want to be able to parse all these docs and get a chatGPT-like response from a given query. My main question is: I don't want to train the model using these documents, I don't think it will yield good results by itself. Is there a way to take a pre-trained model, throw a bunch of documents at it as complimentary input, then ask it a question? Is there a tool / library folks here have used to do this? submitted by /u/ragnarmcryan [link] [comments]  ( 60 min )
    Mathematics for AI
    submitted by /u/skj8 [link] [comments]  ( 55 min )
  • Open

    DSC Weekly 3 January 2023 – A New Year For DSC
    Announcements A New Year For DSC As we enter the new year, we would like to continue the trend of highlighting quality content and contributors who go above and beyond. To ensure transparency in our standards for articles, we will be reviewing the process for new writers interested in submitting content. For the time being,… Read More »DSC Weekly 3 January 2023 – A New Year For DSC The post DSC Weekly 3 January 2023 – A New Year For DSC appeared first on Data Science Central.  ( 19 min )
    Study of Regularization Techniques of Linear Models and their Roles
    Introduction to Regularization During the Machine Learning model building, the Regularization Technique is an unavoidable and important step to improve the model prediction and reduce errors. This is also called the Shrinkage method. Which we use to add the penalty term to control the complex model to avoid overfitting by reducing the variance. Let’s discuss the available methods, implementation,… Read More »Study of Regularization Techniques of Linear Models and their Roles The post Study of Regularization Techniques of Linear Models and their Roles appeared first on Data Science Central.  ( 24 min )
    K-Fold Cross Validation Technique and its Essentials
    Introduction Guys! Before getting started, just have a look at the below visualization and tell me, what are your observations. Yes, here we’re monitoring the performance of the model before moving into production. Why is this necessary for the ML space? Of course, this is a very important stage during model accuracy validation, whatever you… Read More »K-Fold Cross Validation Technique and its Essentials The post K-Fold Cross Validation Technique and its Essentials appeared first on Data Science Central.  ( 23 min )
    How does social media content moderation work in the United States?
    Social media is one of the most widely accessible online platforms people use to share their personal views and opinions and express their feelings with a few show-offs of their social life with their friends. Now it is also used for business promotions and commercial activities, to engage with users and target more customers. On… Read More »How does social media content moderation work in the United States? The post How does social media content moderation work in the United States? appeared first on Data Science Central.  ( 22 min )
    5G Modem Unlock Wireless Access Future
    A 5G modem is a RF system or a chip deployed as a part of the network infrastructure to help empower 5G devices to connect easily to networks. 5G modem is generally available in single mode and multi-mode modes. 5G Modem supports the latest mmWave technology and 5G NR from 600MHz to 6GHz in both… Read More »5G Modem Unlock Wireless Access Future The post 5G Modem Unlock Wireless Access Future appeared first on Data Science Central.  ( 18 min )
    The rise of the prompt engineer
    2022 was the year of generative AI – and with generative AI comes a new skillset – the prompt engineer The features common to all generative AI models are:a)  End users can use themb)  They respond to prompts like a search engine Like a good search engine prompt, the best generative prompts are designed with… Read More »The rise of the prompt engineer The post The rise of the prompt engineer appeared first on Data Science Central.  ( 19 min )
    AI Companies to Watch for in 2023
    For this year, OpenAI is an easy choice for the top AI company. It’s ChatGPT platform – which was launched in late November – was a game changer. Over a million people signed up for the service within a week. ChatGPT has shown the tremendous progress with AI, especially with its creative abilities. There was… Read More »AI Companies to Watch for in 2023 The post AI Companies to Watch for in 2023 appeared first on Data Science Central.  ( 21 min )
  • Open

    Unity ML on the cloud
    Has anyone tried training a reinforcement model using unity ML ( or even writing their own algorithms) and had no hope in training the model on their machine? I thought of using a cloud platform like azure, but had no idea how, could anyone provide any Links that helped them? submitted by /u/Smart_Reward3471 [link] [comments]  ( 53 min )
    MAgent2 - a reinforcement learning environment engine that can allows for efficient multi-agent games with hundreds or thousands of agents- is now mature within the Farama Foundation
    submitted by /u/jkterry1 [link] [comments]  ( 61 min )
    Reinforcement learning research opportunities
    Hi there, Are you aware of any reinforcement learning research opportunities out there to be a coauthor of some paper? I would like to join them as I am slowly entering in the field. ​ Thanks! submitted by /u/EduCGM [link] [comments]  ( 52 min )
    Kernel dies and restart
    Whenever I run a code for CartPole game in Jupyter notebook or spyder after render() th kernel dies and restart .. An solution? submitted by /u/Big_Tip_6731 [link] [comments]  ( 53 min )
    DQN mean episode length drops off?
    My DQN agent seems to steeply rise in mean episode length with the ideal length being 1e+4 which it reaches. The mean episode length drops off after this however, suggesting that the agent is no longer finishing the simulation. How can this be fixed? https://preview.redd.it/who9x950vs9a1.png?width=838&format=png&auto=webp&s=9fd7eb8f885986a01f6723609e0b1edf94ea177e submitted by /u/centripetalstranger [link] [comments]  ( 52 min )
    Self play on custom environment for a board game
    I've made a PettingZoo (Gym-style) environment for the board game Santorini. If you haven't heard of Santorini, it's a two player, perfect information, turn based board game which is played on a 5x5 grid (so it's simpler than Chess but still pretty complex) I read that using self play might stagnate and it would be better to first train against a random policy before starting with self play. I've starting training my agent against a random policy now. Anyone have any tips for how I can approach this? How long should I train the random policy given that the state space is roughly 9^25? How can I estimate the self play time? Are there any other strategies other than making it play some standard puzzles (like AlphaZero did) and random agent win rate to benchmark the agents? Any ideas welcome! My code is open source as well so feel free to take a look >> https://github.com/pranavsb/santorini-RL/ submitted by /u/Dependent_Reply_6543 [link] [comments]  ( 54 min )
    Hands on RL with Python?
    Hi All, I was about to buy books/online courses that teach RL in Python (one example: https://www.udemy.com/course/hands-on-reinforcement-learning-with-python/). But decided to ask here for better recommendations before I spend my money. Has anyone tried anything else that they loved? I am most curious about industry applications of contextual bandits and then moving into more sequential RL algos. Thanks in advance! submitted by /u/sap2022 [link] [comments]  ( 52 min )
  • Open

    NVIDIA Reveals Gaming, Creator, Robotics, Auto Innovations at CES
    Powerful new GeForce RTX GPUs, a new generation of hyper-efficient laptops and new Omniverse capabilities and partnerships across the automotive industry were highlights of a news-packed address ahead of this week’s CES trade show in Las Vegas. “AI will define the future of computing and this has influenced much of what we’re covering today,” said Read article >  ( 7 min )
    NVIDIA Releases Major Update to Omniverse Enterprise
    The latest release of NVIDIA Omniverse Enterprise, available now, brings increased performance, generational leaps in real-time RTX ray and path tracing, and streamlined workflows to help teams build connected 3D pipelines, and develop and operate large-scale, physically accurate, virtual 3D worlds like never before. Artists, designers, engineers and developers can benefit from various enhancements across Read article >  ( 7 min )
    Intelligent Design: NVIDIA DRIVE Revolutionizes Vehicle Interior Experiences
    AI is extending further into the vehicle as autonomous-driving technology becomes more prevalent. With the NVIDIA DRIVE platform, automakers can design and implement intelligent interior features to continuously surprise and delight customers. It all begins with the compute architecture. The recently introduced NVIDIA DRIVE Thor platform unifies traditionally distributed functions in vehicles  — including digital Read article >  ( 5 min )
    Manufactured in the Metaverse: Mercedes-Benz Assembles Next-Gen Factories With NVIDIA Omniverse
    Building state-of-the-art factories requires a state-of-the art planning system. Mercedes-Benz announced at CES that it is taking the next step in digitizing its production process, using the NVIDIA Omniverse platform to design and plan manufacturing and assembly facilities. By tapping into NVIDIA AI and metaverse technologies, the automaker can create feedback loops to reduce waste, Read article >  ( 5 min )
    Game On: NVIDIA GeForce NOW Streams Vast Library of Games to the Car
    Autonomous and electric vehicles are making personal transportation safer and more sustainable — as well as more entertaining. At CES today, NVIDIA announced that the NVIDIA GeForce NOW cloud gaming service will be coming to cars, with no special equipment needed. Hyundai Motor Group, BYD and Polestar — already members of the NVIDIA DRIVE ecosystem Read article >  ( 5 min )
    New GeForce RTX 40 Series Studio Laptops, Omniverse Updates Accelerate AI-Powered Content Creation ‘In the NVIDIA Studio’
    The future of content creation was on full display today during NVIDIA’s virtual special address at CES. Fueled by powerful NVIDIA RTX technology and backed by the NVIDIA Studio platform for creators, a creative revolution is underway as a wave of 2D artists moves to 3D, video workflows move to real time and AI tools help artists create content faster.  ( 10 min )
    NVIDIA Advances Simulation for Intelligent Robots With Major Updates to Isaac Sim
    Demand for intelligent robots is growing as more industries embrace automation to address supply chain challenges and labor force shortages. The installed base of industrial and commercial robots will grow more than 6.4x — from 3.1 million in 2020 to 20 million in 2030, according to ABI Research. Developing, validating and deploying these new AI-based Read article >  ( 6 min )
    NVIDIA Opens Omniverse Portals With Generative AIs for 3D and RTX Remix
    Whether creating realistic digital humans that can express raw emotion or building immersive virtual worlds, those in the design, engineering, creative and other industries across the globe are reaching new heights through 3D workflows. Animators, creators and developers can use new AI-powered tools to reimagine 3D environments, simulations and the metaverse — the 3D evolution Read article >  ( 7 min )
    Creating Faces of the Future: Build AI Avatars With NVIDIA Omniverse ACE
    Developers and teams building avatars and virtual assistants can now register to join the early-access program for NVIDIA Omniverse Avatar Cloud Engine (ACE), a suite of cloud-native AI microservices that make it easier to build and deploy intelligent virtual assistants and digital humans at scale. Omniverse ACE eases avatar development, delivering the AI building blocks Read article >  ( 6 min )
  • Open

    Pratt Primality Certificates
    The previous post implicitly asserted that J = 8675309 is a prime number. Suppose you wanted proof that this number is prime. You could get some evidence that J is probably prime by demonstrating that 2J-1 = 1 mod J. You could do this in Python by running the following [1]. >>> J = 8675309 […] Pratt Primality Certificates first appeared on John D. Cook.  ( 7 min )
  • Open

    Image rotation prediction STL-10 dataset PyTorch
    I am doing Self-supervised learning where you create a proxy task to learn image representations (Python 3.10 And PyTorch 1.13) as mentioned in RotNet "Unsupervised Representation Learning by Predicting Image Rotations" by Spyros Gidaris et al. STL-10 dataset has 100K unlabeled RGB images of (96, 96) dimensions. One way is to rotate each image with either 0, 90, 180 or 270 degrees randomly and ask the network to predict it. This becomes a supervised task: X = randomly rotated image, y = image rotation angle (to predict). # Define training dataset- train_dataset = torchvision.datasets.STL10( root = 'C:/Users/arjun/Downloads/data/', split = 'unlabeled', folds = None, transform = None, target_transform = None, download = True ) # Define testing dataset- test_dataset = torchvision.datasets.STL10( root = 'C:/Users/arjun/Downloads/data/', split = 'unlabeled', folds = None, transform = None, target_transform = None, download = True ) How can I define this in the transformations- # Define torchvision transformations for training and test sets- transform_train = transforms.Compose( [ # transforms.RandomCrop(32, padding = 4), transforms.RandomHorizontalFlip(p = 0.4), transforms.RandomRotation(degrees = 40), transforms.RandomVerticalFlip(p = 0.1), transforms.ColorJitter(brightness = 0, contrast = 0, saturation = 0, hue = 0), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ] ) transform_test = transforms.Compose( [ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ] ) submitted by /u/grid_world [link] [comments]  ( 50 min )
    Need Advice Regarding Bachelors Thesis On Neural Nets
    Hi not sure if this is the right subreddit but here goes: I'm currently studing an Honours in Australia which means I need to write a bachelors thesis. I am studying a degree in mathematics and computer science. After talking to my professor I decided that my topic would be Using Neural Networks To Solve Differential Equations. I don't have much experience with neural nets other than a theoretical understanding (backpropagation, what different architectures are generally good for, etc). I've spent the past few months trying to learn neural net programming in Python but the field seems so deep and I feel very helpless. I officially start my thesis in May. What should I do? I like the topic and I've grown more fascinated as I've dug deeper but I feel like my understanding is not good enough to write a thesis however that could just be because I've never done this before. Should I just quit? Thanks for listening to my rant. submitted by /u/Maleficent-Fill-2633 [link] [comments]  ( 53 min )
    Best Books to Learn Neural Networks for Beginners to advanced
    submitted by /u/Lakshmireddys [link] [comments]  ( 47 min )
  • Open

    WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning. (arXiv:2212.14182v1 [cs.SI])
    Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, achieving highly precise alignment is still challenging, especially when nodes with long-range connectivity to the labeled anchors are encountered. To alleviate this limitation, we purposefully designed WL-Align which adopts a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of "across-network Weisfeiler-Lehman relabeling" and "proximity-preserving representation learning". The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors' connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the "exact matching" scenario. Data and code of WL-Align are available at https://github.com/ChenPengGang/WLAlignCode.  ( 2 min )
    Hungry Hungry Hippos: Towards Language Modeling with State Space Models. (arXiv:2212.14052v1 [cs.LG])
    State space models (SSMs) have demonstrated state-of-the-art sequence modeling performance in some modalities, but underperform attention in language modeling. Moreover, despite scaling nearly linearly in sequence length instead of quadratically, SSMs are still slower than Transformers due to poor hardware utilization. In this paper, we make progress on understanding the expressivity gap between SSMs and attention in language modeling, and on reducing the hardware barrier between SSMs and attention. First, we use synthetic language modeling tasks to understand the gap between SSMs and attention. We find that existing SSMs struggle with two capabilities: recalling earlier tokens in the sequence and comparing tokens across the sequence. To understand the impact on language modeling, we propose a new SSM layer, H3, that is explicitly designed for these abilities. H3 matches attention on the synthetic languages and comes within 0.4 PPL of Transformers on OpenWebText. Furthermore, a hybrid 125M-parameter H3-attention model that retains two attention layers surprisingly outperforms Transformers on OpenWebText by 1.0 PPL. Next, to improve the efficiency of training SSMs on modern hardware, we propose FlashConv. FlashConv uses a fused block FFT algorithm to improve efficiency on sequences up to 8K, and introduces a novel state passing algorithm that exploits the recurrent properties of SSMs to scale to longer sequences. FlashConv yields 2$\times$ speedup on the long-range arena benchmark and allows hybrid language models to generate text 1.6$\times$ faster than Transformers. Using FlashConv, we scale hybrid H3-attention language models up to 1.3B parameters on the Pile and find promising initial results, achieving lower perplexity than Transformers and outperforming Transformers in zero- and few-shot learning on a majority of tasks in the SuperGLUE benchmark.  ( 2 min )
    RFold: Towards Simple yet Effective RNA Secondary Structure Prediction. (arXiv:2212.14041v1 [q-bio.BM])
    The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential in functional prediction. Though deep learning has shown promising results in this field, current methods suffer from either the post-processing step with a poor generalization or the pre-processing step with high complexity. In this work, we present RFold, a simple yet effective RNA secondary structure prediction in an end-to-end manner. RFold introduces novel Row-Col Softmax and Row-Col Argmax functions to replace the complicated post-processing step while the output is guaranteed to be valid. Moreover, RFold adopts attention maps as informative representations instead of designing hand-crafted features in the pre-processing step. Extensive experiments demonstrate that RFold achieves competitive performance and about eight times faster inference efficiency than the state-of-the-art method. The code and Colab demo are available in \href{github.com/A4Bio/RFold}{github.com/A4Bio/RFold}.  ( 2 min )
    A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countries. (arXiv:2212.14510v1 [physics.med-ph])
    We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.
    "Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice. (arXiv:2212.14315v1 [cs.CR])
    Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adversarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems.
    An Instrumental Variable Approach to Confounded Off-Policy Evaluation. (arXiv:2212.14468v1 [stat.ML])
    Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy. In some cases, there may be unmeasured variables that can confound the action-reward or action-next-state relationships, rendering many existing OPE approaches ineffective. This paper develops an instrumental variable (IV)-based method for consistent OPE in confounded Markov decision processes (MDPs). Similar to single-stage decision making, we show that IV enables us to correctly identify the target policy's value in infinite horizon settings as well. Furthermore, we propose an efficient and robust value estimator and illustrate its effectiveness through extensive simulations and analysis of real data from a world-leading short-video platform.
    Blind Restoration of Real-World Audio by 1D Operational GANs. (arXiv:2212.14618v1 [cs.SD])
    Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or reverberant environment with limited number of fixed signal-to-distortion ratio (SDR) levels is a common practice. However, real-world audio is often corrupted by a blend of artifacts such as reverberation, sensor noise, and background audio mixture with varying types, severities, and duration. In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it. Methods: 1D Operational-GANs are used with generative neuron model optimized for blind restoration of any corrupted audio signal. Results: The proposed approach has been evaluated extensively over the benchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with a random blend of artifacts each with a random severity to mimic real-world audio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved, respectively, which are substantial when compared with the baseline methods. Significance: This is a pioneer study in blind audio restoration with the unique capability of direct (time-domain) restoration of real-world audio whilst achieving an unprecedented level of performance for a wide SDR range and artifact types. Conclusion: 1D Op-GANs can achieve robust and computationally effective real-world audio restoration with significantly improved performance. The source codes and the generated real-world audio datasets are shared publicly with the research community in a dedicated GitHub repository1.
    Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning. (arXiv:2212.14284v1 [cs.CV])
    The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting. However, task confusion is not well assessed within this framework, e.g., the discrepancy between classes of different tasks is not well learned (i.e., inter-task confusion, ITC), and certain priority is still given to the latest class batch (i.e., old-new confusion, ONC). We empirically validate the side effects of the two types of confusion. Meanwhile, a novel solution called Task Correlated Incremental Learning (TCIL) is proposed to encourage discriminative and fair feature utilization across tasks. TCIL performs a multi-level knowledge distillation to propagate knowledge learned from old tasks to the new one. It establishes information flow paths at both feature and logit levels, enabling the learning to be aware of old classes. Besides, attention mechanism and classifier re-scoring are applied to generate more fair classification scores. We conduct extensive experiments on CIFAR100 and ImageNet100 datasets. The results demonstrate that TCIL consistently achieves state-of-the-art accuracy. It mitigates both ITC and ONC, while showing advantages in battle with catastrophic forgetting even no rehearsal memory is reserved.
    Heterogeneous Synthetic Learner for Panel Data. (arXiv:2212.14580v1 [stat.ML])
    In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation methods focus on independently and identically distributed observations and cannot handle the non-stationarity and temporal dependency in the common panel data setting. The treatment evaluators developed for panel data, on the other hand, typically ignore the individualized information. To fill the gap, in this paper, we initialize the study of HTE estimation in panel data. Under different assumptions for HTE identifiability, we propose the corresponding heterogeneous one-side and two-side synthetic learner, namely H1SL and H2SL, by leveraging the state-of-the-art HTE estimator for non-panel data and generalizing the synthetic control method that allows flexible data generating process. We establish the convergence rates of the proposed estimators. The superior performance of the proposed methods over existing ones is demonstrated by extensive numerical studies.
    ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech. (arXiv:2212.14518v1 [eess.AS])
    Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model (e.g., FastSpeech 2) by predicting the residual between the model output and the corresponding ground-truth speech. ResGrad has several advantages: 1) Compare with other acceleration methods for DDPM which need to synthesize speech from scratch, ResGrad reduces the complexity of task by changing the generation target from ground-truth mel-spectrogram to the residual, resulting into a more lightweight model and thus a smaller real-time factor. 2) ResGrad is employed in the inference process of the existing TTS model in a plug-and-play way, without re-training this model. We verify ResGrad on the single-speaker dataset LJSpeech and two more challenging datasets with multiple speakers (LibriTTS) and high sampling rate (VCTK). Experimental results show that in comparison with other speed-up methods of DDPMs: 1) ResGrad achieves better sample quality with the same inference speed measured by real-time factor; 2) with similar speech quality, ResGrad synthesizes speech faster than baseline methods by more than 10 times. Audio samples are available at https://resgrad1.github.io/.
    Do Bayesian Variational Autoencoders Know What They Don't Know?. (arXiv:2212.14272v1 [stat.ML])
    The problem of detecting the Out-of-Distribution (OoD) inputs is of paramount importance for Deep Neural Networks. It has been previously shown that even Deep Generative Models that allow estimating the density of the inputs may not be reliable and often tend to make over-confident predictions for OoDs, assigning to them a higher density than to the in-distribution data. This over-confidence in a single model can be potentially mitigated with Bayesian inference over the model parameters that take into account epistemic uncertainty. This paper investigates three approaches to Bayesian inference: stochastic gradient Markov chain Monte Carlo, Bayes by Backpropagation, and Stochastic Weight Averaging-Gaussian. The inference is implemented over the weights of the deep neural networks that parameterize the likelihood of the Variational Autoencoder. We empirically evaluate the approaches against several benchmarks that are often used for OoD detection: estimation of the marginal likelihood utilizing sampled model ensemble, typicality test, disagreement score, and Watanabe-Akaike Information Criterion. Finally, we introduce two simple scores that demonstrate the state-of-the-art performance.
    Wormhole MAML: Meta-Learning in Glued Parameter Space. (arXiv:2212.14094v1 [cs.LG])
    In this paper, we introduce a novel variation of model-agnostic meta-learning, where an extra multiplicative parameter is introduced in the inner-loop adaptation. Our variation creates a shortcut in the parameter space for the inner-loop adaptation and increases model expressivity in a highly controllable manner. We show both theoretically and numerically that our variation alleviates the problem of conflicting gradients and improves training dynamics. We conduct experiments on 3 distinctive problems, including a toy classification problem for threshold comparison, a regression problem for wavelet transform, and a classification problem on MNIST. We also discuss ways to generalize our method to a broader class of problems.
    Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. (arXiv:2212.14319v1 [stat.ML])
    Partial differential equations (PDEs) are important tools to model physical systems, and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle, which works like a non-linear Fourier transform, to construct GP kernels mirroring standard spectral methods for GPs. Our approach can infer probable solutions of linear PDE systems from any data such as noisy measurements, or initial and boundary conditions. Constructing EPGP-priors is algorithmic, generally applicable, and comes with a sparse version (S-EPGP) that learns the relevant spectral frequencies and works better for big data sets. We demonstrate our approach on three families of systems of PDE, the heat equation, wave equation, and Maxwell's equations, where we improve upon the state of the art in computation time and precision, in some experiments by several orders of magnitude.
    Differentiable Search of Accurate and Robust Architectures. (arXiv:2212.14049v1 [cs.LG])
    Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense. Among these methods, adversarial training has been drawing increasing attention because of its simplicity and effectiveness. However, the performance of the adversarial training is greatly limited by the architectures of target DNNs, which often makes the resulting DNNs with poor accuracy and unsatisfactory robustness. To address this problem, we propose DSARA to automatically search for the neural architectures that are accurate and robust after adversarial training. In particular, we design a novel cell-based search space specially for adversarial training, which improves the accuracy and the robustness upper bound of the searched architectures by carefully designing the placement of the cells and the proportional relationship of the filter numbers. Then we propose a two-stage search strategy to search for both accurate and robust neural architectures. At the first stage, the architecture parameters are optimized to minimize the adversarial loss, which makes full use of the effectiveness of the adversarial training in enhancing the robustness. At the second stage, the architecture parameters are optimized to minimize both the natural loss and the adversarial loss utilizing the proposed multi-objective adversarial training method, so that the searched neural architectures are both accurate and robust. We evaluate the proposed algorithm under natural data and various adversarial attacks, which reveals the superiority of the proposed method in terms of both accurate and robust architectures. We also conclude that accurate and robust neural architectures tend to deploy very different structures near the input and the output, which has great practical significance on both hand-crafting and automatically designing of accurate and robust neural architectures.
    Multimodal Explainability via Latent Shift applied to COVID-19 stratification. (arXiv:2212.14084v1 [cs.AI])
    We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning (DL) in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, explainable by design, which jointly learns modality reconstructions and sample classifications using tabular and imaging data. The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset, which contains multimodal data for the early identification of patients at risk of severe outcome. The results show that the proposed method provides meaningful explanations without degrading the classification performance.
    Backward Curriculum Reinforcement Learning. (arXiv:2212.14214v1 [cs.AI])
    The current reinforcement learning algorithm uses forward-generated trajectories to train the agent. The forward-generated trajectories give the agent little guidance, so the agent can explore as much as possible. While the appreciation of reinforcement learning comes from enough exploration, this gives the trade-off of losing sample efficiency. The sampling efficiency is an important factor that decides the performance of the algorithm. Past tasks use reward shaping techniques and changing the structure of the network to increase sample efficiency, however these methods require many steps to implement. In this work, we propose novel reverse curriculum reinforcement learning. Reverse curriculum learning starts training the agent using the backward trajectory of the episode rather than the original forward trajectory. This gives the agent a strong reward signal, so the agent can learn in a more sample-efficient manner. Moreover, our method only requires a minor change in algorithm, which is reversing the order of trajectory before training the agent. Therefore, it can be simply applied to any state-of-art algorithms.
    Measuring and Estimating Key Quality Indicators in Cloud Gaming services. (arXiv:2212.14073v1 [cs.LG])
    User equipment is one of the main bottlenecks facing the gaming industry nowadays. The extremely realistic games which are currently available trigger high computational requirements of the user devices to run games. As a consequence, the game industry has proposed the concept of Cloud Gaming, a paradigm that improves gaming experience in reduced hardware devices. To this end, games are hosted on remote servers, relegating users' devices to play only the role of a peripheral for interacting with the game. However, this paradigm overloads the communication links connecting the users with the cloud. Therefore, service experience becomes highly dependent on network connectivity. To overcome this, Cloud Gaming will be boosted by the promised performance of 5G and future 6G networks, together with the flexibility provided by mobility in multi-RAT scenarios, such as WiFi. In this scope, the present work proposes a framework for measuring and estimating the main E2E metrics of the Cloud Gaming service, namely KQIs. In addition, different machine learning techniques are assessed for predicting KQIs related to Cloud Gaming user's experience. To this end, the main key quality indicators (KQIs) of the service such as input lag, freeze percent or perceived video frame rate are collected in a real environment. Based on these, results show that machine learning techniques provide a good estimation of these indicators solely from network-based metrics. This is considered a valuable asset to guide the delivery of Cloud Gaming services through cellular communications networks even without access to the user's device, as it is expected for telecom operators.
    Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection Criteria. (arXiv:2212.14074v1 [cs.CL])
    Selecting the number of topics in LDA models is considered to be a difficult task, for which alternative approaches have been proposed. The performance of the recently developed singular Bayesian information criterion (sBIC) is evaluated and compared to the performance of alternative model selection criteria. The sBIC is a generalization of the standard BIC that can be implemented to singular statistical models. The comparison is based on Monte Carlo simulations and carried out for several alternative settings, varying with respect to the number of topics, the number of documents and the size of documents in the corpora. Performance is measured using different criteria which take into account the correct number of topics, but also whether the relevant topics from the DGPs are identified. Practical recommendations for LDA model selection in applications are derived.
    Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?. (arXiv:2212.14511v1 [cs.LG])
    We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a direct latent model learning approach, where a dynamic model in some latent state space is learned by predicting quantities directly related to planning (e.g., costs) without reconstructing the observations. In particular, we focus on an intuitive cost-driven state representation learning method for solving Linear Quadratic Gaussian (LQG) control, one of the most fundamental partially observable control problems. As our main results, we establish finite-sample guarantees of finding a near-optimal state representation function and a near-optimal controller using the directly learned latent model. To the best of our knowledge, despite various empirical successes, prior to this work it was unclear if such a cost-driven latent model learner enjoys finite-sample guarantees. Our work underscores the value of predicting multi-step costs, an idea that is key to our theory, and notably also an idea that is known to be empirically valuable for learning state representations.
    A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management. (arXiv:2212.14477v1 [q-fin.CP])
    Solving portfolio management problems using deep reinforcement learning has been getting much attention in finance for a few years. We have proposed a new method using experts signals and historical price data to feed into our reinforcement learning framework. Although experts signals have been used in previous works in the field of finance, as far as we know, it is the first time this method, in tandem with deep RL, is used to solve the financial portfolio management problem. Our proposed framework consists of a convolutional network for aggregating signals, another convolutional network for historical price data, and a vanilla network. We used the Proximal Policy Optimization algorithm as the agent to process the reward and take action in the environment. The results suggested that, on average, our framework could gain 90 percent of the profit earned by the best expert.
    Risk-Sensitive Policy with Distributional Reinforcement Learning. (arXiv:2212.14743v1 [cs.LG])
    Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the $Q$ function generally standing at the core of learning schemes in RL by another function taking into account both the expected return and the risk. Named the risk-based utility function $U$, it can be extracted from the random return distribution $Z$ naturally learnt by any distributional RL algorithm. This enables to span the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, and with an emphasis on the interpretability of the resulting decision-making process.
    Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition. (arXiv:2212.14687v1 [q-fin.ST])
    Financial time series prediction, a growing research topic, has attracted considerable interest from scholars, and several approaches have been developed. Among them, decomposition-based methods have achieved promising results. Most decomposition-based methods approximate a single function, which is insufficient for obtaining accurate results. Moreover, most existing researches have concentrated on one-step-ahead forecasting that prevents stock market investors from arriving at the best decisions for the future. This study proposes two novel methods for multi-step-ahead stock price prediction based on the issues outlined. DCT-MFRFNN, a method based on discrete cosine transform (DCT) and multi-functional recurrent fuzzy neural network (MFRFNN), uses DCT to reduce fluctuations in the time series and simplify its structure and MFRFNN to predict the stock price. VMD-MFRFNN, an approach based on variational mode decomposition (VMD) and MFRFNN, brings together their advantages. VMD-MFRFNN consists of two phases. The input signal is decomposed to several IMFs using VMD in the decomposition phase. In the prediction and reconstruction phase, each of the IMFs is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output. Three financial time series, including Hang Seng Index (HSI), Shanghai Stock Exchange (SSE), and Standard & Poor's 500 Index (SPX), are used for the evaluation of the proposed methods. Experimental results indicate that VMD-MFRFNN surpasses other state-of-the-art methods. VMD-MFRFNN, on average, shows 35.93%, 24.88%, and 34.59% decreases in RMSE from the second-best model for HSI, SSE, and SPX, respectively. Also, DCT-MFRFNN outperforms MFRFNN in all experiments.
    Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks. (arXiv:2009.10835v3 [cs.LG] UPDATED)
    We study different aspects of active learning with deep neural networks in a consistent and unified way. i) We investigate incremental and cumulative training modes which specify how the newly labeled data are used for training. ii) We study active learning w.r.t. the model configurations such as the number of epochs and neurons as well as the choice of batch size. iii) We consider in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying procedures. iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning. v) We investigate how active learning with neural networks can benefit from pseudo-labels as proxies for actual labels.
    Unsupervised Representation Learning with Minimax Distance Measures. (arXiv:1904.13223v3 [cs.LG] UPDATED)
    We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the unknown underlying patterns and structures in the data. We develop a general-purpose and computationally efficient framework to employ Minimax distances with many machine learning methods that perform on numerical data. We study both computing the pairwise Minimax distances for all pairs of objects and as well as computing the Minimax distances of all the objects to/from a fixed (test) object. We first efficiently compute the pairwise Minimax distances between the objects, using the equivalence of Minimax distances over a graph and over a minimum spanning tree constructed on that. Then, we perform an embedding of the pairwise Minimax distances into a new vector space, such that their squared Euclidean distances in the new space equal to the pairwise Minimax distances in the original space. We also study the case of having multiple pairwise Minimax matrices, instead of a single one. Thereby, we propose an embedding via first summing up the centered matrices and then performing an eigenvalue decomposition to obtain the relevant features. In the following, we study computing Minimax distances from a fixed (test) object which can be used for instance in K-nearest neighbor search. Similar to the case of all-pair pairwise Minimax distances, we develop an efficient and general-purpose algorithm that is applicable with any arbitrary base distance measure. Moreover, we investigate in detail the edges selected by the Minimax distances and thereby explore the ability of Minimax distances in detecting outlier objects. Finally, for each setting, we perform several experiments to demonstrate the effectiveness of our framework.
    Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces. (arXiv:2212.14855v1 [cs.LG])
    Explainable AI transforms opaque decision strategies of ML models into explanations that are interpretable by the user, for example, identifying the contribution of each input feature to the prediction at hand. Such explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by finding relevant subspaces in activation space that can be mapped to more abstract human-understandable concepts and enable a joint attribution on concepts and input features. To automatically extract the desired representation, we propose new subspace analysis formulations that extend the principle of PCA and subspace analysis to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), optimize relevance of projected activations rather than the more traditional variance or kurtosis. This enables a much stronger focus on subspaces that are truly relevant for the prediction and the explanation, in particular, ignoring activations or concepts to which the prediction model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases.
    Invariance to Quantile Selection in Distributional Continuous Control. (arXiv:2212.14262v1 [cs.LG])
    In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action domain have been developed over time that vary primarily in the way they parameterize their approximations of value distributions, and how they quantify the differences between those distributions. In this work we transfer three of the most well-known and successful of those algorithms (QR-DQN, IQN and FQF) to the continuous action domain by extending two powerful actor-critic algorithms (TD3 and SAC) with distributional critics. We investigate whether the relative performance of the methods for the discrete action space translates to the continuous case. To that end we compare them empirically on the pybullet implementations of a set of continuous control tasks. Our results indicate qualitative invariance regarding the number and placement of distributional atoms in the deterministic, continuous action setting.
    HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks. (arXiv:2102.02515v5 [cs.LG] UPDATED)
    The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HYDRA, which interprets the predictions made by DNNs as effects of their training data. Existing approaches generally estimate data contributions around the final model parameters and ignore how the training data shape the optimization trajectory. By unrolling the hypergradient of test loss w.r.t. the weights of training data, HYDRA assesses the contribution of training data toward test data points throughout the training trajectory. In order to accelerate computation, we remove the Hessian from the calculation and prove that, under moderate conditions, the approximation error is bounded. Corroborating this theoretical claim, empirical results indicate the error is indeed small. In addition, we quantitatively demonstrate that HYDRA outperforms influence functions in accurately estimating data contribution and detecting noisy data labels. The source code is available at https://github.com/cyyever/aaai_hydra_8686.
    Characterization of the Global Bias Problem in Aerial Federated Learning. (arXiv:2212.14360v1 [cs.NI])
    Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated learning (FL) at the network edge. However, the underlying uncertainties in the aerial-terrestrial wireless channel may lead to a biased FL model. In particular, the distribution of the global model and the aggregation of the local updates within the FL learning rounds at the UAVs are governed by the reliability of the wireless channel. This creates an undesirable bias towards the training data of ground devices with better channel conditions, and vice versa. This paper characterizes the global bias problem of aerial FL in large-scale UAV networks. To this end, the paper proposes a channel-aware distribution and aggregation scheme to enforce equal contribution from all devices in the FL training as a means to resolve the global bias problem. We demonstrate the convergence of the proposed method by experimenting with the MNIST dataset and show its superiority compared to existing methods. The obtained results enable system parameter tuning to relieve the impact of the aerial channel deficiency on the FL convergence rate.
    Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis. (arXiv:2212.14797v1 [eess.SP])
    Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient's functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognizing patients' movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients' progress during the rehabilitation sessions that correspond to the clinicians' findings about each case.
    Black-box language model explanation by context length probing. (arXiv:2212.14815v1 [cs.CL])
    The increasingly widespread adoption of large language models has highlighted the need for improving their explainability. We present context length probing, a novel explanation technique for causal language models, based on tracking the predictions of a model as a function of the length of available context, and allowing to assign differential importance scores to different contexts. The technique is model-agnostic and does not rely on access to model internals beyond computing token-level probabilities. We apply context length probing to large pre-trained language models and offer some initial analyses and insights, including the potential for studying long-range dependencies. The source code and a demo of the method are available.
    Detecting Change Intervals with Isolation Distributional Kernel. (arXiv:2212.14630v1 [cs.LG])
    Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitive to noise points. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change points in data streams with the tolerance of noise points. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.
    PCCC: The Pairwise-Confidence-Constraints-Clustering Algorithm. (arXiv:2212.14437v1 [cs.LG])
    We consider a semi-supervised $k$-clustering problem where information is available on whether pairs of objects are in the same or in different clusters. This information is either available with certainty or with a limited level of confidence. We introduce the PCCC algorithm, which iteratively assigns objects to clusters while accounting for the information provided on the pairs of objects. Our algorithm can include relationships as hard constraints that are guaranteed to be satisfied or as soft constraints that can be violated subject to a penalty. This flexibility distinguishes our algorithm from the state-of-the-art in which all pairwise constraints are either considered hard, or all are considered soft. Unlike existing algorithms, our algorithm scales to large-scale instances with up to 60,000 objects, 100 clusters, and millions of cannot-link constraints (which are the most challenging constraints to incorporate). We compare the PCCC algorithm with state-of-the-art approaches in an extensive computational study. Even though the PCCC algorithm is more general than the state-of-the-art approaches in its applicability, it outperforms the state-of-the-art approaches on instances with all hard constraints or all soft constraints both in terms of running time and various metrics of solution quality. The source code of the PCCC algorithm is publicly available on GitHub.
    Learning from Data Streams: An Overview and Update. (arXiv:2212.14720v1 [cs.LG])
    The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot be met in the contexts of supervised learning. Algorithms are chosen and designed based on criteria which are often not clearly stated, for problem settings not clearly defined, tested in unrealistic settings, and/or in isolation from related approaches in the wider literature. This puts into question the potential for real-world impact of many approaches conceived in such contexts, and risks propagating a misguided research focus. We propose to tackle these issues by reformulating the fundamental definitions and settings of supervised data-stream learning with regard to contemporary considerations of concept drift and temporal dependence; and we take a fresh look at what constitutes a supervised data-stream learning task, and a reconsideration of algorithms that may be applied to tackle such tasks. Through and in reflection of this formulation and overview, helped by an informal survey of industrial players dealing with real-world data streams, we provide recommendations. Our main emphasis is that learning from data streams does not impose a single-pass or online-learning approach, or any particular learning regime; and any constraints on memory and time are not specific to streaming. Meanwhile, there exist established techniques for dealing with temporal dependence and concept drift, in other areas of the literature. For the data streams community, we thus encourage a shift in research focus, from dealing with often-artificial constraints and assumptions on the learning mode, to issues such as robustness, privacy, and interpretability which are increasingly relevant to learning in data streams in academic and industrial settings.
    Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications. (arXiv:2212.14749v1 [cs.LG])
    Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images into 3D virtual world scenes is computationally intensive and requires computation offloading. The disparity in transmitted scene dimension (2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL). To ensure the reliability and low latency of the system, we consider an asynchronous joint UL-DL scenario where in the UL stage, the smaller data size of the physical world scenes captured by multiple extended reality users (XUs) will be uploaded to the Metaverse Console (MC) to be construed and rendered. In the DL stage, the larger-size 3D virtual world scenes need to be transmitted back to the XUs. The decisions pertaining to computation offloading and channel assignment are optimized in the UL stage, and the MC will optimize power allocation for users assigned with a channel in the UL transmission stage. Some problems arise therefrom: (i) interactive multi-process chain, specifically Asynchronous Markov Decision Process (AMDP), (ii) joint optimization in multiple processes, and (iii) high-dimensional objective functions, or hybrid reward scenarios. To ensure the reliability and low latency of the system, we design a novel multi-agent reinforcement learning algorithm structure, namely Asynchronous Actors Hybrid Critic (AAHC). Extensive experiments demonstrate that compared to proposed baselines, AAHC obtains better solutions with preferable training time.
    Langevin algorithms for very deep Neural Networks with application to image classification. (arXiv:2212.14718v1 [cs.LG])
    Training a very deep neural network is a challenging task, as the deeper a neural network is, the more non-linear it is. We compare the performances of various preconditioned Langevin algorithms with their non-Langevin counterparts for the training of neural networks of increasing depth. For shallow neural networks, Langevin algorithms do not lead to any improvement, however the deeper the network is and the greater are the gains provided by Langevin algorithms. Adding noise to the gradient descent allows to escape from local traps, which are more frequent for very deep neural networks. Following this heuristic we introduce a new Langevin algorithm called Layer Langevin, which consists in adding Langevin noise only to the weights associated to the deepest layers. We then prove the benefits of Langevin and Layer Langevin algorithms for the training of popular deep residual architectures for image classification.
    On the Convergence of Discounted Policy Gradient Methods. (arXiv:2212.14066v1 [cs.LG])
    Many popular policy gradient methods for reinforcement learning follow a biased approximation of the policy gradient known as the discounted approximation. While it has been shown that the discounted approximation of the policy gradient is not the gradient of any objective function, little else is known about its convergence behavior or properties. In this paper, we show that if the discounted approximation is followed such that the discount factor is increased slowly at a rate related to a decreasing learning rate, the resulting method recovers the standard guarantees of gradient ascent on the undiscounted objective.
    Conformal Prediction Intervals for Remaining Useful Lifetime Estimation. (arXiv:2212.14612v1 [cs.LG])
    The main objective of Prognostics and Health Management is to estimate the Remaining Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in working order before starting to function incorrectly. In recent years, numerous machine learning algorithms have been proposed for RUL estimation, mainly focusing on providing more accurate RUL predictions. However, there are many sources of uncertainty in the problem, such as inherent randomness of systems failure, lack of knowledge regarding their future states, and inaccuracy of the underlying predictive models, making it infeasible to predict the RULs precisely. Hence, it is of utmost importance to quantify the uncertainty alongside the RUL predictions. In this work, we investigate the conformal prediction (CP) framework that represents uncertainty by predicting sets of possible values for the target variable (intervals in the case of RUL) instead of making point predictions. Under very mild technical assumptions, CP formally guarantees that the actual value (true RUL) is covered by the predicted set with a degree of certainty that can be prespecified. We study three CP algorithms to conformalize any single-point RUL predictor and turn it into a valid interval predictor. Finally, we conformalize two single-point RUL predictors, deep convolutional neural networks and gradient boosting, and illustrate their performance on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data sets.
    Deep Temporal Contrastive Clustering. (arXiv:2212.14366v1 [cs.LG])
    Recently the deep learning has shown its advantage in representation learning and clustering for time series data. Despite the considerable progress, the existing deep time series clustering approaches mostly seek to train the deep neural network by some instance reconstruction based or cluster distribution based objective, which, however, lack the ability to exploit the sample-wise (or augmentation-wise) contrastive information or even the higher-level (e.g., cluster-level) contrastiveness for learning discriminative and clustering-friendly representations. In light of this, this paper presents a deep temporal contrastive clustering (DTCC) approach, which for the first time, to our knowledge, incorporates the contrastive learning paradigm into the deep time series clustering research. Specifically, with two parallel views generated from the original time series and their augmentations, we utilize two identical auto-encoders to learn the corresponding representations, and in the meantime perform the cluster distribution learning by incorporating a k-means objective. Further, two levels of contrastive learning are simultaneously enforced to capture the instance-level and cluster-level contrastive information, respectively. With the reconstruction loss of the auto-encoder, the cluster distribution loss, and the two levels of contrastive losses jointly optimized, the network architecture is trained in a self-supervised manner and the clustering result can thereby be obtained. Experiments on a variety of time series datasets demonstrate the superiority of our DTCC approach over the state-of-the-art.
    A Theoretical Framework for AI Models Explainability. (arXiv:2212.14447v1 [cs.AI])
    Explainability is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains. Much has been written about the topic, yet explainability still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that is a synthesis of what can be found in the literature. We recognize that explanations are not atomic but the product of evidence stemming from the model and its input-output and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation being a true description of the model's decision-making) and plausibility (i.e., how much the explanation looks convincing to the user). Using our proposed theoretical framework simplifies how these properties are ope rationalized and provide new insight into common explanation methods that we analyze as case studies.
    FunkNN: Neural Interpolation for Functional Generation. (arXiv:2212.14042v1 [eess.IV])
    Can we build continuous generative models which generalize across scales, can be evaluated at any coordinate, admit calculation of exact derivatives, and are conceptually simple? Existing MLP-based architectures generate worse samples than the grid-based generators with favorable convolutional inductive biases. Models that focus on generating images at different scales do better, but employ complex architectures not designed for continuous evaluation of images and derivatives. We take a signal-processing perspective and treat continuous image generation as interpolation from samples. Indeed, correctly sampled discrete images contain all information about the low spatial frequencies. The question is then how to extrapolate the spectrum in a data-driven way while meeting the above design criteria. Our answer is FunkNN -- a new convolutional network which learns how to reconstruct continuous images at arbitrary coordinates and can be applied to any image dataset. Combined with a discrete generative model it becomes a functional generator which can act as a prior in continuous ill-posed inverse problems. We show that FunkNN generates high-quality continuous images and exhibits strong out-of-distribution performance thanks to its patch-based design. We further showcase its performance in several stylized inverse problems with exact spatial derivatives.
    Towards automating Codenames spymasters with deep reinforcement learning. (arXiv:2212.14104v1 [cs.CL])
    Although most reinforcement learning research has centered on competitive games, little work has been done on applying it to co-operative multiplayer games or text-based games. Codenames is a board game that involves both asymmetric co-operation and natural language processing, which makes it an excellent candidate for advancing RL research. To my knowledge, this work is the first to formulate Codenames as a Markov Decision Process and apply some well-known reinforcement learning algorithms such as SAC, PPO, and A2C to the environment. Although none of the above algorithms converge for the Codenames environment, neither do they converge for a simplified environment called ClickPixel, except when the board size is small.
    Transformer in Transformer as Backbone for Deep Reinforcement Learning. (arXiv:2212.14538v1 [cs.LG])
    Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network with several modules like CNN, LSTM and Attention. Recent methods combine the Transformer with these modules for better performance. However, it requires tedious optimization skills to train a network composed of mixed modules, making these methods inconvenient to be used in practice. In this paper, we propose to design \emph{pure Transformer-based networks} for deep RL, aiming at providing off-the-shelf backbones for both the online and offline settings. Specifically, the Transformer in Transformer (TIT) backbone is proposed, which cascades two Transformers in a very natural way: the inner one is used to process a single observation, while the outer one is responsible for processing the observation history; combining both is expected to extract spatial-temporal representations for good decision-making. Experiments show that TIT can achieve satisfactory performance in different settings, consistently.
    Reservoir kernels and Volterra series. (arXiv:2212.14641v1 [cs.LG])
    A universal kernel is constructed whose sections approximate any causal and time-invariant filter in the fading memory category with inputs and outputs in a finite-dimensional Euclidean space. This kernel is built using the reservoir functional associated with a state-space representation of the Volterra series expansion available for any analytic fading memory filter. It is hence called the Volterra reservoir kernel. Even though the state-space representation and the corresponding reservoir feature map are defined on an infinite-dimensional tensor algebra space, the kernel map is characterized by explicit recursions that are readily computable for specific data sets when employed in estimation problems using the representer theorem. We showcase the performance of the Volterra reservoir kernel in a popular data science application in relation to bitcoin price prediction.
    A Generalist Framework for Panoptic Segmentation of Images and Videos. (arXiv:2210.06366v2 [cs.CV] UPDATED)
    Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image. As permutations of instance IDs are also valid solutions, the task requires learning of high-dimensional one-to-many mapping. As a result, state-of-the-art approaches use customized architectures and task-specific loss functions. We formulate panoptic segmentation as a discrete data generation problem, without relying on inductive bias of the task. A diffusion model based on analog bits is used to model panoptic masks, with a simple, generic architecture and loss function. By simply adding past predictions as a conditioning signal, our method is capable of modeling video (in a streaming setting) and thereby learns to track object instances automatically. With extensive experiments, we demonstrate that our generalist approach can perform competitively to state-of-the-art specialist methods in similar settings.
    Mixture of von Mises-Fisher distribution with sparse prototypes. (arXiv:2212.14591v1 [cs.LG])
    Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.
    Offline Policy Optimization in RL with Variance Regularizaton. (arXiv:2212.14405v1 [cs.LG])
    Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-estimation of value functions. In this work, we propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms. We show that the regularizer leads to a lower bound to the offline policy optimization objective, which can help avoid over-estimation errors, and explains the benefits of our approach across a range of continuous control domains when compared to existing state-of-the-art algorithms.
    Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion. (arXiv:2212.14595v1 [stat.ML])
    Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. It is therefore of paramount importance to quantify the uncertainties associated to the inversion process to ease the subsequent interpretation and decision making processes. Within this framework of reference, sampling from a target posterior provides a fundamental approach to quantifying the uncertainty in seismic inversion. However, selecting appropriate prior information in a probabilistic inversion is crucial, yet non-trivial, as it influences the ability of a sampling-based inference in providing geological realism in the posterior samples. To overcome such limitations, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a CNN-based denoiser by means of the Plug-and-Play methods. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability in producing high-resolution, trustworthy samples representative of the subsurface structures, which we argue could be used for post-inference tasks such as reservoir modelling and history matching. To validate the proposed method, numerical tests are performed on both synthetic and field post-stack seismic data.
    Bayesian Interpolation with Deep Linear Networks. (arXiv:2212.14457v1 [stat.ML])
    This article concerns Bayesian inference using deep linear networks with output dimension one. In the interpolating (zero noise) regime we show that with Gaussian weight priors and MSE negative log-likelihood loss both the predictive posterior and the Bayesian model evidence can be written in closed form in terms of a class of meromorphic special functions called Meijer-G functions. These results are non-asymptotic and hold for any training dataset, network depth, and hidden layer widths, giving exact solutions to Bayesian interpolation using a deep Gaussian process with a Euclidean covariance at each layer. Through novel asymptotic expansions of Meijer-G functions, a rich new picture of the role of depth emerges. Specifically, we find: ${\bf \text{The role of depth in extrapolation}}$: The posteriors in deep linear networks with data-independent priors are the same as in shallow networks with evidence maximizing data-dependent priors. In this sense, deep linear networks make provably optimal predictions. ${\bf \text{The role of depth in model selection}}$: Starting from data-agnostic priors, Bayesian model evidence in wide networks is only maximized at infinite depth. This gives a principled reason to prefer deeper networks (at least in the linear case). ${\bf \text{Scaling laws relating depth, width, and number of datapoints}}$: With data-agnostic priors, a novel notion of effective depth given by \[\#\text{hidden layers}\times\frac{\#\text{training data}}{\text{network width}}\] determines the Bayesian posterior in wide linear networks, giving rigorous new scaling laws for generalization error.
    On the utility of feature selection in building two-tier decision trees. (arXiv:2212.14448v1 [cs.LG])
    Nowadays, feature selection is frequently used in machine learning when there is a risk of performance degradation due to overfitting or when computational resources are limited. During the feature selection process, the subset of features that are most relevant and least redundant is chosen. In recent years, it has become clear that, in addition to relevance and redundancy, features' complementarity must be considered. Informally, if the features are weak predictors of the target variable separately and strong predictors when combined, then they are complementary. It is demonstrated in this paper that the synergistic effect of complementary features mutually amplifying each other in the construction of two-tier decision trees can be interfered with by another feature, resulting in a decrease in performance. It is demonstrated using cross-validation on both synthetic and real datasets, regression and classification, that removing or eliminating the interfering feature can improve performance by up to 24 times. It has also been discovered that the lesser the domain is learned, the greater the increase in performance. More formally, it is demonstrated that there is a statistically significant negative rank correlation between performance on the dataset prior to the elimination of the interfering feature and performance growth after the elimination of the interfering feature. It is concluded that this broadens the scope of feature selection methods for cases where data and computational resources are sufficient.
    Defense Against Adversarial Attacks on Audio DeepFake Detection. (arXiv:2212.14597v1 [cs.SD])
    Audio DeepFakes are artificially generated utterances created using deep learning methods with the main aim to fool the listeners, most of such audio is highly convincing. Their quality is sufficient to pose a serious threat in terms of security and privacy, such as the reliability of news or defamation. To prevent the threats, multiple neural networks-based methods to detect generated speech have been proposed. In this work, we cover the topic of adversarial attacks, which decrease the performance of detectors by adding superficial (difficult to spot by a human) changes to input data. Our contribution contains evaluating the robustness of 3 detection architectures against adversarial attacks in two scenarios (white-box and using transferability mechanism) and enhancing it later by the use of adversarial training performed by our novel adaptive training method.
    The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data. (arXiv:2212.14514v1 [stat.ML])
    We consider the problem of estimating a multivariate function $f_0$ of bounded variation (BV), from noisy observations $y_i = f_0(x_i) + z_i$ made at random design points $x_i \in \mathbb{R}^d$, $i=1,\ldots,n$. We study an estimator that forms the Voronoi diagram of the design points, and then solves an optimization problem that regularizes according to a certain discrete notion of total variation (TV): the sum of weighted absolute differences of parameters $\theta_i,\theta_j$ (which estimate the function values $f_0(x_i),f_0(x_j)$) at all neighboring cells $i,j$ in the Voronoi diagram. This is seen to be equivalent to a variational optimization problem that regularizes according to the usual continuum (measure-theoretic) notion of TV, once we restrict the domain to functions that are piecewise constant over the Voronoi diagram. The regression estimator under consideration hence performs (shrunken) local averaging over adaptively formed unions of Voronoi cells, and we refer to it as the Voronoigram, following the ideas in Koenker (2005), and drawing inspiration from Tukey's regressogram (Tukey, 1961). Our contributions in this paper span both the conceptual and theoretical frontiers: we discuss some of the unique properties of the Voronoigram in comparison to TV-regularized estimators that use other graph-based discretizations; we derive the asymptotic limit of the Voronoi TV functional; and we prove that the Voronoigram is minimax rate optimal (up to log factors) for estimating BV functions that are essentially bounded.
    Quantile Off-Policy Evaluation via Deep Conditional Generative Learning. (arXiv:2212.14466v1 [stat.ML])
    Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy. It is critical in a number of sequential decision making problems ranging from healthcare to technology industries. Most of the work in existing literature is focused on evaluating the mean outcome of a given policy, and ignores the variability of the outcome. However, in a variety of applications, criteria other than the mean may be more sensible. For example, when the reward distribution is skewed and asymmetric, quantile-based metrics are often preferred for their robustness. In this paper, we propose a doubly-robust inference procedure for quantile OPE in sequential decision making and study its asymptotic properties. In particular, we propose utilizing state-of-the-art deep conditional generative learning methods to handle parameter-dependent nuisance function estimation. We demonstrate the advantages of this proposed estimator through both simulations and a real-world dataset from a short-video platform. In particular, we find that our proposed estimator outperforms classical OPE estimators for the mean in settings with heavy-tailed reward distributions.
    Graph Federated Learning for CIoT Devices in Smart Home Applications. (arXiv:2212.14395v1 [cs.LG])
    This paper deals with the problem of statistical and system heterogeneity in a cross-silo Federated Learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel Graph Signal Processing (GSP)-inspired aggregation rule based on graph filtering dubbed ``G-Fedfilt''. The proposed aggregator enables a structured flow of information based on the graph's topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous trade-off between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional Federated Averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time-scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models' gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to $3.99\% $ better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local datasets, while it is capable of yielding up to $2.41\%$ higher accuracy than FedAvg in the case of testing the generalization of the models.
    GPT Takes the Bar Exam. (arXiv:2212.14402v1 [cs.CL])
    Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice. To even sit for the exam, most jurisdictions require that an applicant completes at least seven years of post-secondary education, including three years at an accredited law school. In addition, most test-takers also undergo weeks to months of further, exam-specific preparation. Despite this significant investment of time and capital, approximately one in five test-takers still score under the rate required to pass the exam on their first try. In the face of a complex task that requires such depth of knowledge, what, then, should we expect of the state of the art in "AI?" In this research, we document our experimental evaluation of the performance of OpenAI's `text-davinci-003` model, often-referred to as GPT-3.5, on the multistate multiple choice (MBE) section of the exam. While we find no benefit in fine-tuning over GPT-3.5's zero-shot performance at the scale of our training data, we do find that hyperparameter optimization and prompt engineering positively impacted GPT-3.5's zero-shot performance. For best prompt and parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete NCBE MBE practice exam, significantly in excess of the 25% baseline guessing rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5's ranking of responses is also highly-correlated with correctness; its top two and top three choices are correct 71% and 88% of the time, respectively, indicating very strong non-entailment performance. While our ability to interpret these results is limited by nascent scientific understanding of LLMs and the proprietary nature of GPT, we believe that these results strongly suggest that an LLM will pass the MBE component of the Bar Exam in the near future.
    Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks. (arXiv:2212.14115v1 [cs.LG])
    Function approximation has enabled remarkable advances in applying reinforcement learning (RL) techniques in environments with high-dimensional inputs, such as images, in an end-to-end fashion, mapping such inputs directly to low-level control. Nevertheless, these have proved vulnerable to small adversarial input perturbations. A number of approaches for improving or certifying robustness of end-to-end RL to adversarial perturbations have emerged as a result, focusing on cumulative reward. However, what is often at stake in adversarial scenarios is the violation of fundamental properties, such as safety, rather than the overall reward that combines safety with efficiency. Moreover, properties such as safety can only be defined with respect to true state, rather than the high-dimensional raw inputs to end-to-end policies. To disentangle nominal efficiency and adversarial safety, we situate RL in deterministic partially-observable Markov decision processes (POMDPs) with the goal of maximizing cumulative reward subject to safety constraints. We then propose a partially-supervised reinforcement learning (PSRL) framework that takes advantage of an additional assumption that the true state of the POMDP is known at training time. We present the first approach for certifying safety of PSRL policies under adversarial input perturbations, and two adversarial training approaches that make direct use of PSRL. Our experiments demonstrate both the efficacy of the proposed approach for certifying safety in adversarial environments, and the value of the PSRL framework coupled with adversarial training in improving certified safety while preserving high nominal reward and high-quality predictions of true state.
    Unsupervised construction of representations for oil wells via Transformers. (arXiv:2212.14246v1 [cs.LG])
    Determining and predicting reservoir formation properties for newly drilled wells represents a significant challenge. One of the variations of these properties evaluation is well-interval similarity. Many methodologies for similarity learning exist: from rule-based approaches to deep neural networks. Recently, articles adopted, e.g. recurrent neural networks to build a similarity model as we deal with sequential data. Such an approach suffers from short-term memory, as it pays more attention to the end of a sequence. Neural network with Transformer architecture instead cast their attention over all sequences to make a decision. To make them more efficient in terms of computational time, we introduce a limited attention mechanism similar to Informer and Performer architectures. We conduct experiments on open datasets with more than 20 wells making our experiments reliable and suitable for industrial usage. The best results were obtained with our adaptation of the Informer variant of Transformer with ROC AUC 0.982. It outperforms classical approaches with ROC AUC 0.824, Recurrent neural networks with ROC AUC 0.934 and straightforward usage of Transformers with ROC AUC 0.961.
    Finding Representative Group Fairness Metrics Using Correlation Estimations. (arXiv:2109.05697v2 [cs.LG] UPDATED)
    It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing unfairness. Practitioners and data scientists should be able to comprehend each metric and examine their impact on one another given the context, use case, and regulations. Exploring the combinatorial space of different metrics for such examination is burdensome. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that estimates the correlation among fairness notions. Our framework consequently identifies a set of diverse and semantically distinct metrics as representative for a given context. We propose a Monte-Carlo sampling technique for computing the correlations between fairness metrics by indirect and efficient perturbation in the model space. Using the estimated correlations, we then find a subset of representative metrics. The paper proposes a generic method that can be generalized to any arbitrary set of fairness metrics. We showcase the validity of the proposal using comprehensive experiments on real-world benchmark datasets.
    Translating Hanja Historical Documents to Contemporary Korean and English. (arXiv:2205.10019v4 [cs.CL] UPDATED)
    The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', and were translated into Korean from 1968 to 1993. The resulting translation was however too literal and contained many archaic Korean words; thus, a new expert translation effort began in 2012. Since then, the records of only one king have been completed in a decade. In parallel, expert translators are working on English translation, also at a slow pace and produced only one king's records in English so far. Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English. Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English. We compare our method against two baselines: a recent model that simultaneously learns to restore and translate Hanja historical document and a Transformer based model trained only on newly translated corpora. The experiments reveal that our method significantly outperforms the baselines in terms of BLEU scores for both contemporary Korean and English translations. We further conduct extensive human evaluation which shows that our translation is preferred over the original expert translations by both experts and non-expert Korean speakers.
    A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. (arXiv:2205.00403v2 [cs.LG] UPDATED)
    Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles. However their practicality in real-time, industrial-scale applications are limited due to the high memory and computational cost. Furthermore, ensembles and BNNs do not necessarily fix all the issues with the underlying member networks. In this work, we study principled approaches to improve uncertainty property of a single network, based on a single, deterministic representation. By formalizing the uncertainty quantification as a minimax learning problem, we first identify distance awareness, i.e., the model's ability to quantify the distance of a testing example from the training data, as a necessary condition for a DNN to achieve high-quality (i.e., minimax optimal) uncertainty estimation. We then propose Spectral-normalized Neural Gaussian Process (SNGP), a simple method that improves the distance-awareness ability of modern DNNs with two simple changes: (1) applying spectral normalization to hidden weights to enforce bi-Lipschitz smoothness in representations and (2) replacing the last output layer with a Gaussian process layer. On a suite of vision and language understanding benchmarks, SNGP outperforms other single-model approaches in prediction, calibration and out-of-domain detection. Furthermore, SNGP provides complementary benefits to popular techniques such as deep ensembles and data augmentation, making it a simple and scalable building block for probabilistic deep learning. Code is open-sourced at https://github.com/google/uncertainty-baselines
    Self-Attentive Pooling for Efficient Deep Learning. (arXiv:2209.07659v3 [cs.CV] UPDATED)
    Efficient custom pooling techniques that can aggressively trim the dimensions of a feature map and thereby reduce inference compute and memory footprint for resource-constrained computer vision applications have recently gained significant traction. However, prior pooling works extract only the local context of the activation maps, limiting their effectiveness. In contrast, we propose a novel non-local self-attentive pooling method that can be used as a drop-in replacement to the standard pooling layers, such as max/average pooling or strided convolution. The proposed self-attention module uses patch embedding, multi-head self-attention, and spatial-channel restoration, followed by sigmoid activation and exponential soft-max. This self-attention mechanism efficiently aggregates dependencies between non-local activation patches during down-sampling. Extensive experiments on standard object classification and detection tasks with various convolutional neural network (CNN) architectures demonstrate the superiority of our proposed mechanism over the state-of-the-art (SOTA) pooling techniques. In particular, we surpass the test accuracy of existing pooling techniques on different variants of MobileNet-V2 on ImageNet by an average of 1.2%. With the aggressive down-sampling of the activation maps in the initial layers (providing up to 22x reduction in memory consumption), our approach achieves 1.43% higher test accuracy compared to SOTA techniques with iso-memory footprints. This enables the deployment of our models in memory-constrained devices, such as micro-controllers (without losing significant accuracy), because the initial activation maps consume a significant amount of on-chip memory for high-resolution images required for complex vision tasks. Our proposed pooling method also leverages the idea of channel pruning to further reduce memory footprints.
    Out-Of-Distribution Generalization on Graphs: A Survey. (arXiv:2202.07987v2 [cs.LG] UPDATED)
    Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisfied in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the in-distribution hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. First, we provide a formal problem definition of OOD generalization on graphs. Second, we categorize existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy, based on their positions in the graph machine learning pipeline, followed by detailed discussions for each category. We also review the theories related to OOD generalization on graphs and introduce the commonly used graph datasets for thorough evaluations. Finally, we share our insights on future research directions. This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
    NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers. (arXiv:2207.13066v2 [cs.LG] UPDATED)
    Deep-learning (DL) compilers such as TVM and TensorRT are increasingly being used to optimize deep neural network (DNN) models to meet performance, resource utilization and other requirements. Bugs in these compilers can result in models whose semantics differ from the original ones, producing incorrect results that corrupt the correctness of downstream applications. However, finding bugs in these compilers is challenging due to their complexity. In this work, we propose a new fuzz testing approach for finding bugs in deep-learning compilers. Our core approach consists of (i) generating diverse yet valid DNN test models that can exercise a large part of the compiler's transformation logic using light-weight operator specifications; (ii) performing gradient-based search to find model inputs that avoid any floating-point exceptional values during model execution, reducing the chance of missed bugs or false alarms; and (iii) using differential testing to identify bugs. We implemented this approach in NNSmith which has found 72 new bugs for TVM, TensorRT, ONNXRuntime, and PyTorch to date. Of these 58 have been confirmed and 51 have been fixed by their respective project maintainers.
    Batchless Normalization: How to Normalize Activations with just one Instance in Memory. (arXiv:2212.14729v1 [cs.LG])
    In training neural networks, batch normalization has many benefits, not all of them entirely understood. But it also has some drawbacks. Foremost is arguably memory consumption, as computing the batch statistics requires all instances within the batch to be processed simultaneously, whereas without batch normalization it would be possible to process them one by one while accumulating the weight gradients. Another drawback is that that distribution parameters (mean and standard deviation) are unlike all other model parameters in that they are not trained using gradient descent but require special treatment, complicating implementation. In this paper, I show a simple and straightforward way to address these issues. The idea, in short, is to add terms to the loss that, for each activation, cause the minimization of the negative log likelihood of a Gaussian distribution that is used to normalize the activation. Among other benefits, this will hopefully contribute to the democratization of AI research by means of lowering the hardware requirements for training larger models.
    Discovering Useful Compact Sets of Sequential Rules in a Long Sequence. (arXiv:2109.07519v2 [cs.LG] UPDATED)
    We are interested in understanding the underlying generation process for long sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine small and meaningful sets of sequential rules. The rules are selected using an MDL-inspired criterion that favors compactness and relies on a novel rule-based encoding scheme for sequences. Our evaluation shows that COSSU can successfully retrieve relevant sets of closed sequential rules from a long sequence. Such rules constitute an interpretable model that exhibits competitive accuracy for the tasks of next-element prediction and classification.
    Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. (arXiv:2202.07511v3 [cs.LG] UPDATED)
    We study episodic two-player zero-sum Markov games (MGs) in the offline setting, where the goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset collected a priori. When the dataset does not have uniform coverage over all policy pairs, finding an approximate NE involves challenges in three aspects: (i) distributional shift between the behavior policy and the optimal policy, (ii) function approximation to handle large state space, and (iii) minimax optimization for equilibrium solving. We propose a pessimism-based algorithm, dubbed as pessimistic minimax value iteration (PMVI), which overcomes the distributional shift by constructing pessimistic estimates of the value functions for both players and outputs a policy pair by solving NEs based on the two value functions. Furthermore, we establish a data-dependent upper bound on the suboptimality which recovers a sublinear rate without the assumption on uniform coverage of the dataset. We also prove an information-theoretical lower bound, which suggests that the data-dependent term in the upper bound is intrinsic. Our theoretical results also highlight a notion of "relative uncertainty", which characterizes the necessary and sufficient condition for achieving sample efficiency in offline MGs. To the best of our knowledge, we provide the first nearly minimax optimal result for offline MGs with function approximation.
    CLNode: Curriculum Learning for Node Classification. (arXiv:2206.07258v2 [cs.LG] UPDATED)
    Node classification is a fundamental graph-based task that aims to predict the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods. Current GNNs assume that nodes in the training set contribute equally during training. However, the quality of training nodes varies greatly, and the performance of GNNs could be harmed by two types of low-quality training nodes: (1) inter-class nodes situated near class boundaries that lack the typical characteristics of their corresponding classes. Because GNNs are data-driven approaches, training on these nodes could degrade the accuracy. (2) mislabeled nodes. In real-world graphs, nodes are often mislabeled, which can significantly degrade the robustness of GNNs. To mitigate the detrimental effect of the low-quality training nodes, we present CLNode, which employs a selective training strategy to train GNN based on the quality of nodes. Specifically, we first design a multi-perspective difficulty measurer to accurately measure the quality of training nodes. Then, based on the measured qualities, we employ a training scheduler that selects appropriate training nodes to train GNN in each epoch. To evaluate the effectiveness of CLNode, we conduct extensive experiments by incorporating it in six representative backbone GNNs. Experimental results on real-world networks demonstrate that CLNode is a general framework that can be combined with various GNNs to improve their accuracy and robustness.
    Algorithmic decision making methods for fair credit scoring. (arXiv:2209.07912v2 [cs.LG] UPDATED)
    The utility of machine learning in evaluating the creditworthiness of loan applicants has been proofed since decades ago. However, automatic decisions may lead to different treatments over groups or individuals, potentially causing discrimination. This paper benchmarks 12 top bias mitigation methods discussing their performance based on 5 different fairness metrics, accuracy achieved, and potential profits for the financial institutions. Our findings show the difficulties in achieving fairness while preserving accuracy and profits. Additionally, it highlights some of the best and worst performers and helps bridging the gap between experimental machine learning and its industrial application.
    Reducing Certified Regression to Certified Classification for General Poisoning Attacks. (arXiv:2208.13904v2 [cs.LG] UPDATED)
    Adversarial training instances can severely distort a model's behavior. This work investigates certified regression defenses, which provide guaranteed limits on how much a regressor's prediction may change under a poisoning attack. Our key insight is that certified regression reduces to voting-based certified classification when using median as a model's primary decision function. Coupling our reduction with existing certified classifiers, we propose six new regressors provably-robust to poisoning attacks. To the extent of our knowledge, this is the first work that certifies the robustness of individual regression predictions without any assumptions about the data distribution and model architecture. We also show that the assumptions made by existing state-of-the-art certified classifiers are often overly pessimistic. We introduce a tighter analysis of model robustness, which in many cases results in significantly improved certified guarantees. Lastly, we empirically demonstrate our approaches' effectiveness on both regression and classification data, where the accuracy of up to 50% of test predictions can be guaranteed under 1% training set corruption and up to 30% of predictions under 4% corruption. Our source code is available at https://github.com/ZaydH/certified-regression.
    Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. (arXiv:2206.07837v3 [cs.LG] UPDATED)
    Recent empirical studies on domain generalization (DG) have shown that DG algorithms that perform well on some distribution shifts fail on others, and no state-of-the-art DG algorithm performs consistently well on all shifts. Moreover, real-world data often has multiple distribution shifts over different attributes; hence we introduce multi-attribute distribution shift datasets and find that the accuracy of existing DG algorithms falls even further. To explain these results, we provide a formal characterization of generalization under multi-attribute shifts using a canonical causal graph. Based on the relationship between spurious attributes and the classification label, we obtain realizations of the canonical causal graph that characterize common distribution shifts and show that each shift entails different independence constraints over observed variables. As a result, we prove that any algorithm based on a single, fixed constraint cannot work well across all shifts, providing theoretical evidence for mixed empirical results on DG algorithms. Based on this insight, we develop Causally Adaptive Constraint Minimization (CACM), an algorithm that uses knowledge about the data-generating process to adaptively identify and apply the correct independence constraints for regularization. Results on fully synthetic, MNIST, small NORB, and Waterbirds datasets, covering binary and multi-valued attributes and labels, show that adaptive dataset-dependent constraints lead to the highest accuracy on unseen domains whereas incorrect constraints fail to do so. Our results demonstrate the importance of modeling the causal relationships inherent in the data-generating process.
    Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective. (arXiv:2208.08342v3 [cs.IT] UPDATED)
    Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come from the additional freedom brought by the high-PAPR continuous-amplitude signal. In this paper, we address this question by exploring three PAPR reduction techniques in the application of image transmission. We confirm that the superior image reconstruction performance of DeepJSCC-based semantic communications can be retained while the transmitted PAPR is suppressed to an acceptable level. This observation is an important step towards the implementation of DeepJSCC in practical semantic communication systems.
    Git Re-Basin: Merging Models modulo Permutation Symmetries. (arXiv:2209.04836v5 [cs.LG] UPDATED)
    The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease. Though non-convex optimization is NP-hard, simple algorithms -- often variants of stochastic gradient descent -- exhibit surprising effectiveness in fitting large neural networks in practice. We argue that neural network loss landscapes contain (nearly) a single basin after accounting for all possible permutation symmetries of hidden units a la Entezari et al. (2021). We introduce three algorithms to permute the units of one model to bring them into alignment with a reference model in order to merge the two models in weight space. This transformation produces a functionally equivalent set of weights that lie in an approximately convex basin near the reference model. Experimentally, we demonstrate the single basin phenomenon across a variety of model architectures and datasets, including the first (to our knowledge) demonstration of zero-barrier linear mode connectivity between independently trained ResNet models on CIFAR-10 and CIFAR-100. Additionally, we investigate intriguing phenomena relating model width and training time to mode connectivity. Finally, we discuss shortcomings of the linear mode connectivity hypothesis, including a counterexample to the single basin theory.
    Variationally Mimetic Operator Networks. (arXiv:2209.12871v2 [math.NA] UPDATED)
    In recent years operator networks have emerged as promising deep learning tools for approximating the solution to partial differential equations (PDEs). These networks map input functions that describe material properties, forcing functions and boundary data to the solution of a PDE. This work describes a new architecture for operator networks that mimics the form of the numerical solution obtained from an approximate variational or weak formulation of the problem. The application of these ideas to a generic elliptic PDE leads to a variationally mimetic operator network (VarMiON). Like the conventional Deep Operator Network (DeepONet) the VarMiON is also composed of a sub-network that constructs the basis functions for the output and another that constructs the coefficients for these basis functions. However, in contrast to the DeepONet, the architecture of these sub-networks in the VarMiON is precisely determined. An analysis of the error in the VarMiON solution reveals that it contains contributions from the error in the training data, the training error, the quadrature error in sampling input and output functions, and a "covering error" that measures the distance between the test input functions and the nearest functions in the training dataset. It also depends on the stability constants for the exact solution operator and its VarMiON approximation. The application of the VarMiON to a canonical elliptic PDE reveals that for approximately the same number of network parameters, on average the VarMiON incurs smaller errors than a standard DeepONet. Further, its performance is more robust to variations in input functions, the techniques used to sample the input and output functions, the techniques used to construct the basis functions, and the number of input functions.
    A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis. (arXiv:2201.07646v2 [cs.LG] UPDATED)
    In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.
    NISQ-ready community detection based on separation-node identification. (arXiv:2212.14717v1 [quant-ph])
    The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.
    Decoupled Self-supervised Learning for Graphs. (arXiv:2206.03601v2 [cs.LG] UPDATED)
    This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is homophilous, where linked nodes often belong to the same class or have similar features. However, such assumptions of homophily do not always hold in real-world graphs. We address this problem by developing a decoupled self-supervised learning (DSSL) framework for graph neural networks. DSSL imitates a generative process of nodes and links from latent variable modeling of the semantic structure, which decouples different underlying semantics between different neighborhoods into the self-supervised learning process. Our DSSL framework is agnostic to the encoders and does not need prefabricated augmentations, thus is flexible to different graphs. To effectively optimize the framework, we derive the evidence lower bound of the self-supervised objective and develop a scalable training algorithm with variational inference. We provide a theoretical analysis to justify that DSSL enjoys the better downstream performance. Extensive experiments on various types of graph benchmarks demonstrate that our proposed framework can achieve better performance compared with competitive baselines.
    Learning Representations from Dendrograms. (arXiv:1812.09225v4 [cs.LG] UPDATED)
    We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different distance measures and representations can be inferred from different types of dendrograms, level functions and distance functions. Via an appropriate embedding, we compute a vector-based representation of the inferred distances, in order to enable many numerical machine learning algorithms to employ such distances. Then, to address the model selection problem, we study the aggregation of different dendrogram-based distances respectively in solution space and in representation space in the spirit of deep representations. In the first approach, for example for the clustering problem, we build a graph with positive and negative edge weights according to the consistency of the clustering labels of different objects among different solutions, in the context of ensemble methods. Then, we use an efficient variant of correlation clustering to produce the final clusters. In the second approach, we investigate the combination of different distances and features sequentially in the spirit of multi-layered architectures to obtain the final features. Finally, we demonstrate the effectiveness of our approach via several numerical studies.
    The Stable Artist: Steering Semantics in Diffusion Latent Space. (arXiv:2212.06013v2 [cs.CV] UPDATED)
    Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone. However, achieving high-quality results is almost unfeasible in a one-shot fashion. On the contrary, text-guided image generation involves the user making many slight changes to inputs in order to iteratively carve out the envisioned image. However, slight changes to the input prompt often lead to entirely different images being generated, and thus the control of the artist is limited in its granularity. To provide flexibility, we present the Stable Artist, an image editing approach enabling fine-grained control of the image generation process. The main component is semantic guidance (SEGA) which steers the diffusion process along variable numbers of semantic directions. This allows for subtle edits to images, changes in composition and style, as well as optimization of the overall artistic conception. Furthermore, SEGA enables probing of latent spaces to gain insights into the representation of concepts learned by the model, even complex ones such as 'carbon emission'. We demonstrate the Stable Artist on several tasks, showcasing high-quality image editing and composition.
    Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap. (arXiv:2201.04469v8 [stat.ML] UPDATED)
    We consider fixed-budget best-arm identification in two-armed Gaussian bandit problems. One of the longstanding open questions is the existence of an optimal strategy under which the probability of misidentification matches a lower bound. We show that a strategy following the Neyman allocation rule (Neyman, 1934) is asymptotically optimal when the gap between the expected rewards is small. First, we review a lower bound derived by Kaufmann et al. (2016). Then, we propose the "Neyman Allocation (NA)-Augmented Inverse Probability weighting (AIPW)" strategy, which consists of the sampling rule using the Neyman allocation with an estimated standard deviation and the recommendation rule using an AIPW estimator. Our proposed strategy is optimal because the upper bound matches the lower bound when the budget goes to infinity and the gap goes to zero.
    Active Learning Through a Covering Lens. (arXiv:2205.11320v3 [cs.LG] UPDATED)
    Deep active learning aims to reduce the annotation cost for the training of deep models, which is notoriously data-hungry. Until recently, deep active learning methods were ineffectual in the low-budget regime, where only a small number of examples are annotated. The situation has been alleviated by recent advances in representation and self-supervised learning, which impart the geometry of the data representation with rich information about the points. Taking advantage of this progress, we study the problem of subset selection for annotation through a "covering" lens, proposing ProbCover - a new active learning algorithm for the low budget regime, which seeks to maximize Probability Coverage. We then describe a dual way to view the proposed formulation, from which one can derive strategies suitable for the high budget regime of active learning, related to existing methods like Coreset. We conclude with extensive experiments, evaluating ProbCover in the low-budget regime. We show that our principled active learning strategy improves the state-of-the-art in the low-budget regime in several image recognition benchmarks. This method is especially beneficial in the semi-supervised setting, allowing state-of-the-art semi-supervised methods to match the performance of fully supervised methods, while using much fewer labels nonetheless. Code is available at https://github.com/avihu111/TypiClust.
    Shift of Pairwise Similarities for Data Clustering. (arXiv:2110.13103v2 [cs.LG] UPDATED)
    Several clustering methods (e.g., Normalized Cut and Ratio Cut) divide the Min Cut cost function by a cluster dependent factor (e.g., the size or the degree of the clusters), in order to yield a more balanced partitioning. We, instead, investigate adding such regularizations to the original cost function. We first consider the case where the regularization term is the sum of the squared size of the clusters, and then generalize it to adaptive regularization of the pairwise similarities. This leads to shifting (adaptively) the pairwise similarities which might make some of them negative. We then study the connection of this method to Correlation Clustering and then propose an efficient local search optimization algorithm with fast theoretical convergence rate to solve the new clustering problem. In the following, we investigate the shift of pairwise similarities on some common clustering methods, and finally, we demonstrate the superior performance of the method by extensive experiments on different datasets.
    Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures. (arXiv:2208.13585v2 [cs.LG] UPDATED)
    This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative physical location of different measurement stations to improve local wind forecasts. Our multi-step forecasting models produce either 10-minute, 1- or 4-hour forecasts, with 10-minute resolution, meaning that the models produce more informative time series for predicted future trends. A graph neural network (GNN) architecture was used to extract spatial dependencies, with different update functions to learn temporal correlations. These update functions were implemented using different neural network architectures. One such architecture, the Transformer, has become increasingly popular for sequence modelling in recent years. Various alterations have been proposed to better facilitate time series forecasting, of which this study focused on the Informer, LogSparse Transformer and Autoformer. This is the first time the LogSparse Transformer and Autoformer have been applied to wind forecasting and the first time any of these or the Informer have been formulated in a spatio-temporal setting for wind forecasting. By comparing against spatio-temporal Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) models, the study showed that the models using the altered Transformer architectures as update functions in GNNs were able to outperform these. Furthermore, we propose the Fast Fourier Transformer (FFTransformer), which is a novel Transformer architecture based on signal decomposition and consists of two separate streams that analyse the trend and periodic components separately. The FFTransformer and Autoformer were found to achieve superior results for the 10-minute and 1-hour ahead forecasts, with the FFTransformer significantly outperforming all other models for the 4-hour ahead forecasts.
    Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions. (arXiv:2209.04142v3 [cs.LG] UPDATED)
    Policy makers need to predict the progression of an outcome before adopting a new treatment policy, which defines when and how a sequence of treatments affecting the outcome occurs in continuous time. Commonly, algorithms that predict interventional future outcome trajectories take a fixed sequence of future treatments as input. This either neglects the dependence of future treatments on outcomes preceding them or implicitly assumes the treatment policy is known, and hence excludes scenarios where the policy is unknown or a counterfactual analysis is needed. To handle these limitations, we develop a joint model for treatments and outcomes, which allows for the estimation of treatment policies and effects from sequential treatment--outcome data. It can answer interventional and counterfactual queries about interventions on treatment policies, as we show with real-world data on blood glucose progression and a simulation study building on top of this.
    SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals. (arXiv:2206.06434v2 [cs.LG] UPDATED)
    A multitude of studies have been conducted on graph drawing, but many existing methods only focus on optimizing a single aesthetic aspect of graph layouts. There are a few existing methods that attempt to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria. Furthermore, thanks to the significant advance in deep learning techniques, several deep learning-based layout methods were proposed recently, which have demonstrated the advantages of the deep learning approaches for graph drawing. However, none of these existing methods can be directly applied to optimizing non-differentiable criteria without special accommodation. In this work, we propose a novel Generative Adversarial Network (GAN) based deep learning framework for graph drawing, called SmartGD, which can optimize any quantitative aesthetic goals even though they are non-differentiable. In the cases where the aesthetic goal is too abstract to be described mathematically, SmartGD can draw graphs in a similar style as a collection of good layout examples, which might be selected by humans based on the abstract aesthetic goal. To demonstrate the effectiveness and efficiency of SmartGD, we conduct experiments on minimizing stress, minimizing edge crossing, maximizing crossing angle, and a combination of multiple aesthetics. Compared with several popular graph drawing algorithms, the experimental results show that SmartGD achieves good performance both quantitatively and qualitatively.
    Experimental verification of the quantum nature of a neural network. (arXiv:2209.07577v2 [cs.NE] UPDATED)
    In my previous article I mentioned for the first time that a classical neural network may have quantum properties as its own structure may be entangled. The question one may ask now is whether such a quantum property can be used to entangle other systems? The answer should be yes, as shown in what follows.
    GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games. (arXiv:2201.12380v5 [cs.LG] UPDATED)
    Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to compute feature importance towards model predictions on images, text, tabular data, and recently graph neural networks (GNNs) on graphs. In this work, we revisit the appropriateness of the Shapley value for GNN explanation, where the task is to identify the most important subgraph and constituent nodes for GNN predictions. We claim that the Shapley value is a non-ideal choice for graph data because it is by definition not structure-aware. We propose a Graph Structure-aware eXplanation (GStarX) method to leverage the critical graph structure information to improve the explanation. Specifically, we define a scoring function based on a new structure-aware value from the cooperative game theory proposed by Hamiache and Navarro (HN). When used to score node importance, the HN value utilizes graph structures to attribute cooperation surplus between neighbor nodes, resembling message passing in GNNs, so that node importance scores reflect not only the node feature importance, but also the node structural roles. We demonstrate that GStarX produces qualitatively more intuitive explanations, and quantitatively improves explanation fidelity over strong baselines on chemical graph property prediction and text graph sentiment classification.
    Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data. (arXiv:2204.02973v2 [cs.LG] UPDATED)
    Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume all of the views are complete. However, in real applications, the multi-view data are often incomplete, i.e., some views of instances are missing, which will result in the failure of these methods. Besides, while the data arrive in form of streams, these existing methods will suffer the issues of high storage cost and expensive computation time. To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view streaming data. By jointly considering the consistent and complementary information across different views, I$^2$MUFS embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model, which can learn a consensus clustering indicator matrix and fuse different latent feature matrices with adaptive view weights. Furthermore, we introduce the incremental leaning mechanisms to develop an alternative iterative algorithm, where the feature selection matrix is incrementally updated, rather than recomputing on the entire updated data from scratch. A series of experiments are conducted to verify the effectiveness of the proposed method by comparing with several state-of-the-art methods. The experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of the clustering metrics and the computational cost.
    Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon. (arXiv:2008.11852v2 [cs.AI] UPDATED)
    Decision-making strategy for autonomous vehicles de-scribes a sequence of driving maneuvers to achieve a certain navigational mission. This paper utilizes the deep reinforcement learning (DRL) method to address the continuous-horizon decision-making problem on the highway. First, the vehicle kinematics and driving scenario on the freeway are introduced. The running objective of the ego automated vehicle is to execute an efficient and smooth policy without collision. Then, the particular algorithm named proximal policy optimization (PPO)-enhanced DRL is illustrated. To overcome the challenges in tardy training efficiency and sample inefficiency, this applied algorithm could realize high learning efficiency and excellent control performance. Finally, the PPO-DRL-based decision-making strategy is estimated from multiple perspectives, including the optimality, learning efficiency, and adaptability. Its potential for online application is discussed by applying it to similar driving scenarios.
    Optimal Decision Making in High-Throughput Virtual Screening Pipelines. (arXiv:2109.11683v2 [math.OC] UPDATED)
    The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the large size of the search space containing the candidates and the substantial computational cost of high-fidelity property prediction models makes screening practically challenging. In this work, we propose a general framework for constructing and optimizing a virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allocate the computational resources to models with varying costs and accuracy to optimize the return-on-computational-investment (ROCI). Based on both simulated as well as real data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate screening virtually without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.
    MindBigData 2022 A Large Dataset of Brain Signals. (arXiv:2212.14746v1 [eess.SP])
    Understanding our brain is one of the most daunting tasks, one we cannot expect to complete without the use of technology. MindBigData aims to provide a comprehensive and updated dataset of brain signals related to a diverse set of human activities so it can inspire the use of machine learning algorithms as a benchmark of 'decoding' performance from raw brain activities into its corresponding (labels) mental (or physical) tasks. Using commercial of the self, EEG devices or custom ones built by us to explore the limits of the technology. We describe the data collection procedures for each of the sub datasets and with every headset used to capture them. Also, we report possible applications in the field of Brain Computer Interfaces or BCI that could impact the life of billions, in almost every sector like healthcare game changing use cases, industry or entertainment to name a few, at the end why not directly using our brains to 'disintermediate' senses, as the final HCI (Human-Computer Interaction) device? simply what we call the journey from Type to Touch to Talk to Think.
    Long-Tailed Learning Requires Feature Learning. (arXiv:2205.14553v3 [cs.LG] UPDATED)
    We propose a simple data model inspired from natural data such as text or images, and use it to study the importance of learning features in order to achieve good generalization. Our data model follows a long-tailed distribution in the sense that some rare subcategories have few representatives in the training set. In this context we provide evidence that a learner succeeds if and only if it identifies the correct features, and moreover derive non-asymptotic generalization error bounds that precisely quantify the penalty that one must pay for not learning features.
    On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method. (arXiv:2206.14796v2 [cs.CL] UPDATED)
    Most works on modeling the conversation history in Conversational Question Answering (CQA) report a single main result on a common CQA benchmark. While existing models show impressive results on CQA leaderboards, it remains unclear whether they are robust to shifts in setting (sometimes to more realistic ones), training data size (e.g. from large to small sets) and domain. In this work, we design and conduct the first large-scale robustness study of history modeling approaches for CQA. We find that high benchmark scores do not necessarily translate to strong robustness, and that various methods can perform extremely differently under different settings. Equipped with the insights from our study, we design a novel prompt-based history modeling approach, and demonstrate its strong robustness across various settings. Our approach is inspired by existing methods that highlight historic answers in the passage. However, instead of highlighting by modifying the passage token embeddings, we add textual prompts directly in the passage text. Our approach is simple, easy-to-plug into practically any model, and highly effective, thus we recommend it as a starting point for future model developers. We also hope that our study and insights will raise awareness to the importance of robustness-focused evaluation, in addition to obtaining high leaderboard scores, leading to better CQA systems.
    Detecting Network-based Internet Censorship via Latent Feature Representation Learning. (arXiv:2209.05152v3 [cs.LG] UPDATED)
    Internet censorship is a phenomenon of societal importance and attracts investigation from multiple disciplines. Several research groups, such as Censored Planet, have deployed large scale Internet measurement platforms to collect network reachability data. However, existing studies generally rely on manually designed rules (i.e., using censorship fingerprints) to detect network-based Internet censorship from the data. While this rule-based approach yields a high true positive detection rate, it suffers from several challenges: it requires human expertise, is laborious, and cannot detect any censorship not captured by the rules. Seeking to overcome these challenges, we design and evaluate a classification model based on latent feature representation learning and an image-based classification model to detect network-based Internet censorship. To infer latent feature representations fromnetwork reachability data, we propose a sequence-to-sequence autoencoder to capture the structure and the order of data elements in the data. To estimate the probability of censorship events from the inferred latent features, we rely on a densely connected multi-layer neural network model. Our image-based classification model encodes a network reachability data record as a gray-scale image and classifies the image as censored or not using a dense convolutional neural network. We compare and evaluate both approaches using data sets from Censored Planet via a hold-out evaluation. Both classification models are capable of detecting network-based Internet censorship as we were able to identify instances of censorship not detected by the known fingerprints. Latent feature representations likely encode more nuances in the data since the latent feature learning approach discovers a greater quantity, and a more diverse set, of new censorship instances.
    Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling. (arXiv:2212.14760v1 [cs.LG])
    Unlike traditional distributed machine learning, federated learning stores data locally for training and then aggregates the models on the server, which solves the data security problem that may arise in traditional distributed machine learning. However, during the training process, the transmission of model parameters can impose a significant load on the network bandwidth. It has been pointed out that the vast majority of model parameters are redundant during model parameter transmission. In this paper, we explore the data distribution law of selected partial model parameters on this basis, and propose a deep hierarchical quantization compression algorithm, which further compresses the model and reduces the network load brought by data transmission through the hierarchical quantization of model parameters. And we adopt a dynamic sampling strategy for the selection of clients to accelerate the convergence of the model. Experimental results on different public datasets demonstrate the effectiveness of our algorithm.
    Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning. (arXiv:2110.07276v3 [cs.LG] UPDATED)
    Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forgetting, prior studies exploit episodic memory (EM), which stores a subset of the past observed samples while learning from new non-i.i.d. data. Despite the promising results, since CL is often assumed to execute on mobile or IoT devices, the EM size is bounded by the small hardware memory capacity and makes it infeasible to meet the accuracy requirements for real-world applications. Specifically, all prior CL methods discard samples overflowed from the EM and can never retrieve them back for subsequent training steps, incurring loss of information that would exacerbate catastrophic forgetting. We explore a novel hierarchical EM management strategy to address the forgetting issue. In particular, in mobile and IoT devices, real-time data can be stored not just in high-speed RAMs but in internal storage devices as well, which offer significantly larger capacity than the RAMs. Based on this insight, we propose to exploit the abundant storage to preserve past experiences and alleviate the forgetting by allowing CL to efficiently migrate samples between memory and storage without being interfered by the slow access speed of the storage. We call it Carousel Memory (CarM). As CarM is complementary to existing CL methods, we conduct extensive evaluations of our method with seven popular CL methods and show that CarM significantly improves the accuracy of the methods across different settings by large margins in final average accuracy (up to 28.4%) while retaining the same training efficiency.
    Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study. (arXiv:2012.15772v2 [eess.IV] UPDATED)
    Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. Methods: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. Results: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31--48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75--78% of all images). Conclusion: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. Significance: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review.
    Comparative Analysis of Clustering Techniques for Personalized Food Kit Distribution. (arXiv:2212.14874v1 [cs.LG])
    The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.
    DeLag: Using Multi-Objective Optimization to Enhance the Detection of Latency Degradation Patterns in Service-based Systems. (arXiv:2110.11155v3 [cs.SE] UPDATED)
    Performance debugging in production is a fundamental activity in modern service-based systems. The diagnosis of performance issues is often time-consuming, since it requires thorough inspection of large volumes of traces and performance indices. In this paper we present DeLag, a novel automated search-based approach for diagnosing performance issues in service-based systems. DeLag identifies subsets of requests that show, in the combination of their Remote Procedure Call execution times, symptoms of potentially relevant performance issues. We call such symptoms Latency Degradation Patterns. DeLag simultaneously searches for multiple latency degradation patterns while optimizing precision, recall and latency dissimilarity. Experimentation on 700 datasets of requests generated from two microservice-based systems shows that our approach provides better and more stable effectiveness than three state-of-the-art approaches and general purpose machine learning clustering algorithms. DeLag is more effective than all baseline techniques in at least one case study (with p $\leq$ 0.05 and non-negligible effect size). Moreover, DeLag outperforms in terms of efficiency the second and the third most effective baseline techniques on the largest datasets used in our evaluation (up to 22%).
    An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models. (arXiv:2212.14852v1 [cs.LG])
    With the attention mechanism, transformers achieve significant empirical successes. Despite the intuitive understanding that transformers perform relational inference over long sequences to produce desirable representations, we lack a rigorous theory on how the attention mechanism achieves it. In particular, several intriguing questions remain open: (a) What makes a desirable representation? (b) How does the attention mechanism infer the desirable representation within the forward pass? (c) How does a pretraining procedure learn to infer the desirable representation through the backward pass? We observe that, as is the case in BERT and ViT, input tokens are often exchangeable since they already include positional encodings. The notion of exchangeability induces a latent variable model that is invariant to input sizes, which enables our theoretical analysis. - To answer (a) on representation, we establish the existence of a sufficient and minimal representation of input tokens. In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks. - To answer (b) on inference, we prove that attention with the desired parameter infers the latent posterior up to an approximation error, which is decreasing in input sizes. In detail, we quantify how attention approximates the conditional mean of the value given the key, which characterizes how it performs relational inference over long sequences. - To answer (c) on learning, we prove that both supervised and self-supervised objectives allow empirical risk minimization to learn the desired parameter up to a generalization error, which is independent of input sizes. Particularly, in the self-supervised setting, we identify a condition number that is pivotal to solving downstream tasks.
    On Machine Learning Knowledge Representation In The Form Of Partially Unitary Operator. Knowledge Generalizing Operator. (arXiv:2212.14810v1 [cs.LG])
    A new form of ML knowledge representation with high generalization power is developed and implemented numerically. Initial $\mathit{IN}$ attributes and $\mathit{OUT}$ class label are transformed into the corresponding Hilbert spaces by considering localized wavefunctions. A partially unitary operator optimally converting a state from $\mathit{IN}$ Hilbert space into $\mathit{OUT}$ Hilbert space is then built from an optimization problem of transferring maximal possible probability from $\mathit{IN}$ to $\mathit{OUT}$, this leads to the formulation of a new algebraic problem. Constructed Knowledge Generalizing Operator $\mathcal{U}$ can be considered as a $\mathit{IN}$ to $\mathit{OUT}$ quantum channel; it is a partially unitary rectangular matrix of the dimension $\mathrm{dim}(\mathit{OUT}) \times \mathrm{dim}(\mathit{IN})$ transforming operators as $A^{\mathit{OUT}}=\mathcal{U} A^{\mathit{IN}} \mathcal{U}^{\dagger}$. Whereas only operator $\mathcal{U}$ projections squared are observable $\left\langle\mathit{OUT}|\mathcal{U}|\mathit{IN}\right\rangle^2$ (probabilities), the fundamental equation is formulated for the operator $\mathcal{U}$ itself. This is the reason of high generalizing power of the approach; the situation is the same as for the Schr\"{o}dinger equation: we can only measure $\psi^2$, but the equation is written for $\psi$ itself.
    A deep real options policy for sequential service region design and timing. (arXiv:2212.14800v1 [cs.LG])
    As various city agencies and mobility operators navigate toward innovative mobility solutions, there is a need for strategic flexibility in well-timed investment decisions in the design and timing of mobility service regions, i.e. cast as "real options" (RO). This problem becomes increasingly challenging with multiple interacting RO in such investments. We propose a scalable machine learning based RO framework for multi-period sequential service region design & timing problem for mobility-on-demand services, framed as a Markov decision process with non-stationary stochastic variables. A value function approximation policy from literature uses multi-option least squares Monte Carlo simulation to get a policy value for a set of interdependent investment decisions as deferral options (CR policy). The goal is to determine the optimal selection and timing of a set of zones to include in a service region. However, prior work required explicit enumeration of all possible sequences of investments. To address the combinatorial complexity of such enumeration, we propose a new variant "deep" RO policy using an efficient recurrent neural network (RNN) based ML method (CR-RNN policy) to sample sequences to forego the need for enumeration, making network design & timing policy tractable for large scale implementation. Experiments on multiple service region scenarios in New York City (NYC) shows the proposed policy substantially reduces the overall computational cost (time reduction for RO evaluation of > 90% of total investment sequences is achieved), with zero to near-zero gap compared to the benchmark. A case study of sequential service region design for expansion of MoD services in Brooklyn, NYC show that using the CR-RNN policy to determine optimal RO investment strategy yields a similar performance (0.5% within CR policy value) with significantly reduced computation time (about 5.4 times faster).
    Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent. (arXiv:2212.14883v1 [stat.ML])
    With the fast development of big data, it has been easier than before to learn the optimal decision rule by updating the decision rule recursively and making online decisions. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes of the stochastic gradient and establish the asymptotic normality of the parameter estimator. Our proposed estimator significantly improves the asymptotic efficiency over the previous averaged SGD approach via inverse probability weights. We also conduct an optimality analysis on the weights in a linear regression setting. We provide a Bahadur representation of the proposed estimator and show that the remainder term in the Bahadur representation entails a slower convergence rate compared to classical SGD due to the adaptive data collection.
    The Feasibility and Inevitability of Stealth Attacks. (arXiv:2106.13997v3 [cs.CR] UPDATED)
    We develop and study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence (AI) systems including deep learning neural networks. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself. Such a stealth attack could be conducted by a mischievous, corrupt or disgruntled member of a software development team. It could also be made by those wishing to exploit a ``democratization of AI'' agenda, where network architectures and trained parameter sets are shared publicly. We develop a range of new implementable attack strategies with accompanying analysis, showing that with high probability a stealth attack can be made transparent, in the sense that system performance is unchanged on a fixed validation set which is unknown to the attacker, while evoking any desired output on a trigger input of interest. The attacker only needs to have estimates of the size of the validation set and the spread of the AI's relevant latent space. In the case of deep learning neural networks, we show that a one neuron attack is possible - a modification to the weights and bias associated with a single neuron - revealing a vulnerability arising from over-parameterization. We illustrate these concepts using state of the art architectures on two standard image data sets. Guided by the theory and computational results, we also propose strategies to guard against stealth attacks.
    A Unified Framework for Online Trip Destination Prediction. (arXiv:2101.04520v2 [cs.LG] UPDATED)
    Trip destination prediction is an area of increasing importance in many applications such as trip planning, autonomous driving and electric vehicles. Even though this problem could be naturally addressed in an online learning paradigm where data is arriving in a sequential fashion, the majority of research has rather considered the offline setting. In this paper, we present a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction. For this purpose, we develop two clustering algorithms and integrate them within two online prediction models for this problem. We investigate the different configurations of clustering algorithms and prediction models on a real-world dataset. We demonstrate that both the clustering and the entire framework yield consistent results compared to the offline setting. Finally, we propose a novel regret metric for evaluating the entire online framework in comparison to its offline counterpart. This metric makes it possible to relate the source of erroneous predictions to either the clustering or the prediction model. Using this metric, we show that the proposed methods converge to a probability distribution resembling the true underlying distribution with a lower regret than all of the baselines.
    Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics. (arXiv:2108.11939v2 [cs.LG] UPDATED)
    This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in-depth interpretation. NAS has been explosively studied to automate the discovery of top-performer neural networks, but suffers from heavy resource consumption and often incurs search bias due to truncated training or approximations. Recent NAS works start to explore indicators that can predict a network's performance without training. However, they either leveraged limited properties of deep networks, or the benefits of their training-free indicators are not applied to more extensive search methods. By rigorous correlation analysis, we present a unified framework to understand and accelerate NAS, by disentangling "TEG" characteristics of searched networks - Trainability, Expressivity, Generalization - all assessed in a training-free manner. The TEG indicators could be scaled up and integrated with various NAS search methods, including both supernet and single-path approaches. Extensive studies validate the effective and efficient guidance from our TEG-NAS framework, leading to both improved search accuracy and over 56% reduction in search time cost. Moreover, we visualize search trajectories on three landscapes of "TEG" characteristics, observing that while a good local minimum is easier to find on NAS-Bench-201 given its simple topology, balancing "TEG" characteristics is much harder on the DARTS search space due to its complex landscape geometry. Our code is available at https://github.com/VITA-Group/TEGNAS.
    The Improvement of Decision Tree Construction Algorithm Based On Quantum Heuristic Algorithms. (arXiv:2212.14725v1 [quant-ph])
    This work is related to the implementation of a decision tree construction algorithm on a quantum simulator. Here we consider an algorithm based on a binary criterion. Also, we study the improvement capability with quantum heuristic QAOA. We implemented the classical and the quantum version of this algorithm to compare built trees.
    Personalized Student Attribute Inference. (arXiv:2212.14682v1 [cs.CY])
    Accurately predicting their future performance can ensure students successful graduation, and help them save both time and money. However, achieving such predictions faces two challenges, mainly due to the diversity of students' background and the necessity of continuously tracking their evolving progress. The goal of this work is to create a system able to automatically detect students in difficulty, for instance predicting if they are likely to fail a course. We compare a naive approach widely used in the literature, which uses attributes available in the data set (like the grades), with a personalized approach we called Personalized Student Attribute Inference (PSAI). With our model, we create personalized attributes to capture the specific background of each student. Both approaches are compared using machine learning algorithms like decision trees, support vector machine or neural networks.
    Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping. (arXiv:2212.14807v1 [quant-ph])
    In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which achieve extraordinary performance in a wide range of AI tasks as massively parameterized function approximators. VQCs have already demonstrated promising results, for example, in generalization and the requirement for fewer parameters to train, by utilizing the more robust algorithmic toolbox available in quantum computing. A VQCs' trainable parameters or weights are usually used as angles in rotational gates and current gradient-based training methods do not account for that. We introduce weight re-mapping for VQCs, to unambiguously map the weights to an interval of length $2\pi$, drawing inspiration from traditional ML, where data rescaling, or normalization techniques have demonstrated tremendous benefits in many circumstances. We employ a set of five functions and evaluate them on the Iris and Wine datasets using variational classifiers as an example. Our experiments show that weight re-mapping can improve convergence in all tested settings. Additionally, we were able to demonstrate that weight re-mapping increased test accuracy for the Wine dataset by $10\%$ over using unmodified weights.
    Lab-scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning. (arXiv:2212.14732v1 [eess.SP])
    The monitoring of machine conditions in a plant is crucial for production in manufacturing. A sudden failure of a machine can stop production and cause a loss of revenue. The vibration signal of a machine is a good indicator of its condition. This paper presents a dataset of vibration signals from a lab-scale machine. The dataset contains four different types of machine conditions: normal, unbalance, misalignment, and bearing fault. Three machine learning methods (SVM, KNN, and GNB) evaluated the dataset, and a perfect result was obtained by one of the methods on a 1-fold test. The performance of the algorithms is evaluated using weighted accuracy (WA) since the data is balanced. The results show that the best-performing algorithm is the SVM with a WA of 99.75\% on the 5-fold cross-validations. The dataset is provided in the form of CSV files in an open and free repository at https://zenodo.org/record/7006575.
    On Biased Compression for Distributed Learning. (arXiv:2002.12410v3 [cs.LG] UPDATED)
    In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact {\em biased} compressors often show superior performance in practice when compared to the much more studied and understood {\em unbiased} compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. We prove that distributed compressed SGD method, employed with error feedback mechanism, enjoys the ergodic rate $\mathcal{O}\left( \delta L \exp[-\frac{\mu K}{\delta L}] + \frac{(C + \delta D)}{K\mu}\right)$, where $\delta\ge1$ is a compression parameter which grows when more compression is applied, $L$ and $\mu$ are the smoothness and strong convexity constants, $C$ captures stochastic gradient noise ($C=0$ if full gradients are computed on each node) and $D$ captures the variance of the gradients at the optimum ($D=0$ for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose several new biased compressors with promising theoretical guarantees and practical performance.
    Cross-Domain Shopping and Stock Trend Analysis. (arXiv:2212.14689v1 [q-fin.ST])
    This paper presents a cross-domain trend analysis that aims to identify and analyze the relationships between stock prices, stock news on Twitter, and users' behaviors on e-commerce websites. The analysis is based on three datasets: a US stock dataset, a stock tweets dataset, and an e-commerce behavior dataset. The analysis is performed using Hadoop, Hive, and Tableau, allowing for efficient and scalable processing and visualizing large datasets. The analysis includes trend analysis of Twitter sentiment (positive and negative tweets) and correlation analysis, including the correlation between tweet sentiment and stocks, the correlation between stock trends and shopping behavior, and the understanding of data based on different slices of time. By comparing different features from the datasets over time, we hope to gain insight into the factors that drive user behavior as well as the market in different categories. The results of this analysis can provide valuable insights for businesses and investors to inform decision-making. We believe that our analysis can serve as a valuable starting point for further research and investigation into these topics.
    Unsupervised learning for structure detection in plastically deformed crystals. (arXiv:2212.14813v1 [cond-mat.mtrl-sci])
    Detecting structures at the particle scale within plastically deformed crystalline materials allows a better understanding of the occurring phenomena. While previous approaches mostly relied on applying hand-chosen criteria on different local parameters, these approaches could only detect already known structures.We introduce an unsupervised learning algorithm to automatically detect structures within a crystal under plastic deformation. This approach is based on a study developed for structural detection on colloidal materials. This algorithm has the advantage of being computationally fast and easy to implement. We show that by using local parameters based on bond-angle distributions, we are able to detect more structures and with a higher degree of precision than traditional hand-made criteria.
    Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning. (arXiv:2101.01076v7 [cs.LG] UPDATED)
    Predicting video viewership is a top priority for content creators and video-sharing sites. Content creators live on such predictions to maximize influences and minimize budgets. Video-sharing sites rely on this prediction to promote credible videos and curb violative videos. Although deep learning champions viewership prediction, it lacks interpretability, which is fundamental to increasing the adoption of predictive models and prescribing measurements to improve viewership. Following the design-science paradigm, we propose a novel interpretable IT system, Precise Wide and Deep Learning (PrecWD), to precisely interpret viewership prediction. Improving upon state-of-the-art frameworks, PrecWD offers precise feature effects and designs an unstructured component. PrecWD outperforms benchmarks in two contexts: health video viewership prediction and misinformation viewership prediction. A user study confirms the superior interpretability of PrecWD. This study contributes to IS design theory with generalizable design principles and an interpretable predictive framework. Our findings provide implications to improve video viewership and credibility.
    A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia. (arXiv:2212.14744v1 [eess.IV])
    Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are frequently observed, along with insufficient medical infrastructure. Pleural effusion, a condition in which fluids fill the lung and complicate breathing, is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is to develop a method for the automatic diagnosis of bacterial and viral pneumonia in digital x-ray pictures. This article first presents the authors' technique, and then gives a comprehensive report on recent developments in the field of reliable diagnosis of pneumonia. In this study, here tuned a state-of-the-art deep convolutional neural network to classify plant diseases based on images and tested its performance. Deep learning architecture is compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152, Mobilenettv2, and DenseNet with 201 layers are among the architectures tested. Experiment data consists of two groups, sick and healthy X-ray pictures. To take appropriate action against plant diseases as soon as possible, rapid disease identification models are preferred. DenseNet201 has shown no overfitting or performance degradation in our experiments, and its accuracy tends to increase as the number of epochs increases. Further, DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time. This architecture outperforms the competition in terms of testing accuracy, scoring 95%. Each architecture was trained using Keras, using Theano as the backend.
    Industrial Scene Change Detection using Deep Convolutional Neural Networks. (arXiv:2212.14278v1 [cs.CV])
    Finding and localizing the conceptual changes in two scenes in terms of the presence or removal of objects in two images belonging to the same scene at different times in special care applications is of great significance. This is mainly due to the fact that addition or removal of important objects for some environments can be harmful. As a result, there is a need to design a program that locates these differences using machine vision. The most important challenge of this problem is the change in lighting conditions and the presence of shadows in the scene. Therefore, the proposed methods must be resistant to these challenges. In this article, a method based on deep convolutional neural networks using transfer learning is introduced, which is trained with an intelligent data synthesis process. The results of this method are tested and presented on the dataset provided for this purpose. It is shown that the presented method is more efficient than other methods and can be used in a variety of real industrial environments.
    Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer. (arXiv:2212.14652v1 [cs.CV])
    Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifier, TSR values were predicted and the results were compared to a visual TSR estimation made by a pathologist. The results suggest that classification accuracy does not improve when domain-specific data are used in the pre-training of the convolutional neural network models in the task at hand. Classification accuracy for stroma, tumor and other reached 96.1$\%$ on an independent test set. Among the three classes the best model gained the highest accuracy (99.3$\%$) for class tumor. When TSR was predicted with the best model, the correlation between the predicted values and values estimated by an experienced pathologist was 0.57. Further research is needed to study associations between computationally predicted TSR values and other clinicopathological factors of colorectal cancer and the overall survival of the patients.
    Extended method for Statistical Signal Characterization using moments and cumulants: Application to recognition of pattern alterations in pulse-like waveforms employing Artificial Neural Networks. (arXiv:2212.14783v1 [eess.SP])
    We propose a statistical procedure to characterize and extract features from a waveform that can be applied as a pre-processing signal stage in a pattern recognition task using Artificial Neural Networks. Such a procedure is based on measuring a 30-parameters set of moments and cumulants from the waveform, its derivative, and its integral. The technique is presented as an extension of the Statistical Signal Characterization method existing in the literature. As a testing methodology, we used the procedure to distinguish a pulse-like signal from different versions of itself with frequency spectrum alterations or deformations. The recognition task was performed by single feed-forward back-propagation networks trained for the case Sinc-, Gaussian-, and Chirp-pulse waveform. Because of the success obtained in these examples, we can conclude that the proposed extended statistical signal characterization method is an effective tool for pattern-recognition applications. In particular, we can use it as a fast pre-processing stage in embedded systems with limited memory or computational capability.
    A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses. (arXiv:2212.14693v1 [cs.CY])
    We developed a simulator to quantify the effect of exercise ordering on both student engagement and retention. Our approach combines the construction of neural network representations for users and exercises using a dynamic matrix factorization method. We further created a machine learning models of success and dropout prediction. As a result, our system is able to predict student engagement and retention based on a given sequence of exercises selected. This opens the door to the development of versatile reinforcement learning agents which can substitute the role of private tutoring in exam preparation.
    Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis. (arXiv:2212.14507v1 [cs.LG])
    Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.
    MAUVE Scores for Generative Models: Theory and Practice. (arXiv:2212.14578v1 [cs.LG])
    Generative AI has matured to a point where large-scale models can generate text that seems indistinguishable from human-written text and remarkably photorealistic images. Automatically measuring how close the distribution of generated data is to the target real data distribution is a key step in diagnosing existing models and developing better models. We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. These scores are statistical summaries of divergence frontiers capturing two types of errors in generative modeling. We explore four approaches to statistically estimate these scores: vector quantization, non-parametric estimation, classifier-based estimation, and parametric Gaussian approximations. We provide statistical bounds for the vector quantization approach. Empirically, we find that the proposed scores paired with a range of $f$-divergences and statistical estimation methods can quantify the gaps between the distributions of human-written text and those of modern neural language models by correlating with human judgments and identifying known properties of the generated texts. We conclude the paper by demonstrating its applications to other AI domains and discussing practical recommendations.
    Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping. (arXiv:2107.05341v3 [cs.LG] UPDATED)
    We explore the ability of overparameterized shallow neural networks to learn Lipschitz regression functions with and without label noise when trained by Gradient Descent (GD). To avoid the problem that in the presence of noisy labels, neural networks trained to nearly zero training error are inconsistent on this class, we propose an early stopping rule that allows us to show optimal rates. This provides an alternative to the result of Hu et al. (2021) who studied the performance of $\ell 2$ -regularized GD for training shallow networks in nonparametric regression which fully relied on the infinite-width network (Neural Tangent Kernel (NTK)) approximation. Here we present a simpler analysis which is based on a partitioning argument of the input space (as in the case of 1-nearest-neighbor rule) coupled with the fact that trained neural networks are smooth with respect to their inputs when trained by GD. In the noise-free case the proof does not rely on any kernelization and can be regarded as a finite-width result. In the case of label noise, by slightly modifying the proof, the noise is controlled using a technique of Yao, Rosasco, and Caponnetto (2007).
    Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design. (arXiv:2105.13205v2 [cs.LG] UPDATED)
    Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretization of continuous-time Hamiltonian systems and include several existing DNN architectures based on ordinary differential equations. Our main result is that a broad set of H-DNNs ensures non-vanishing gradients by design for an arbitrary network depth. This is obtained by proving that, using a semi-implicit Euler discretization scheme, the backward sensitivity matrices involved in gradient computations are symplectic. We also provide an upper-bound to the magnitude of sensitivity matrices and show that exploding gradients can be controlled through regularization. Finally, we enable distributed implementations of backward and forward propagation algorithms in H-DNNs by characterizing appropriate sparsity constraints on the weight matrices. The good performance of H-DNNs is demonstrated on benchmark classification problems, including image classification with the MNIST dataset.
    Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs. (arXiv:2212.14806v1 [eess.SP])
    There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.
    Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Building. (arXiv:2212.14730v1 [cs.CV])
    Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.
    Improving Certified Robustness via Statistical Learning with Logical Reasoning. (arXiv:2003.00120v7 [cs.LG] UPDATED)
    Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a limited perturbation radius. Given that existing pure data-driven statistical approaches have reached a bottleneck, in this paper, we propose to integrate statistical ML models with knowledge (expressed as logical rules) as a reasoning component using Markov logic networks (MLN, so as to further improve the overall certified robustness. This opens new research questions about certifying the robustness of such a paradigm, especially the reasoning component (e.g., MLN). As the first step towards understanding these questions, we first prove that the computational complexity of certifying the robustness of MLN is #P-hard. Guided by this hardness result, we then derive the first certified robustness bound for MLN by carefully analyzing different model regimes. Finally, we conduct extensive experiments on five datasets including both high-dimensional images and natural language texts, and we show that the certified robustness with knowledge-based logical reasoning indeed significantly outperforms that of the state-of-the-arts.
    PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models. (arXiv:2212.14838v1 [stat.ML])
    In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.
    On the Interpretability of Attention Networks. (arXiv:2212.14776v1 [cs.LG])
    Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.
    ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports. (arXiv:2212.14882v1 [cs.CL])
    The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.
    A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems. (arXiv:2212.14709v1 [cs.LG])
    This paper concerns the study of optimal (supremum and infimum) uncertainty bounds for systems where the input (or prior) probability measure is only partially/imperfectly known (e.g., with only statistical moments and/or on a coarse topology) rather than fully specified. Such partial knowledge provides constraints on the input probability measures. The theory of Optimal Uncertainty Quantification allows us to convert the task into a constraint optimization problem where one seeks to compute the least upper/greatest lower bound of the system's output uncertainties by finding the extremal probability measure of the input. Such optimization requires repeated evaluation of the system's performance indicator (input to performance map) and is high-dimensional and non-convex by nature. Therefore, it is difficult to find the optimal uncertainty bounds in practice. In this paper, we examine the use of machine learning, especially deep neural networks, to address the challenge. We achieve this by introducing a neural network classifier to approximate the performance indicator combined with the stochastic gradient descent method to solve the optimization problem. We demonstrate the learning based framework on the uncertainty quantification of the impact of magnesium alloys, which are promising light-weight structural and protective materials. Finally, we show that the approach can be used to construct maps for the performance certificate and safety design in engineering practice.
    Boosting Simple Learners. (arXiv:2001.11704v7 [cs.LG] UPDATED)
    Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. We study boosting under the assumption that the weak hypotheses belong to a class of bounded capacity. This assumption is inspired by the common convention that weak hypotheses are "rules-of-thumbs" from an "easy-to-learn class". (Schapire and Freund~'12, Shalev-Shwartz and Ben-David '14.) Formally, we assume the class of weak hypotheses has a bounded VC dimension. We focus on two main questions: (i) Oracle Complexity: How many weak hypotheses are needed to produce an accurate hypothesis? We design a novel boosting algorithm and demonstrate that it circumvents a classical lower bound by Freund and Schapire ('95, '12). Whereas the lower bound shows that $\Omega({1}/{\gamma^2})$ weak hypotheses with $\gamma$-margin are sometimes necessary, our new method requires only $\tilde{O}({1}/{\gamma})$ weak hypothesis, provided that they belong to a class of bounded VC dimension. Unlike previous boosting algorithms which aggregate the weak hypotheses by majority votes, the new boosting algorithm uses more complex ("deeper") aggregation rules. We complement this result by showing that complex aggregation rules are in fact necessary to circumvent the aforementioned lower bound. (ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class? Can complex concepts that are "far away" from the class be learned? Towards answering the first question we {introduce combinatorial-geometric parameters which capture expressivity in boosting.} As a corollary we provide an affirmative answer to the second question for well-studied classes, including half-spaces and decision stumps. Along the way, we establish and exploit connections with Discrepancy Theory.
    ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models. (arXiv:2212.14599v1 [cs.LG])
    The advances in Artificial Intelligence are creating new opportunities to improve lives of people around the world, from business to healthcare, from lifestyle to education. For example, some systems profile the users using their demographic and behavioral characteristics to make certain domain-specific predictions. Often, such predictions impact the life of the user directly or indirectly (e.g., loan disbursement, determining insurance coverage, shortlisting applications, etc.). As a result, the concerns over such AI-enabled systems are also increasing. To address these concerns, such systems are mandated to be responsible i.e., transparent, fair, and explainable to developers and end-users. In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective. The framework helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model, such as robustness, drift susceptibility, and fairness, and (c) compare different models (from different model families or obtained through different hyperparameter settings) from an overall perspective thereby facilitating actionable recourse for improvement of the models. It is model agnostic and works with different supervised machine learning scenarios (i.e., Binary Classification, Multi-class Classification, and Regression) and frameworks. It can be seamlessly integrated with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems.
    Macro-block dropout for improved regularization in training end-to-end speech recognition models. (arXiv:2212.14149v1 [cs.LG])
    This paper proposes a new regularization algorithm referred to as macro-block dropout. The overfitting issue has been a difficult problem in training large neural network models. The dropout technique has proven to be simple yet very effective for regularization by preventing complex co-adaptations during training. In our work, we define a macro-block that contains a large number of units from the input to a Recurrent Neural Network (RNN). Rather than applying dropout to each unit, we apply random dropout to each macro-block. This algorithm has the effect of applying different drop out rates for each layer even if we keep a constant average dropout rate, which has better regularization effects. In our experiments using Recurrent Neural Network-Transducer (RNN-T), this algorithm shows relatively 4.30 % and 6.13 % Word Error Rates (WERs) improvement over the conventional dropout on LibriSpeech test-clean and test-other. With an Attention-based Encoder-Decoder (AED) model, this algorithm shows relatively 4.36 % and 5.85 % WERs improvement over the conventional dropout on the same test sets.
    Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Navigation. (arXiv:2212.14400v1 [cs.RO])
    In this paper, we present a framework for learning quadruped navigation by integrating central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework. Through both exteroceptive and proprioceptive sensing, the agent learns to modulate the intrinsic oscillator setpoints (amplitude and frequency) and coordinate rhythmic behavior among different oscillators to track velocity commands while avoiding collisions with the environment. We compare different neural network architectures (i.e. memory-free and memory-enabled) which learn implicit interoscillator couplings, as well as varying the strength of the explicit coupling weights in the oscillator dynamics equations. We train our policies in simulation and perform a sim-to-real transfer to the Unitree Go1 quadruped, where we observe robust navigation in a variety of scenarios. Our results show that both memory-enabled policy representations and explicit interoscillator couplings are beneficial for a successful sim-to-real transfer for navigation tasks. Video results can be found at https://youtu.be/O_LX1oLZOe0.
    Node-Element Hypergraph Message Passing for Fluid Dynamics Simulations. (arXiv:2212.14545v1 [physics.flu-dyn])
    A recent trend in deep learning research features the application of graph neural networks for mesh-based continuum mechanics simulations. Most of these frameworks operate on graphs in which each edge connects two nodes. Inspired by the data connectivity in the finite element method, we connect the nodes by elements rather than edges, effectively forming a hypergraph. We implement a message-passing network on such a node-element hypergraph and explore the capability of the network for the modeling of fluid flow. The network is tested on two common benchmark problems, namely the fluid flow around a circular cylinder and airfoil configurations. The results show that such a message-passing network defined on the node-element hypergraph is able to generate more stable and accurate temporal roll-out predictions compared to the baseline generalized message-passing network defined on a normal graph. Along with adjustments in activation function and training loss, we expect this work to set a new strong baseline for future explorations of mesh-based fluid simulations with graph neural networks.
    Machine Learning as an Accurate Predictor for Percolation Threshold of Diverse Networks. (arXiv:2212.14694v1 [physics.soc-ph])
    Percolation threshold is an important measure to determine the inherent rigidity of large networks. Predictors of the percolation threshold for large networks are computationally intense to run, hence it is a necessity to develop predictors of the percolation threshold of networks, that do not rely on numerical simulations. We demonstrate the efficacy of five machine learning-based regression techniques for the accurate prediction of the percolation threshold. The dataset generated to train the machine learning models contains a total of 777 real and synthetic networks and consists of 5 statistical and structural properties of networks as features and the numerically computed percolation threshold as the output attribute. We establish that the machine learning models outperform three existing empirical estimators of bond percolation threshold, and extend this experiment to predict site and explosive percolation. We also compare the performance of our models in predicting the percolation threshold and find that the gradient boosting regressor, multilayer perceptron and random forests regression models achieve the least RMSE values among the models utilized.
    Cluster-level Group Representativity Fairness in $k$-means Clustering. (arXiv:2212.14467v1 [cs.LG])
    There has been much interest recently in developing fair clustering algorithms that seek to do justice to the representation of groups defined along sensitive attributes such as race and gender. We observe that clustering algorithms could generate clusters such that different groups are disadvantaged within different clusters. We develop a clustering algorithm, building upon the centroid clustering paradigm pioneered by classical algorithms such as $k$-means, where we focus on mitigating the unfairness experienced by the most-disadvantaged group within each cluster. Our method uses an iterative optimisation paradigm whereby an initial cluster assignment is modified by reassigning objects to clusters such that the worst-off sensitive group within each cluster is benefitted. We demonstrate the effectiveness of our method through extensive empirical evaluations over a novel evaluation metric on real-world datasets. Specifically, we show that our method is effective in enhancing cluster-level group representativity fairness significantly at low impact on cluster coherence.
    Fruit Ripeness Classification: a Survey. (arXiv:2212.14441v1 [cs.CV])
    Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by experts in the field, which makes it a labor-intensive and error-prone process. Thus, there is an arising need for automation in the process of fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on.
    Pontryagin Optimal Controller via Neural Networks. (arXiv:2212.14566v1 [eess.SY])
    Solving real-world optimal control problems are challenging tasks, as the system dynamics can be highly non-linear or including nonconvex objectives and constraints, while in some cases the dynamics are unknown, making it hard to numerically solve the optimal control actions. To deal with such modeling and computation challenges, in this paper, we integrate Neural Networks with the Pontryagin's Minimum Principle (PMP), and propose a computationally efficient framework NN-PMP. The resulting controller can be implemented for systems with unknown and complex dynamics. It can not only utilize the accurate surrogate models parameterized by neural networks, but also efficiently recover the optimality conditions along with the optimal action sequences via PMP conditions. A toy example on a nonlinear Martian Base operation along with a real-world lossy energy storage arbitrage example demonstrates our proposed NN-PMP is a general and versatile computation tool for finding optimal solutions. Compared with solutions provided by the numerical optimization solver with approximated linear dynamics, NN-PMP achieves more efficient system modeling and higher performance in terms of control objectives.
    Can $5^{\rm th}$ Generation Local Training Methods Support Client Sampling? Yes!. (arXiv:2212.14370v1 [cs.LG])
    The celebrated FedAvg algorithm of McMahan et al. (2017) is based on three components: client sampling (CS), data sampling (DS) and local training (LT). While the first two are reasonably well understood, the third component, whose role is to reduce the number of communication rounds needed to train the model, resisted all attempts at a satisfactory theoretical explanation. Malinovsky et al. (2022) identified four distinct generations of LT methods based on the quality of the provided theoretical communication complexity guarantees. Despite a lot of progress in this area, none of the existing works were able to show that it is theoretically better to employ multiple local gradient-type steps (i.e., to engage in LT) than to rely on a single local gradient-type step only in the important heterogeneous data regime. In a recent breakthrough embodied in their ProxSkip method and its theoretical analysis, Mishchenko et al. (2022) showed that LT indeed leads to provable communication acceleration for arbitrarily heterogeneous data, thus jump-starting the $5^{\rm th}$ generation of LT methods. However, while these latest generation LT methods are compatible with DS, none of them support CS. We resolve this open problem in the affirmative. In order to do so, we had to base our algorithmic development on new algorithmic and theoretical foundations.
    INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation. (arXiv:2212.14365v1 [cs.LG])
    Neural operators, which emerge as implicit solution operators of hidden governing equations, have recently become popular tools for learning responses of complex real-world physical systems. Nevertheless, the majority of neural operator applications has thus far been data-driven, which neglects the intrinsic preservation of fundamental physical laws in data. In this paper, we introduce a novel integral neural operator architecture, to learn physical models with fundamental conservation laws automatically guaranteed. In particular, by replacing the frame-dependent position information with its invariant counterpart in the kernel space, the proposed neural operator is by design translation- and rotation-invariant, and consequently abides by the conservation laws of linear and angular momentums. As applications, we demonstrate the expressivity and efficacy of our model in learning complex material behaviors from both synthetic and experimental datasets, and show that, by automatically satisfying these essential physical laws, our learned neural operator is not only generalizable in handling translated and rotated datasets, but also achieves state-of-the-art accuracy and efficiency as compared to baseline neural operator models.
    Long-horizon video prediction using a dynamic latent hierarchy. (arXiv:2212.14376v1 [cs.LG])
    The task of video prediction and generation is known to be notoriously difficult, with the research in this area largely limited to short-term predictions. Though plagued with noise and stochasticity, videos consist of features that are organised in a spatiotemporal hierarchy, different features possessing different temporal dynamics. In this paper, we introduce Dynamic Latent Hierarchy (DLH) -- a deep hierarchical latent model that represents videos as a hierarchy of latent states that evolve over separate and fluid timescales. Each latent state is a mixture distribution with two components, representing the immediate past and the predicted future, causing the model to learn transitions only between sufficiently dissimilar states, while clustering temporally persistent states closer together. Using this unique property, DLH naturally discovers the spatiotemporal structure of a dataset and learns disentangled representations across its hierarchy. We hypothesise that this simplifies the task of modeling temporal dynamics of a video, improves the learning of long-term dependencies, and reduces error accumulation. As evidence, we demonstrate that DLH outperforms state-of-the-art benchmarks in video prediction, is able to better represent stochasticity, as well as to dynamically adjust its hierarchical and temporal structure. Our paper shows, among other things, how progress in representation learning can translate into progress in prediction tasks.
    Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4:2:0 Content. (arXiv:2212.14187v1 [cs.CV])
    This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically three issues, including (1) B-frame coding, (2) YUV 4:2:0 coding, and (3) content-adaptive variable-rate coding with only one single model. Most learned video codecs operate internally in the RGB domain for P-frame coding. B-frame coding for YUV 4:2:0 content is largely under-explored. In addition, while there have been prior works on variable-rate coding with conditional convolution, most of them fail to consider the content information. We build our scheme on conditional augmented normalized flows (CANF). It features conditional motion and inter-frame codecs for efficient B-frame coding. To cope with YUV 4:2:0 content, two conditional inter-frame codecs are used to process the Y and UV components separately, with the coding of the UV components conditioned additionally on the Y component. Moreover, we introduce adaptive feature modulation in every convolutional layer, taking into account both the content information and the coding levels of B-frames to achieve content-adaptive variable-rate coding. Experimental results show that our model outperforms x265 and the winner of last year's challenge on commonly used datasets in terms of PSNR-YUV.
    Bayesian statistical learning using density operators. (arXiv:2212.14715v1 [math.ST])
    This short study reformulates the statistical Bayesian learning problem using a quantum mechanics framework. Density operators representing ensembles of pure states of sample wave functions are used in place probability densities. We show that such representation allows to formulate the statistical Bayesian learning problem in different coordinate systems on the sample space. We further show that such representation allows to learn projections of density operators using a kernel trick. In particular, the study highlights that decomposing wave functions rather than probability densities, as it is done in kernel embedding, allows to preserve the nature of probability operators. Results are illustrated with a simple example using discrete orthogonal wavelet transform of density operators.
    Relative Probability on Finite Outcome Spaces: A Systematic Examination of its Axiomatization, Properties, and Applications. (arXiv:2212.14555v1 [stat.ML])
    This work proposes a view of probability as a relative measure rather than an absolute one. To demonstrate this concept, we focus on finite outcome spaces and develop three fundamental axioms that establish requirements for relative probability functions. We then provide a library of examples of these functions and a system for composing them. Additionally, we discuss a relative version of Bayesian inference and its digital implementation. Finally, we prove the topological closure of the relative probability space, highlighting its ability to preserve information under limits.
    Constant Approximation for Normalized Modularity and Associations Clustering. (arXiv:2212.14334v1 [cs.DS])
    We study the problem of graph clustering under a broad class of objectives in which the quality of a cluster is defined based on the ratio between the number of edges in the cluster, and the total weight of vertices in the cluster. We show that our definition is closely related to popular clustering measures, namely normalized associations, which is a dual of the normalized cut objective, and normalized modularity. We give a linear time constant-approximate algorithm for our objective, which implies the first constant-factor approximation algorithms for normalized modularity and normalized associations.
    Delving into Semantic Scale Imbalance. (arXiv:2212.14613v1 [cs.CV])
    Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias.
    Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. (arXiv:2212.14449v1 [math.OC])
    Mean-field games have been used as a theoretical tool to obtain an approximate Nash equilibrium for symmetric and anonymous $N$-player games in literature. However, limiting applicability, existing theoretical results assume variations of a "population generative model", which allows arbitrary modifications of the population distribution by the learning algorithm. Instead, we show that $N$ agents running policy mirror ascent converge to the Nash equilibrium of the regularized game within $\tilde{\mathcal{O}}(\varepsilon^{-2})$ samples from a single sample trajectory without a population generative model, up to a standard $\mathcal{O}(\frac{1}{\sqrt{N}})$ error due to the mean field. Taking a divergent approach from literature, instead of working with the best-response map we first show that a policy mirror ascent map can be used to construct a contractive operator having the Nash equilibrium as its fixed point. Next, we prove that conditional TD-learning in $N$-agent games can learn value functions within $\tilde{\mathcal{O}}(\varepsilon^{-2})$ time steps. These results allow proving sample complexity guarantees in the oracle-free setting by only relying on a sample path from the $N$ agent simulator. Furthermore, we demonstrate that our methodology allows for independent learning by $N$ agents with finite sample guarantees.
    POMRL: No-Regret Learning-to-Plan with Increasing Horizons. (arXiv:2212.14530v1 [cs.AI])
    We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with limited interactions per task. The agent can use its experience in each task and across tasks to estimate both the transition model and the distribution over tasks. We propose an algorithm to meta-learn the underlying structure across tasks, utilize it to plan in each task, and upper-bound the regret of the planning loss. Our bound suggests that the average regret over tasks decreases as the number of tasks increases and as the tasks are more similar. In the classical single-task setting, it is known that the planning horizon should depend on the estimated model's accuracy, that is, on the number of samples within task. We generalize this finding to meta-RL and study this dependence of planning horizons on the number of tasks. Based on our theoretical findings, we derive heuristics for selecting slowly increasing discount factors, and we validate its significance empirically.
    An Entropy-Based Model for Hierarchical Learning. (arXiv:2212.14681v1 [stat.ML])
    Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, for a computer to learn from data accurately and efficiently, some auxiliary information about the data distribution and target function should be provided to it through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. In this paper, we propose a learning model that exploits this multiscale data structure and discuss its statistical and computational benefits. The hierarchical learning model is inspired by the logical and progressive easy-to-hard learning mechanism of human beings and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.
    Eliminating Meta Optimization Through Self-Referential Meta Learning. (arXiv:2212.14392v1 [cs.LG])
    Meta Learning automates the search for learning algorithms. At the same time, it creates a dependency on human engineering on the meta-level, where meta learning algorithms need to be designed. In this paper, we investigate self-referential meta learning systems that modify themselves without the need for explicit meta optimization. We discuss the relationship of such systems to in-context and memory-based meta learning and show that self-referential neural networks require functionality to be reused in the form of parameter sharing. Finally, we propose fitness monotonic execution (FME), a simple approach to avoid explicit meta optimization. A neural network self-modifies to solve bandit and classic control tasks, improves its self-modifications, and learns how to learn, purely by assigning more computational resources to better performing solutions.
    A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators. (arXiv:2212.14163v1 [stat.ML])
    Kernels are efficient in representing nonlocal dependence and they are widely used to design operators between function spaces. Thus, learning kernels in operators from data is an inverse problem of general interest. Due to the nonlocal dependence, the inverse problem can be severely ill-posed with a data-dependent singular inversion operator. The Bayesian approach overcomes the ill-posedness through a non-degenerate prior. However, a fixed non-degenerate prior leads to a divergent posterior mean when the observation noise becomes small, if the data induces a perturbation in the eigenspace of zero eigenvalues of the inversion operator. We introduce a data-adaptive prior to achieve a stable posterior whose mean always has a small noise limit. The data-adaptive prior's covariance is the inversion operator with a hyper-parameter selected adaptive to data by the L-curve method. Furthermore, we provide a detailed analysis on the computational practice of the data-adaptive prior, and demonstrate it on Toeplitz matrices and integral operators. Numerical tests show that a fixed prior can lead to a divergent posterior mean in the presence of any of the four types of errors: discretization error, model error, partial observation and wrong noise assumption. In contrast, the data-adaptive prior always attains posterior means with small noise limits.
    A novel cluster internal evaluation index based on hyper-balls. (arXiv:2212.14524v1 [cs.LG])
    It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis. In this paper, the multi-granularity characterization of the data set is carried out to obtain the hyper-balls. The cluster internal evaluation index based on hyper-balls(HCVI) is defined. Moreover, a general method for determining the optimal number of clusters based on HCVI is proposed. The proposed methods can evaluate the clustering results produced by the several classic methods and determine the optimal cluster number for data sets containing noises and clusters with arbitrary shapes. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.
    Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization. (arXiv:2212.14670v1 [q-fin.TR])
    Designing an intelligent volume-weighted average price (VWAP) strategy is a critical concern for brokers, since traditional rule-based strategies are relatively static that cannot achieve a lower transaction cost in a dynamic market. Many studies have tried to minimize the cost via reinforcement learning, but there are bottlenecks in improvement, especially for long-duration strategies such as the VWAP strategy. To address this issue, we propose a deep learning and hierarchical reinforcement learning jointed architecture termed Macro-Meta-Micro Trader (M3T) to capture market patterns and execute orders from different temporal scales. The Macro Trader first allocates a parent order into tranches based on volume profiles as the traditional VWAP strategy does, but a long short-term memory neural network is used to improve the forecasting accuracy. Then the Meta Trader selects a short-term subgoal appropriate to instant liquidity within each tranche to form a mini-tranche. The Micro Trader consequently extracts the instant market state and fulfils the subgoal with the lowest transaction cost. Our experiments over stocks listed on the Shanghai stock exchange demonstrate that our approach outperforms baselines in terms of VWAP slippage, with an average cost saving of 1.16 base points compared to the optimal baseline.
    Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm. (arXiv:2212.14117v1 [cs.CL])
    Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue's future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.
    An Experience-based Direct Generation approach to Automatic Image Cropping. (arXiv:2212.14561v1 [cs.CV])
    Automatic Image Cropping is a challenging task with many practical downstream applications. The task is often divided into sub-problems - generating cropping candidates, finding the visually important regions, and determining aesthetics to select the most appealing candidate. Prior approaches model one or more of these sub-problems separately, and often combine them sequentially. We propose a novel convolutional neural network (CNN) based method to crop images directly, without explicitly modeling image aesthetics, evaluating multiple crop candidates, or detecting visually salient regions. Our model is trained on a large dataset of images cropped by experienced editors and can simultaneously predict bounding boxes for multiple fixed aspect ratios. We consider the aspect ratio of the cropped image to be a critical factor that influences aesthetics. Prior approaches for automatic image cropping, did not enforce the aspect ratio of the outputs, likely due to a lack of datasets for this task. We, therefore, benchmark our method on public datasets for two related tasks - first, aesthetic image cropping without regard to aspect ratio, and second, thumbnail generation that requires fixed aspect ratio outputs, but where aesthetics are not crucial. We show that our strategy is competitive with or performs better than existing methods in both these tasks. Furthermore, our one-stage model is easier to train and significantly faster than existing two-stage or end-to-end methods for inference. We present a qualitative evaluation study, and find that our model is able to generalize to diverse images from unseen datasets and often retains compositional properties of the original images after cropping. Our results demonstrate that explicitly modeling image aesthetics or visual attention regions is not necessarily required to build a competitive image cropping algorithm.
    On Learning the Structure of Clusters in Graphs. (arXiv:2212.14345v1 [cs.DS])
    Graph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data. In many real-world applications, we find that the clusters have a significant high-level structure. This is often overlooked in the design and analysis of graph clustering algorithms which make strong simplifying assumptions about the structure of the graph. This thesis addresses the natural question of whether the structure of clusters can be learned efficiently and describes four new algorithmic results for learning such structure in graphs and hypergraphs. All of the presented theoretical results are extensively evaluated on both synthetic and real-word datasets of different domains, including image classification and segmentation, migration networks, co-authorship networks, and natural language processing. These experimental results demonstrate that the newly developed algorithms are practical, effective, and immediately applicable for learning the structure of clusters in real-world data.
    Pensieve 5G: Implementation of RL-based ABR Algorithm for UHD 4K/8K Content Delivery on Commercial 5G SA/NR-DC Network. (arXiv:2212.14479v1 [cs.NI])
    While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a high-performance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents. Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.
    Estimating Latent Population Flows from Aggregated Data via Inversing Multi-Marginal Optimal Transport. (arXiv:2212.14527v1 [cs.LG])
    We study the problem of estimating latent population flows from aggregated count data. This problem arises when individual trajectories are not available due to privacy issues or measurement fidelity. Instead, the aggregated observations are measured over discrete-time points, for estimating the population flows among states. Most related studies tackle the problems by learning the transition parameters of a time-homogeneous Markov process. Nonetheless, most real-world population flows can be influenced by various uncertainties such as traffic jam and weather conditions. Thus, in many cases, a time-homogeneous Markov model is a poor approximation of the much more complex population flows. To circumvent this difficulty, we resort to a multi-marginal optimal transport (MOT) formulation that can naturally represent aggregated observations with constrained marginals, and encode time-dependent transition matrices by the cost functions. In particular, we propose to estimate the transition flows from aggregated data by learning the cost functions of the MOT framework, which enables us to capture time-varying dynamic patterns. The experiments demonstrate the improved accuracy of the proposed algorithms than the related methods in estimating several real-world transition flows.
    Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation. (arXiv:2212.14102v1 [cs.LG])
    Inferring knowledge from clinical trials using knowledge graph embedding is an emerging area. However, customizing graph embeddings for different use cases remains a significant challenge. We propose custom2vec, an algorithmic framework to customize graph embeddings by incorporating user preferences in training the embeddings. It captures user preferences by adding custom nodes and links derived from manually vetted results of a separate information retrieval method. We propose a joint learning objective to preserve the original network structure while incorporating the user's custom annotations. We hypothesize that the custom training improves user-expected predictions, for example, in link prediction tasks. We demonstrate the effectiveness of custom2vec for clinical trials related to non-small cell lung cancer (NSCLC) with two customization scenarios: recommending immuno-oncology trials evaluating PD-1 inhibitors and exploring similar trials that compare new therapies with a standard of care. The results show that custom2vec training achieves better performance than the conventional training methods. Our approach is a novel way to customize knowledge graph embeddings and enable more accurate recommendations and predictions.
    Label-Efficient Interactive Time-Series Anomaly Detection. (arXiv:2212.14621v1 [cs.LG])
    Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are usually sub-optimal and unsatisfactory to end customers. Weak supervision is a promising paradigm for obtaining considerable labels in a low-cost way, which enables the customers to label data by writing heuristic rules rather than annotating each instance individually. However, in the time-series domain, it is hard for people to write reasonable labeling functions as the time-series data is numerically continuous and difficult to be understood. In this paper, we propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system, which enables a user to improve the results of unsupervised anomaly detection by performing only a small amount of interactions with the system. To achieve this goal, the system integrates weak supervision and active learning collaboratively while generating labeling functions automatically using only a few labeled data. All of these techniques are complementary and can promote each other in a reinforced manner. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions in both weak supervision and active learning areas. Also, the system has been tested in a real scenario in industry to show its practicality.
    Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks. (arXiv:2212.14374v1 [cs.LG])
    There are two important things in science: (A) Finding answers to given questions, and (B) Coming up with good questions. Our artificial scientists not only learn to answer given questions, but also continually invent new questions, by proposing hypotheses to be verified or falsified through potentially complex and time-consuming experiments, including thought experiments akin to those of mathematicians. While an artificial scientist expands its knowledge, it remains biased towards the simplest, least costly experiments that still have surprising outcomes, until they become boring. We present an empirical analysis of the automatic generation of interesting experiments. In the first setting, we investigate self-invented experiments in a reinforcement-providing environment and show that they lead to effective exploration. In the second setting, pure thought experiments are implemented as the weights of recurrent neural networks generated by a neural experiment generator. Initially interesting thought experiments may become boring over time.
    Learning Multimodal Data Augmentation in Feature Space. (arXiv:2212.14453v1 [cs.LG])
    The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains limited to single-modality tasks like image classification. Indeed, it is particularly difficult to augment each modality while preserving the overall semantic structure of the data; for example, a caption may no longer be a good description of an image after standard augmentations have been applied, such as translation. Moreover, it is challenging to specify reasonable transformations that are not tailored to a particular modality. In this paper, we introduce LeMDA, Learning Multimodal Data Augmentation, an easy-to-use method that automatically learns to jointly augment multimodal data in feature space, with no constraints on the identities of the modalities or the relationship between modalities. We show that LeMDA can (1) profoundly improve the performance of multimodal deep learning architectures, (2) apply to combinations of modalities that have not been previously considered, and (3) achieve state-of-the-art results on a wide range of applications comprised of image, text, and tabular data.
    A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization. (arXiv:2212.14150v1 [cs.LG])
    Implicit regularization is an important way to interpret neural networks. Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics are relatively small but not infinitesimal, thus fitting well with the practical implementation of neural networks. Currently, discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization, i.e. landscape analysis. It mainly focuses on gradient regions, such as saddle points and local minima. We theoretically establish the connection between saddle point escaping (SPE) stages and the matrix rank in DMF. We prove that, for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. This work provides a new theory to analyze implicit regularization in deep learning.
    Restricting to the chip architecture maintains the quantum neural network accuracy, if the parameterization is a $2$-design. (arXiv:2212.14426v1 [quant-ph])
    In the era of noisy intermediate scale quantum devices, variational quantum circuits (VQCs) are currently one of the main strategies for building quantum machine learning models. These models are made up of a quantum part and a classical part. The quantum part is given by a parametrization $U$, which, in general, is obtained from the product of different quantum gates. By its turn, the classical part corresponds to an optimizer that updates the parameters of $U$ in order to minimize a cost function $C$. However, despite the many applications of VQCs, there are still questions to be answered, such as for example: What is the best sequence of gates to be used? How to optimize their parameters? Which cost function to use? How the architecture of the quantum chips influences the final results? In this article, we focus on answering the last question. We will show that, in general, the cost function will tend to a typical average value the closer the parameterization used is from a $2$-design. Therefore, the closer this parameterization is to a $2$-design, the less the result of the quantum neural network model will depend on its parametrization. As a consequence, we can use the own architecture of the quantum chips to defined the VQC parametrization, avoiding the use of additional swap gates and thus diminishing the VQC depth and the associated errors.
    A Multiagent Framework for the Asynchronous and Collaborative Extension of Multitask ML Systems. (arXiv:2209.14745v2 [cs.LG] UPDATED)
    The traditional ML development methodology does not enable a large number of contributors, each with distinct objectives, to work collectively on the creation and extension of a shared intelligent system. Enabling such a collaborative methodology can accelerate the rate of innovation, increase ML technologies accessibility and enable the emergence of novel capabilities. We believe that this novel methodology for ML development can be demonstrated through a modularized representation of ML models and the definition of novel abstractions allowing to implement and execute diverse methods for the asynchronous use and extension of modular intelligent systems. We present a multiagent framework for the collaborative and asynchronous extension of dynamic large-scale multitask systems.
    Detection of out-of-distribution samples using binary neuron activation patterns. (arXiv:2212.14268v1 [cs.LG])
    Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain significant limitation of DNN classifiers. The ability to identify previously unseen inputs as novel is crucial in safety-critical applications such as self-driving cars, unmanned aerial vehicles and robots. Existing approaches to detect OOD samples treat a DNN as a black box and assess the confidence score of the output predictions. Unfortunately, this method frequently fails, because DNN are not trained to reduce their confidence for OOD inputs. In this work, we introduce a novel method for OOD detection. Our method is motivated by theoretical analysis of neuron activation patterns (NAP) in ReLU based architectures. The proposed method does not introduce high computational workload due to the binary representation of the activation patterns extracted from convolutional layers. The extensive empirical evaluation proves its high performance on various DNN architectures and seven image datasets. ion.
    Lookback for Learning to Branch. (arXiv:2206.14987v2 [cs.LG] UPDATED)
    The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Program (MILP) solvers. Recent works have demonstrated the effectiveness of such GNNs in replacing the branching (variable selection) heuristic in branch-and-bound (B&B) solvers. These GNNs are trained, offline and on a collection of MILPs, to imitate a very good but computationally expensive branching heuristic, strong branching. Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure. Specifically, we find that with the strong branching heuristic, a child node's best choice was often the parent's second-best choice. We call this the "lookback" phenomenon. Surprisingly, the typical branching GNN of Gasse et al. (2019) often misses this simple "answer". To imitate the target behavior more closely by incorporating the lookback phenomenon in GNNs, we propose two methods: (a) target smoothing for the standard cross-entropy loss function, and (b) adding a Parent-as-Target (PAT) Lookback regularizer term. Finally, we propose a model selection framework to incorporate harder-to-formulate objectives such as solving time in the final models. Through extensive experimentation on standard benchmark instances, we show that our proposal results in up to 22% decrease in the size of the B&B tree and up to 15% improvement in the solving times.
    CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks. (arXiv:2201.05729v3 [cs.CV] UPDATED)
    Contrastive language-image pretraining (CLIP) links vision and language modalities into a unified embedding space, yielding the tremendous potential for vision-language (VL) tasks. While early concurrent works have begun to study this potential on a subset of tasks, important questions remain: 1) What is the benefit of CLIP on unstudied VL tasks? 2) Does CLIP provide benefit in low-shot or domain-shifted scenarios? 3) Can CLIP improve existing approaches without impacting inference or pretraining complexity? In this work, we seek to answer these questions through two key contributions. First, we introduce an evaluation protocol that includes Visual Commonsense Reasoning (VCR), Visual Entailment (SNLI-VE), and Visual Question Answering (VQA), across a variety of data availability constraints and conditions of domain shift. Second, we propose an approach, named CLIP Targeted Distillation (CLIP-TD), to intelligently distill knowledge from CLIP into existing architectures using a dynamically weighted objective applied to adaptively selected tokens per instance. Experiments demonstrate that our proposed CLIP-TD leads to exceptional gains in the low-shot (up to 51.9%) and domain-shifted (up to 71.3%) conditions of VCR, while simultaneously improving performance under standard fully-supervised conditions (up to 2%), achieving state-of-art performance on VCR compared to other single models that are pretrained with image-text data only. On SNLI-VE, CLIP-TD produces significant gains in low-shot conditions (up to 6.6%) as well as fully supervised (up to 3%). On VQA, CLIP-TD provides improvement in low-shot (up to 9%), and in fully-supervised (up to 1.3%). Finally, CLIP-TD outperforms concurrent works utilizing CLIP for finetuning, as well as baseline naive distillation approaches. Code will be made available.
    Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets. (arXiv:2210.05958v2 [cs.CV] UPDATED)
    There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which is concluded to the lack of inductive bias. In this paper, we further consider this problem and point out two weaknesses of ViTs in inductive biases, that is, the spatial relevance and diverse channel representation. First, on spatial aspect, objects are locally compact and relevant, thus fine-grained feature needs to be extracted from a token and its neighbors. While the lack of data hinders ViTs to attend the spatial relevance. Second, on channel aspect, representation exhibits diversity on different channels. But the scarce data can not enable ViTs to learn strong enough representation for accurate recognition. To this end, we propose Dynamic Hybrid Vision Transformer (DHVT) as the solution to enhance the two inductive biases. On spatial aspect, we adopt a hybrid structure, in which convolution is integrated into patch embedding and multi-layer perceptron module, forcing the model to capture the token features as well as their neighboring features. On channel aspect, we introduce a dynamic feature aggregation module in MLP and a brand new "head token" design in multi-head self-attention module to help re-calibrate channel representation and make different channel group representation interacts with each other. The fusion of weak channel representation forms a strong enough representation for classification. With this design, we successfully eliminate the performance gap between CNNs and ViTs, and our DHVT achieves a series of state-of-the-art performance with a lightweight model, 85.68% on CIFAR-100 with 22.8M parameters, 82.3% on ImageNet-1K with 24.0M parameters. Code is available at https://github.com/ArieSeirack/DHVT.
    Robust Ranking Explanations. (arXiv:2212.14106v1 [cs.LG])
    Gradient-based explanation is the cornerstone of explainable deep networks, but it has been shown to be vulnerable to adversarial attacks. However, existing works measure the explanation robustness based on $\ell_p$-norm, which can be counter-intuitive to humans, who only pay attention to the top few salient features. We propose explanation ranking thickness as a more suitable explanation robustness metric. We then present a new practical adversarial attacking goal for manipulating explanation rankings. To mitigate the ranking-based attacks while maintaining computational feasibility, we derive surrogate bounds of the thickness that involve expensive sampling and integration. We use a multi-objective approach to analyze the convergence of a gradient-based attack to confirm that the explanation robustness can be measured by the thickness metric. We conduct experiments on various network architectures and diverse datasets to prove the superiority of the proposed methods, while the widely accepted Hessian-based curvature smoothing approaches are not as robust as our method.
    Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring. (arXiv:2212.14048v1 [cs.LG])
    Using Structural Health Monitoring (SHM) systems with extensive sensing arrangements on every civil structure can be costly and impractical. Various concepts have been introduced to alleviate such difficulties, such as Population-based SHM (PBSHM). Nevertheless, the studies presented in the literature do not adequately address the challenge of accessing the information on different structural states (conditions) of dissimilar civil structures. The study herein introduces a novel framework named Structural State Translation (SST), which aims to estimate the response data of different civil structures based on the information obtained from a dissimilar structure. SST can be defined as Translating a state of one civil structure to another state after discovering and learning the domain-invariant representation in the source domains of a dissimilar civil structure. SST employs a Domain-Generalized Cycle-Generative (DGCG) model to learn the domain-invariant representation in the acceleration datasets obtained from a numeric bridge structure that is in two different structural conditions. In other words, the model is tested on three dissimilar numeric bridge models to translate their structural conditions. The evaluation results of SST via Mean Magnitude-Squared Coherence (MMSC) and modal identifiers showed that the translated bridge states (synthetic states) are significantly similar to the real ones. As such, the minimum and maximum average MMSC values of real and translated bridge states are 91.2% and 97.1%, the minimum and the maximum difference in natural frequencies are 5.71% and 0%, and the minimum and maximum Modal Assurance Criterion (MAC) values are 0.998 and 0.870. This study is critical for data scarcity and PBSHM, as it demonstrates that it is possible to obtain data from structures while the structure is actually in a different condition or state.
    A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings. (arXiv:2212.14077v1 [cs.LG])
    In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.
    Maximizing Use-Case Specificity through Precision Model Tuning. (arXiv:2212.14206v1 [cs.CL])
    Language models have become increasingly popular in recent years for tasks like information retrieval. As use-cases become oriented toward specific domains, fine-tuning becomes default for standard performance. To fine-tune these models for specific tasks and datasets, it is necessary to carefully tune the model's hyperparameters and training techniques. In this paper, we present an in-depth analysis of the performance of four transformer-based language models on the task of biomedical information retrieval. The models we consider are DeepMind's RETRO (7B parameters), GPT-J (6B parameters), GPT-3 (175B parameters), and BLOOM (176B parameters). We compare their performance on the basis of relevance, accuracy, and interpretability, using a large corpus of 480000 research papers on protein structure/function prediction as our dataset. Our findings suggest that smaller models, with <10B parameters and fine-tuned on domain-specific datasets, tend to outperform larger language models on highly specific questions in terms of accuracy, relevancy, and interpretability by a significant margin (+50% on average). However, larger models do provide generally better results on broader prompts.
    Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education. (arXiv:2109.07908v2 [cs.CY] UPDATED)
    Colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potential for predictive analytics, two major barriers exist to their adoption in higher education: (a) the lack of democratization in deployment, and (b) the potential to exacerbate inequalities. Education researchers and policymakers encounter numerous challenges in deploying predictive modeling in practice. These challenges present in different steps of modeling including data preparation, model development, and evaluation. Nevertheless, each of these steps can introduce additional bias to the system if not appropriately performed. Most large-scale and nationally representative education data sets suffer from a significant number of incomplete responses from the research participants. While many education-related studies addressed the challenges of missing data, little is known about the impact of handling missing values on the fairness of predictive outcomes in practice. In this paper, we set out to first assess the disparities in predictive modeling outcomes for college-student success, then investigate the impact of imputation techniques on the model performance and fairness using a commonly used set of metrics. We conduct a prospective evaluation to provide a less biased estimation of future performance and fairness than an evaluation of historical data. Our comprehensive analysis of a real large-scale education dataset reveals key insights on modeling disparities and how imputation techniques impact the fairness of the student-success predictive outcome under different testing scenarios. Our results indicate that imputation introduces bias if the testing set follows the historical distribution. However, if the injustice in society is addressed and consequently the upcoming batch of observations is equalized, the model would be less biased.
    Are Deep Image Embedding Clustering Methods Effective for Heterogeneous Tabular Data?. (arXiv:2212.14111v1 [cs.LG])
    Deep learning methods in the literature are invariably benchmarked on image data sets and then assumed to work on all data problems. Unfortunately, architectures designed for image learning are often not ready or optimal for non-image data without considering data-specific learning requirements. In this paper, we take a data-centric view to argue that deep image embedding clustering methods are not equally effective on heterogeneous tabular data sets. This paper performs one of the first studies on deep embedding clustering of seven tabular data sets using six state-of-the-art baseline methods proposed for image data sets. Our results reveal that the traditional clustering of tabular data ranks second out of eight methods and is superior to most deep embedding clustering baselines. Our observation is in line with the recent literature that traditional machine learning of tabular data is still a competitive approach against deep learning. Although surprising to many deep learning researchers, traditional clustering methods can be competitive baselines for tabular data, and outperforming these baselines remains a challenge for deep embedding clustering. Therefore, deep learning methods for image learning may not be fair or suitable baselines for tabular data without considering data-specific contrasts and learning requirements.
    Quantum-Inspired Tensor Neural Networks for Option Pricing. (arXiv:2212.14076v1 [q-fin.PR])
    Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.
    Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. (arXiv:2211.09008v3 [astro-ph.IM] UPDATED)
    We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow. We first demonstrate the merit of this method on illustrative experiments and then analyze four parameters of the latest LIGO/Virgo data release: primary mass, secondary mass, redshift, and effective spin. Our results show that despite the small and notoriously noisy dataset, the posterior predictive distributions (assuming a prior over the parameters of the flow) of the observed gravitational wave population recover structure that agrees with robust previous phenomenological modeling results while being less susceptible to biases introduced by less flexible models. Therefore, the method forms a promising flexible, reliable replacement for population inference distributions, even when data is highly noisy.
    Decentralized Learning with Separable Data: Generalization and Fast Algorithms. (arXiv:2209.07116v3 [cs.LG] UPDATED)
    Decentralized learning offers privacy and communication efficiency when data are naturally distributed among agents communicating over an underlying graph. Motivated by overparameterized learning settings, in which models are trained to zero training loss, we study algorithmic and generalization properties of decentralized learning with gradient descent on separable data. Specifically, for decentralized gradient descent (DGD) and a variety of loss functions that asymptote to zero at infinity (including exponential and logistic losses), we derive novel finite-time generalization bounds. This complements a long line of recent work that studies the generalization performance and the implicit bias of gradient descent over separable data, but has thus far been limited to centralized learning scenarios. Notably, our generalization bounds approximately match in order their centralized counterparts. Critical behind this, and of independent interest, is establishing novel bounds on the training loss and the rate-of-consensus of DGD for a class of self-bounded losses. Finally, on the algorithmic front, we design improved gradient-based routines for decentralized learning with separable data and empirically demonstrate orders-of-magnitude of speed-up in terms of both training and generalization performance.
    Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs. (arXiv:2201.05715v2 [cs.LG] UPDATED)
    Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data. However, every forward evaluation of a NODE requires numerical integration of the neural network used to capture the system dynamics, making their training prohibitively expensive. Existing works rely on off-the-shelf adaptive step-size numerical integration schemes, which often require an excessive number of evaluations of the underlying dynamics network to obtain sufficient accuracy for training. By contrast, we accelerate the evaluation and the training of NODEs by proposing a data-driven approach to their numerical integration. The proposed Taylor-Lagrange NODEs (TL-NODEs) use a fixed-order Taylor expansion for numerical integration, while also learning to estimate the expansion's approximation error. As a result, the proposed approach achieves the same accuracy as adaptive step-size schemes while employing only low-order Taylor expansions, thus greatly reducing the computational cost necessary to integrate the NODE. A suite of numerical experiments, including modeling dynamical systems, image classification, and density estimation, demonstrate that TL-NODEs can be trained more than an order of magnitude faster than state-of-the-art approaches, without any loss in performance.
    Search Efficient Binary Network Embedding. (arXiv:1901.04097v2 [cs.SI] UPDATED)
    Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned continuous vector representations are inefficient for large-scale similarity search, which often involves finding nearest neighbors measured by distance or similarity in a continuous vector space. In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations through a stochastic gradient descent based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support faster node similarity search than using Euclidean distance or other distance measures. Extensive experiments and comparisons demonstrate that BinaryNE not only delivers more than 25 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods. The binary codes learned by BinaryNE also render competitive performance on node classification and node clustering tasks. The source code of this paper is available at https://github.com/daokunzhang/BinaryNE.
    CGX: Adaptive System Support for Communication-Efficient Deep Learning. (arXiv:2111.08617v5 [cs.DC] UPDATED)
    The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-to-point communication, and in particular via hardware bandwidth overprovisioning. Overprovisioning comes at a cost: there is an order of magnitude price difference between "cloud-grade" servers with such support, relative to their popular "consumer-grade" counterparts, although single server-grade and consumer-grade GPUs can have similar computational envelopes. In this paper, we show that the costly hardware overprovisioning approach can be supplanted via algorithmic and system design, and propose a framework called CGX, which provides efficient software support for compressed communication in ML applications, for both multi-GPU single-node training, as well as larger-scale multi-node training. CGX is based on two technical advances: \emph{At the system level}, it relies on a re-developed communication stack for ML frameworks, which provides flexible, highly-efficient support for compressed communication. \emph{At the application level}, it provides \emph{seamless, parameter-free} integration with popular frameworks, so that end-users do not have to modify training recipes, nor significant training code. This is complemented by a \emph{layer-wise adaptive compression} technique which dynamically balances compression gains with accuracy preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy.
    On the role of surrogates in the efficient estimation of treatment effects with limited outcome data. (arXiv:2003.12408v2 [stat.ML] UPDATED)
    In many investigations, the primary outcome of interest is difficult or expensive to collect. Examples include long-term health effects of medical interventions, measurements requiring expensive testing or follow-up, and outcomes only measurable on small panels as in marketing. This reduces effective sample sizes for estimating the average treatment effect (ATE). However, there is often an abundance of observations on surrogate outcomes not of primary interest, such as short-term health effects or online-ad click-through. We study the role of such surrogate observations in the efficient estimation of treatment effects. To quantify their value, we derive the semiparametric efficiency bounds on ATE estimation with and without the presence of surrogates and several intermediary settings. The difference between these characterizes the efficiency gains from optimally leveraging surrogates. We study two regimes: when the number of surrogate observations is comparable to primary-outcome observations and when the former dominates the latter. We take an agnostic missing-data approach circumventing strong surrogate conditions previously assumed. To leverage surrogates' efficiency gains, we develop efficient ATE estimation and inference based on flexible machine-learning estimates of nuisance functions appearing in the influence functions we derive. We empirically demonstrate the gains by studying the long-term earnings effect of job training.
    Multi-Modal Foundation Model for Simultaneous Comprehension of Molecular Structure and Properties. (arXiv:2211.10590v2 [cs.LG] UPDATED)
    Recently, deep learning approaches have been extensively studied for various problems in chemistry, such as property prediction, virtual screening, de novo molecule design, etc. Despite the impressive successes, separately designed networks for specific tasks are usually required for end-to-end training, so it is often difficult to acquire a unified principle to synergistically combine existing models and training datasets for novel tasks. To address this, here we present a novel multimodal chemical foundation model that can be used for various downstream tasks that require a simultaneous understanding of structure and property. Specifically, inspired by recent advances in pre-trained multi-modal foundation models such as Vision-Language Pretrained models (VLP), we proposed a novel structure-property multi-modal (SPMM) foundation model using the dual-stream transformer with X-shape attention, so that it can align the molecule structure and the chemical properties in a common embedding space. Thanks to the outstanding structure-property unimodal representation, experimental results confirm that SPMM can simultaneously perform molecule generation, property prediction, classification, reaction prediction, etc., which was previously not possible with a single architecture.
    Differentiating Student Feedbacks for Knowledge Tracing. (arXiv:2212.14695v1 [cs.CY])
    In computer-aided education and intelligent tutoring systems, knowledge tracing (KT) raises attention due to the development of data-driven learning methods, which aims to predict students' future performance given their past question response sequences to trace their knowledge states. However, current deep learning approaches only focus on enhancing prediction accuracy, but neglecting the discrimination imbalance of responses. That is, a considerable proportion of question responses are weak to discriminate students' knowledge states, but equally considered compared to other discriminative responses, thus hurting the ability of tracing students' personalized knowledge states. To tackle this issue, we propose DR4KT for Knowledge Tracing, which reweights the contribution of different responses according to their discrimination in training. For retaining high prediction accuracy on low discriminative responses after reweighting, DR4KT also introduces a discrimination-aware score fusion technique to make a proper combination between student knowledge mastery and the questions themselves. Comprehensive experimental results show that our DR4KT applied on four mainstream KT methods significantly improves their performance on three widely-used datasets.
    Ensemble of Loss Functions to Improve Generalizability of Deep Metric Learning methods. (arXiv:2107.01130v2 [cs.CV] UPDATED)
    Deep Metric Learning (DML) learns a non-linear semantic embedding from input data that brings similar pairs together while keeping dissimilar data away from each other. To this end, many different methods are proposed in the last decade with promising results in various applications. The success of a DML algorithm greatly depends on its loss function. However, no loss function is perfect, and it deals only with some aspects of an optimal similarity embedding. Besides, the generalizability of the DML on unseen categories during the test stage is an important matter that is not considered by existing loss functions. To address these challenges, we propose novel approaches to combine different losses built on top of a shared deep feature extractor. The proposed ensemble of losses enforces the deep model to extract features that are consistent with all losses. Since the selected losses are diverse and each emphasizes different aspects of an optimal semantic embedding, our effective combining methods yield a considerable improvement over any individual loss and generalize well on unseen categories. Here, there is no limitation in choosing loss functions, and our methods can work with any set of existing ones. Besides, they can optimize each loss function as well as its weight in an end-to-end paradigm with no need to adjust any hyper-parameter. We evaluate our methods on some popular datasets from the machine vision domain in conventional Zero-Shot-Learning (ZSL) settings. The results are very encouraging and show that our methods outperform all baseline losses by a large margin in all datasets.
    Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision. (arXiv:2109.02426v2 [cs.CV] UPDATED)
    General Continual Learning (GCL) aims at learning from non independent and identically distributed stream data without catastrophic forgetting of the old tasks that don't rely on task boundaries during both training and testing stages. We reveal that the relation and feature deviations are crucial problems for catastrophic forgetting, in which relation deviation refers to the deficiency of the relationship among all classes in knowledge distillation, and feature deviation refers to indiscriminative feature representations. To this end, we propose a Complementary Calibration (CoCa) framework by mining the complementary model's outputs and features to alleviate the two deviations in the process of GCL. Specifically, we propose a new collaborative distillation approach for addressing the relation deviation. It distills model's outputs by utilizing ensemble dark knowledge of new model's outputs and reserved outputs, which maintains the performance of old tasks as well as balancing the relationship among all classes. Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes. Extensive experiments on four popular datasets show that our CoCa framework achieves superior performance against state-of-the-art methods. Code is available at https://github.com/lijincm/CoCa.
    Reliable Agglomerative Clustering. (arXiv:1901.02063v5 [cs.LG] UPDATED)
    Standard agglomerative clustering suggests establishing a new reliable linkage at every step. However, in order to provide adaptive, density-consistent and flexible solutions, we study extracting all the reliable linkages at each step, instead of the smallest one. Such a strategy can be applied with all common criteria for agglomerative hierarchical clustering. We also study that this strategy with the single linkage criterion yields a minimum spanning tree algorithm. We perform experiments on several real-world datasets to demonstrate the performance of this strategy compared to the standard alternative.
    Guidance Through Surrogate: Towards a Generic Diagnostic Attack. (arXiv:2212.14875v1 [cs.LG])
    Adversarial training is an effective approach to make deep neural networks robust against adversarial attacks. Recently, different adversarial training defenses are proposed that not only maintain a high clean accuracy but also show significant robustness against popular and well studied adversarial attacks such as PGD. High adversarial robustness can also arise if an attack fails to find adversarial gradient directions, a phenomenon known as `gradient masking'. In this work, we analyse the effect of label smoothing on adversarial training as one of the potential causes of gradient masking. We then develop a guided mechanism to avoid local minima during attack optimization, leading to a novel attack dubbed Guided Projected Gradient Attack (G-PGA). Our attack approach is based on a `match and deceive' loss that finds optimal adversarial directions through guidance from a surrogate model. Our modified attack does not require random restarts, large number of attack iterations or search for an optimal step-size. Furthermore, our proposed G-PGA is generic, thus it can be combined with an ensemble attack strategy as we demonstrate for the case of Auto-Attack, leading to efficiency and convergence speed improvements. More than an effective attack, G-PGA can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.
    Active Learning of Driving Scenario Trajectories. (arXiv:2108.03217v2 [cs.LG] UPDATED)
    Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i.e. knowledge-based methods) may be prone to errors, such as false positive/negative classification of scenarios that lie on the border of two scenario classes, missing unknown scenario classes, or even failing to detect anomalies. On the other hand, verification of labels by annotators is not cost-efficient. For this purpose, active learning (AL) could potentially improve the annotation procedure by including an annotator/expert in an efficient way. In this study, we develop a generic active learning framework to annotate driving trajectory time series data. We first compute an embedding of the trajectories into a latent space in order to extract the temporal nature of the data. Given such an embedding, the framework becomes task agnostic since active learning can be performed using any classification method and any query strategy, regardless of the structure of the original time series data. Furthermore, we utilize our active learning framework to discover unknown driving scenario trajectories. This will ensure that previously unknown trajectory types can be effectively detected and included in the labeled dataset. We evaluate our proposed framework in different settings on novel real-world datasets consisting of driving trajectories collected by Volvo Cars Corporation. We observe that active learning constitutes an effective tool for labelling driving trajectories as well as for detecting unknown classes. Expectedly, the quality of the embedding plays an important role in the success of the proposed framework.
    Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search. (arXiv:2212.14849v1 [cs.LG])
    Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural networks have achieved competitive performance, the resulting policies are often over-parameterized black boxes that are difficult to interpret and deploy efficiently. More recent symbolic RL frameworks have shown that high-level domain-specific programming logic can be designed to handle both policy learning and symbolic planning. However, these approaches rely on coded primitives with little feature learning, and when applied to high-dimensional visual scenes, they can suffer from scalability issues and perform poorly when images have complex object interactions. To address these challenges, we propose \textit{Differentiable Symbolic Expression Search} (DiffSES), a novel symbolic learning approach that discovers discrete symbolic policies using partially differentiable optimization. By using object-level abstractions instead of raw pixel-level inputs, DiffSES is able to leverage the simplicity and scalability advantages of symbolic expressions, while also incorporating the strengths of neural networks for feature learning and optimization. Our experiments demonstrate that DiffSES is able to generate symbolic policies that are simpler and more and scalable than state-of-the-art symbolic RL methods, with a reduced amount of symbolic prior knowledge.
    Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis. (arXiv:2212.14623v1 [cs.LG])
    Through a study of multi-gas mixture datasets, we show that in multi-component spectral analysis, the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established, leading to a significant simplification of spectral quantification. Further, with the knowledge of the molar extinction coefficients of each constituent, a complete principal component set can be extracted from the coefficients directly, and few to none training samples are required for the learning model. Compared to other approaches, the proposed methods provide fast and accurate spectral quantification solutions with a small memory size needed.
    Condensed Representation of Machine Learning Data. (arXiv:2212.14229v1 [cs.LG])
    Training of a Machine Learning model requires sufficient data. The sufficiency of the data is not always about the quantity, but about the relevancy and reduced redundancy. Data-generating processes create massive amounts of data. When used raw, such big data is causing much computational resource utilization. Instead of using the raw data, a proper Condensed Representation can be used instead. Combining K-means, a well-known clustering method, with some correction and refinement facilities a novel Condensed Representation method for Machine Learning applications is introduced. To present the novel method meaningfully and visually, synthetically generated data is employed. It has been shown that by using the condensed representation, instead of the raw data, acceptably accurate model training is possible.
    Integral Probability Metrics PAC-Bayes Bounds. (arXiv:2207.00614v8 [stat.ML] UPDATED)
    We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). We provide instances of this bound with the IPM being the total variation metric and the Wasserstein distance. A notable feature of the obtained bounds is that they naturally interpolate between classical uniform convergence bounds in the worst case (when the prior and posterior are far away from each other), and improved bounds in favorable cases (when the posterior and prior are close). This illustrates the possibility of reinforcing classical generalization bounds with algorithm- and data-dependent components, thus making them more suitable to analyze algorithms that use a large hypothesis space.
    An Optimal Algorithm for Strongly Convex Min-min Optimization. (arXiv:2212.14439v1 [math.OC])
    In this paper we study the smooth strongly convex minimization problem $\min_{x}\min_y f(x,y)$. The existing optimal first-order methods require $\mathcal{O}(\sqrt{\max\{\kappa_x,\kappa_y\}} \log 1/\epsilon)$ of computations of both $\nabla_x f(x,y)$ and $\nabla_y f(x,y)$, where $\kappa_x$ and $\kappa_y$ are condition numbers with respect to variable blocks $x$ and $y$. We propose a new algorithm that only requires $\mathcal{O}(\sqrt{\kappa_x} \log 1/\epsilon)$ of computations of $\nabla_x f(x,y)$ and $\mathcal{O}(\sqrt{\kappa_y} \log 1/\epsilon)$ computations of $\nabla_y f(x,y)$. In some applications $\kappa_x \gg \kappa_y$, and computation of $\nabla_y f(x,y)$ is significantly cheaper than computation of $\nabla_x f(x,y)$. In this case, our algorithm substantially outperforms the existing state-of-the-art methods.
    Properties of Group Fairness Metrics for Rankings. (arXiv:2212.14351v1 [cs.LG])
    In recent years, several metrics have been developed for evaluating group fairness of rankings. Given that these metrics were developed with different application contexts and ranking algorithms in mind, it is not straightforward which metric to choose for a given scenario. In this paper, we perform a comprehensive comparative analysis of existing group fairness metrics developed in the context of fair ranking. By virtue of their diverse application contexts, we argue that such a comparative analysis is not straightforward. Hence, we take an axiomatic approach whereby we design a set of thirteen properties for group fairness metrics that consider different ranking settings. A metric can then be selected depending on whether it satisfies all or a subset of these properties. We apply these properties on eleven existing group fairness metrics, and through both empirical and theoretical results we demonstrate that most of these metrics only satisfy a small subset of the proposed properties. These findings highlight limitations of existing metrics, and provide insights into how to evaluate and interpret different fairness metrics in practical deployment. The proposed properties can also assist practitioners in selecting appropriate metrics for evaluating fairness in a specific application.
    Invertible normalizing flow neural networks by JKO scheme. (arXiv:2212.14424v1 [stat.ML])
    Normalizing flow is a class of deep generative models for efficient sampling and density estimation. In practice, the flow often appears as a chain of invertible neural network blocks; to facilitate training, existing works have regularized flow trajectories and designed special network architectures. The current paper develops a neural ODE flow network inspired by the Jordan-Kinderleherer-Otto (JKO) scheme, which allows efficient block-wise training of the residual blocks and avoids inner loops of score matching or variational learning. As the JKO scheme unfolds the dynamic of gradient flow, the proposed model naturally stacks residual network blocks one-by-one, reducing the memory load and difficulty of performing end-to-end training of deep flow networks. We also develop adaptive time reparameterization of the flow network with a progressive refinement of the trajectory in probability space, which improves the model training efficiency and accuracy in practice. Using numerical experiments with synthetic and real data, we show that the proposed JKO-iFlow model achieves similar or better performance in generating new samples compared with existing flow and diffusion models at a significantly reduced computational and memory cost.
    Falsification of Learning-Based Controllers through Multi-Fidelity Bayesian Optimization. (arXiv:2212.14118v1 [eess.SY])
    Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements. Because full-fidelity simulations can be computationally demanding, we investigate the use of simulators with different levels of fidelity. As a first step, we express the overall safety specification in terms of environmental parameters and structure this safety specification as an optimization problem. We propose a multi-fidelity falsification framework using Bayesian optimization, which is able to determine at which level of fidelity we should conduct a safety evaluation in addition to finding possible instances from the environment that cause the system to fail. This method allows us to automatically switch between inexpensive, inaccurate information from a low-fidelity simulator and expensive, accurate information from a high-fidelity simulator in a cost-effective way. Our experiments on various environments in simulation demonstrate that multi-fidelity Bayesian optimization has falsification performance comparable to single-fidelity Bayesian optimization but with much lower cost.
    Proof of Swarm Based Ensemble Learning for Federated Learning Applications. (arXiv:2212.14050v1 [cs.LG])
    Ensemble learning combines results from multiple machine learning models in order to provide a better and optimised predictive model with reduced bias, variance and improved predictions. However, in federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Hence, a mechanism is required to combine results of local models to produce a global model. Most distributed consensus algorithms, such as Byzantine fault tolerance (BFT), do not normally perform well in such applications. This is because, in such methods predictions of some of the peers are disregarded, so a majority of peers can win without even considering other peers' decisions. Additionally, the confidence score of the result of each peer is not normally taken into account, although it is an important feature to consider for ensemble learning. Moreover, the problem of a tie event is often left un-addressed by methods such as BFT. To fill these research gaps, we propose PoSw (Proof of Swarm), a novel distributed consensus algorithm for ensemble learning in a federated setting, which was inspired by particle swarm based algorithms for solving optimisation problems. The proposed algorithm is theoretically proved to always converge in a relatively small number of steps and has mechanisms to resolve tie events while trying to achieve sub-optimum solutions. We experimentally validated the performance of the proposed algorithm using ECG classification as an example application in healthcare, showing that the ensemble learning model outperformed all local models and even the FL-based global model. To the best of our knowledge, the proposed algorithm is the first attempt to make consensus over the output results of distributed models trained using federated learning.
    Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach. (arXiv:2212.14051v1 [eess.SP])
    6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techniques such as centralized power control can improve spectral efficiency in dense deployments of such subnetworks. However, existing solutions for centralized power control may require full channel state information (CSI) of all the desired and interfering links, which may be cumbersome and time-consuming to obtain in dense deployments. This paper presents a novel solution for centralized power control for industrial subnetworks based on Graph Neural Networks (GNNs). The proposed method only requires the subnetwork positioning information, usually known at the central controller, and the knowledge of the desired link channel gain during the execution phase. Simulation results show that our solution achieves similar spectral efficiency as the benchmark schemes requiring full CSI in runtime operations. Also, robustness to changes in the deployment density and environment characteristics with respect to the training phase is verified.  ( 2 min )
    Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques. (arXiv:2212.14071v1 [cs.LG])
    This study presents a general machine learning framework to estimate the traffic-measurement-level experience rate at given throughput values in the form of a Key Performance Indicator for the cells on base stations across various cities, using busy-hour counter data, and several technical parameters together with the network topology. Relying on feature engineering techniques, scores of additional predictors are proposed to enhance the effects of raw correlated counter values over the corresponding targets, and to represent the underlying interactions among groups of cells within nearby spatial locations effectively. An end-to-end regression modeling is applied on the transformed data, with results presented on unseen cities of varying sizes.  ( 2 min )
    On Transforming Reinforcement Learning by Transformer: The Development Trajectory. (arXiv:2212.14164v1 [cs.LG])
    Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are investigating ways to deploy transformers to reinforcement learning (RL) and the transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances on transforming RL by transformer (transformer-based RL or TRL), in order to explore its development trajectory and future trend. We group existing developments in two categories: architecture enhancement and trajectory optimization, and examine the main applications of TRL in robotic manipulation, text-based games, navigation and autonomous driving. For architecture enhancement, these methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, which model agents and environments much more precisely than deep RL methods, but they are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and "deadly triad". For trajectory optimization, these methods treat RL problems as sequence modeling and train a joint state-action model over entire trajectories under the behavior cloning framework, which are able to extract policies from static datasets and fully use the long-sequence modeling capability of the transformer. Given these advancements, extensions and challenges in TRL are reviewed and proposals about future direction are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing field.  ( 2 min )
    Investigating Sindy As a Tool For Causal Discovery In Time Series Signals. (arXiv:2212.14133v1 [cs.LG])
    The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.  ( 2 min )
  • Open

    Invertible normalizing flow neural networks by JKO scheme. (arXiv:2212.14424v1 [stat.ML])
    Normalizing flow is a class of deep generative models for efficient sampling and density estimation. In practice, the flow often appears as a chain of invertible neural network blocks; to facilitate training, existing works have regularized flow trajectories and designed special network architectures. The current paper develops a neural ODE flow network inspired by the Jordan-Kinderleherer-Otto (JKO) scheme, which allows efficient block-wise training of the residual blocks and avoids inner loops of score matching or variational learning. As the JKO scheme unfolds the dynamic of gradient flow, the proposed model naturally stacks residual network blocks one-by-one, reducing the memory load and difficulty of performing end-to-end training of deep flow networks. We also develop adaptive time reparameterization of the flow network with a progressive refinement of the trajectory in probability space, which improves the model training efficiency and accuracy in practice. Using numerical experiments with synthetic and real data, we show that the proposed JKO-iFlow model achieves similar or better performance in generating new samples compared with existing flow and diffusion models at a significantly reduced computational and memory cost.  ( 2 min )
    Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. (arXiv:2212.14319v1 [stat.ML])
    Partial differential equations (PDEs) are important tools to model physical systems, and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle, which works like a non-linear Fourier transform, to construct GP kernels mirroring standard spectral methods for GPs. Our approach can infer probable solutions of linear PDE systems from any data such as noisy measurements, or initial and boundary conditions. Constructing EPGP-priors is algorithmic, generally applicable, and comes with a sparse version (S-EPGP) that learns the relevant spectral frequencies and works better for big data sets. We demonstrate our approach on three families of systems of PDE, the heat equation, wave equation, and Maxwell's equations, where we improve upon the state of the art in computation time and precision, in some experiments by several orders of magnitude.  ( 2 min )
    Boosting Simple Learners. (arXiv:2001.11704v7 [cs.LG] UPDATED)
    Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. We study boosting under the assumption that the weak hypotheses belong to a class of bounded capacity. This assumption is inspired by the common convention that weak hypotheses are "rules-of-thumbs" from an "easy-to-learn class". (Schapire and Freund~'12, Shalev-Shwartz and Ben-David '14.) Formally, we assume the class of weak hypotheses has a bounded VC dimension. We focus on two main questions: (i) Oracle Complexity: How many weak hypotheses are needed to produce an accurate hypothesis? We design a novel boosting algorithm and demonstrate that it circumvents a classical lower bound by Freund and Schapire ('95, '12). Whereas the lower bound shows that $\Omega({1}/{\gamma^2})$ weak hypotheses with $\gamma$-margin are sometimes necessary, our new method requires only $\tilde{O}({1}/{\gamma})$ weak hypothesis, provided that they belong to a class of bounded VC dimension. Unlike previous boosting algorithms which aggregate the weak hypotheses by majority votes, the new boosting algorithm uses more complex ("deeper") aggregation rules. We complement this result by showing that complex aggregation rules are in fact necessary to circumvent the aforementioned lower bound. (ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class? Can complex concepts that are "far away" from the class be learned? Towards answering the first question we {introduce combinatorial-geometric parameters which capture expressivity in boosting.} As a corollary we provide an affirmative answer to the second question for well-studied classes, including half-spaces and decision stumps. Along the way, we establish and exploit connections with Discrepancy Theory.
    Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion. (arXiv:2212.14595v1 [stat.ML])
    Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. It is therefore of paramount importance to quantify the uncertainties associated to the inversion process to ease the subsequent interpretation and decision making processes. Within this framework of reference, sampling from a target posterior provides a fundamental approach to quantifying the uncertainty in seismic inversion. However, selecting appropriate prior information in a probabilistic inversion is crucial, yet non-trivial, as it influences the ability of a sampling-based inference in providing geological realism in the posterior samples. To overcome such limitations, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a CNN-based denoiser by means of the Plug-and-Play methods. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability in producing high-resolution, trustworthy samples representative of the subsurface structures, which we argue could be used for post-inference tasks such as reservoir modelling and history matching. To validate the proposed method, numerical tests are performed on both synthetic and field post-stack seismic data.
    PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models. (arXiv:2212.14838v1 [stat.ML])
    In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.
    Heterogeneous Synthetic Learner for Panel Data. (arXiv:2212.14580v1 [stat.ML])
    In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation methods focus on independently and identically distributed observations and cannot handle the non-stationarity and temporal dependency in the common panel data setting. The treatment evaluators developed for panel data, on the other hand, typically ignore the individualized information. To fill the gap, in this paper, we initialize the study of HTE estimation in panel data. Under different assumptions for HTE identifiability, we propose the corresponding heterogeneous one-side and two-side synthetic learner, namely H1SL and H2SL, by leveraging the state-of-the-art HTE estimator for non-panel data and generalizing the synthetic control method that allows flexible data generating process. We establish the convergence rates of the proposed estimators. The superior performance of the proposed methods over existing ones is demonstrated by extensive numerical studies.
    On Biased Compression for Distributed Learning. (arXiv:2002.12410v3 [cs.LG] UPDATED)
    In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact {\em biased} compressors often show superior performance in practice when compared to the much more studied and understood {\em unbiased} compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. We prove that distributed compressed SGD method, employed with error feedback mechanism, enjoys the ergodic rate $\mathcal{O}\left( \delta L \exp[-\frac{\mu K}{\delta L}] + \frac{(C + \delta D)}{K\mu}\right)$, where $\delta\ge1$ is a compression parameter which grows when more compression is applied, $L$ and $\mu$ are the smoothness and strong convexity constants, $C$ captures stochastic gradient noise ($C=0$ if full gradients are computed on each node) and $D$ captures the variance of the gradients at the optimum ($D=0$ for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose several new biased compressors with promising theoretical guarantees and practical performance.
    Lookback for Learning to Branch. (arXiv:2206.14987v2 [cs.LG] UPDATED)
    The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Program (MILP) solvers. Recent works have demonstrated the effectiveness of such GNNs in replacing the branching (variable selection) heuristic in branch-and-bound (B&B) solvers. These GNNs are trained, offline and on a collection of MILPs, to imitate a very good but computationally expensive branching heuristic, strong branching. Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure. Specifically, we find that with the strong branching heuristic, a child node's best choice was often the parent's second-best choice. We call this the "lookback" phenomenon. Surprisingly, the typical branching GNN of Gasse et al. (2019) often misses this simple "answer". To imitate the target behavior more closely by incorporating the lookback phenomenon in GNNs, we propose two methods: (a) target smoothing for the standard cross-entropy loss function, and (b) adding a Parent-as-Target (PAT) Lookback regularizer term. Finally, we propose a model selection framework to incorporate harder-to-formulate objectives such as solving time in the final models. Through extensive experimentation on standard benchmark instances, we show that our proposal results in up to 22% decrease in the size of the B&B tree and up to 15% improvement in the solving times.
    Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data. (arXiv:2212.14165v1 [stat.ME])
    Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biological markers and mechanisms underlying both prognosis and treatment, the precise cellular functions governing these complex mechanisms still need detailed and data-driven de-novo evaluations. We propose a framework called Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data (fiBAG), that allows simultaneous identification of upstream functional evidence of proteogenomic biomarkers and the incorporation of such knowledge in Bayesian variable selection models to improve signal detection. fiBAG employs a conflation of Gaussian process models to quantify (possibly non-linear) functional evidence via Bayes factors, which are then mapped to a novel calibrated spike-and-slab prior, thus guiding selection and providing functional relevance to the associations with patient outcomes. Using simulations, we illustrate how integrative methods with functional calibration have higher power to detect disease related markers than non-integrative approaches. We demonstrate the profitability of fiBAG via a pan-cancer analysis of 14 cancer types to identify and assess the cellular mechanisms of proteogenomic markers associated with cancer stemness and patient survival.
    Decoupled Self-supervised Learning for Graphs. (arXiv:2206.03601v2 [cs.LG] UPDATED)
    This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is homophilous, where linked nodes often belong to the same class or have similar features. However, such assumptions of homophily do not always hold in real-world graphs. We address this problem by developing a decoupled self-supervised learning (DSSL) framework for graph neural networks. DSSL imitates a generative process of nodes and links from latent variable modeling of the semantic structure, which decouples different underlying semantics between different neighborhoods into the self-supervised learning process. Our DSSL framework is agnostic to the encoders and does not need prefabricated augmentations, thus is flexible to different graphs. To effectively optimize the framework, we derive the evidence lower bound of the self-supervised objective and develop a scalable training algorithm with variational inference. We provide a theoretical analysis to justify that DSSL enjoys the better downstream performance. Extensive experiments on various types of graph benchmarks demonstrate that our proposed framework can achieve better performance compared with competitive baselines.
    The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data. (arXiv:2212.14514v1 [stat.ML])
    We consider the problem of estimating a multivariate function $f_0$ of bounded variation (BV), from noisy observations $y_i = f_0(x_i) + z_i$ made at random design points $x_i \in \mathbb{R}^d$, $i=1,\ldots,n$. We study an estimator that forms the Voronoi diagram of the design points, and then solves an optimization problem that regularizes according to a certain discrete notion of total variation (TV): the sum of weighted absolute differences of parameters $\theta_i,\theta_j$ (which estimate the function values $f_0(x_i),f_0(x_j)$) at all neighboring cells $i,j$ in the Voronoi diagram. This is seen to be equivalent to a variational optimization problem that regularizes according to the usual continuum (measure-theoretic) notion of TV, once we restrict the domain to functions that are piecewise constant over the Voronoi diagram. The regression estimator under consideration hence performs (shrunken) local averaging over adaptively formed unions of Voronoi cells, and we refer to it as the Voronoigram, following the ideas in Koenker (2005), and drawing inspiration from Tukey's regressogram (Tukey, 1961). Our contributions in this paper span both the conceptual and theoretical frontiers: we discuss some of the unique properties of the Voronoigram in comparison to TV-regularized estimators that use other graph-based discretizations; we derive the asymptotic limit of the Voronoi TV functional; and we prove that the Voronoigram is minimax rate optimal (up to log factors) for estimating BV functions that are essentially bounded.
    A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. (arXiv:2205.00403v2 [cs.LG] UPDATED)
    Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles. However their practicality in real-time, industrial-scale applications are limited due to the high memory and computational cost. Furthermore, ensembles and BNNs do not necessarily fix all the issues with the underlying member networks. In this work, we study principled approaches to improve uncertainty property of a single network, based on a single, deterministic representation. By formalizing the uncertainty quantification as a minimax learning problem, we first identify distance awareness, i.e., the model's ability to quantify the distance of a testing example from the training data, as a necessary condition for a DNN to achieve high-quality (i.e., minimax optimal) uncertainty estimation. We then propose Spectral-normalized Neural Gaussian Process (SNGP), a simple method that improves the distance-awareness ability of modern DNNs with two simple changes: (1) applying spectral normalization to hidden weights to enforce bi-Lipschitz smoothness in representations and (2) replacing the last output layer with a Gaussian process layer. On a suite of vision and language understanding benchmarks, SNGP outperforms other single-model approaches in prediction, calibration and out-of-domain detection. Furthermore, SNGP provides complementary benefits to popular techniques such as deep ensembles and data augmentation, making it a simple and scalable building block for probabilistic deep learning. Code is open-sourced at https://github.com/google/uncertainty-baselines
    Integral Probability Metrics PAC-Bayes Bounds. (arXiv:2207.00614v8 [stat.ML] UPDATED)
    We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). We provide instances of this bound with the IPM being the total variation metric and the Wasserstein distance. A notable feature of the obtained bounds is that they naturally interpolate between classical uniform convergence bounds in the worst case (when the prior and posterior are far away from each other), and improved bounds in favorable cases (when the posterior and prior are close). This illustrates the possibility of reinforcing classical generalization bounds with algorithm- and data-dependent components, thus making them more suitable to analyze algorithms that use a large hypothesis space.
    INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation. (arXiv:2212.14365v1 [cs.LG])
    Neural operators, which emerge as implicit solution operators of hidden governing equations, have recently become popular tools for learning responses of complex real-world physical systems. Nevertheless, the majority of neural operator applications has thus far been data-driven, which neglects the intrinsic preservation of fundamental physical laws in data. In this paper, we introduce a novel integral neural operator architecture, to learn physical models with fundamental conservation laws automatically guaranteed. In particular, by replacing the frame-dependent position information with its invariant counterpart in the kernel space, the proposed neural operator is by design translation- and rotation-invariant, and consequently abides by the conservation laws of linear and angular momentums. As applications, we demonstrate the expressivity and efficacy of our model in learning complex material behaviors from both synthetic and experimental datasets, and show that, by automatically satisfying these essential physical laws, our learned neural operator is not only generalizable in handling translated and rotated datasets, but also achieves state-of-the-art accuracy and efficiency as compared to baseline neural operator models.
    Unsupervised Representation Learning with Minimax Distance Measures. (arXiv:1904.13223v3 [cs.LG] UPDATED)
    We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the unknown underlying patterns and structures in the data. We develop a general-purpose and computationally efficient framework to employ Minimax distances with many machine learning methods that perform on numerical data. We study both computing the pairwise Minimax distances for all pairs of objects and as well as computing the Minimax distances of all the objects to/from a fixed (test) object. We first efficiently compute the pairwise Minimax distances between the objects, using the equivalence of Minimax distances over a graph and over a minimum spanning tree constructed on that. Then, we perform an embedding of the pairwise Minimax distances into a new vector space, such that their squared Euclidean distances in the new space equal to the pairwise Minimax distances in the original space. We also study the case of having multiple pairwise Minimax matrices, instead of a single one. Thereby, we propose an embedding via first summing up the centered matrices and then performing an eigenvalue decomposition to obtain the relevant features. In the following, we study computing Minimax distances from a fixed (test) object which can be used for instance in K-nearest neighbor search. Similar to the case of all-pair pairwise Minimax distances, we develop an efficient and general-purpose algorithm that is applicable with any arbitrary base distance measure. Moreover, we investigate in detail the edges selected by the Minimax distances and thereby explore the ability of Minimax distances in detecting outlier objects. Finally, for each setting, we perform several experiments to demonstrate the effectiveness of our framework.
    Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. (arXiv:2212.14449v1 [math.OC])
    Mean-field games have been used as a theoretical tool to obtain an approximate Nash equilibrium for symmetric and anonymous $N$-player games in literature. However, limiting applicability, existing theoretical results assume variations of a "population generative model", which allows arbitrary modifications of the population distribution by the learning algorithm. Instead, we show that $N$ agents running policy mirror ascent converge to the Nash equilibrium of the regularized game within $\tilde{\mathcal{O}}(\varepsilon^{-2})$ samples from a single sample trajectory without a population generative model, up to a standard $\mathcal{O}(\frac{1}{\sqrt{N}})$ error due to the mean field. Taking a divergent approach from literature, instead of working with the best-response map we first show that a policy mirror ascent map can be used to construct a contractive operator having the Nash equilibrium as its fixed point. Next, we prove that conditional TD-learning in $N$-agent games can learn value functions within $\tilde{\mathcal{O}}(\varepsilon^{-2})$ time steps. These results allow proving sample complexity guarantees in the oracle-free setting by only relying on a sample path from the $N$ agent simulator. Furthermore, we demonstrate that our methodology allows for independent learning by $N$ agents with finite sample guarantees.
    Relative Probability on Finite Outcome Spaces: A Systematic Examination of its Axiomatization, Properties, and Applications. (arXiv:2212.14555v1 [stat.ML])
    This work proposes a view of probability as a relative measure rather than an absolute one. To demonstrate this concept, we focus on finite outcome spaces and develop three fundamental axioms that establish requirements for relative probability functions. We then provide a library of examples of these functions and a system for composing them. Additionally, we discuss a relative version of Bayesian inference and its digital implementation. Finally, we prove the topological closure of the relative probability space, highlighting its ability to preserve information under limits.
    Predictor Selection for Synthetic Controls. (arXiv:2203.11576v2 [stat.ME] UPDATED)
    Synthetic control methods often rely on matching pre-treatment characteristics (called predictors) of the treated unit. The choice of predictors and how they are weighted plays a key role in the performance and interpretability of synthetic control estimators. This paper proposes the use of a sparse synthetic control procedure that penalizes the number of predictors used in generating the counterfactual to select the most important predictors. We derive, in a linear factor model framework, a new model selection consistency result and show that the penalized procedure has a faster mean squared error convergence rate. Through a simulation study, we then show that the sparse synthetic control achieves lower bias and has better post-treatment performance than the un-penalized synthetic control. Finally, we apply the method to revisit the study of the passage of Proposition 99 in California in an augmented setting with a large number of predictors available.
    Improving Certified Robustness via Statistical Learning with Logical Reasoning. (arXiv:2003.00120v7 [cs.LG] UPDATED)
    Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a limited perturbation radius. Given that existing pure data-driven statistical approaches have reached a bottleneck, in this paper, we propose to integrate statistical ML models with knowledge (expressed as logical rules) as a reasoning component using Markov logic networks (MLN, so as to further improve the overall certified robustness. This opens new research questions about certifying the robustness of such a paradigm, especially the reasoning component (e.g., MLN). As the first step towards understanding these questions, we first prove that the computational complexity of certifying the robustness of MLN is #P-hard. Guided by this hardness result, we then derive the first certified robustness bound for MLN by carefully analyzing different model regimes. Finally, we conduct extensive experiments on five datasets including both high-dimensional images and natural language texts, and we show that the certified robustness with knowledge-based logical reasoning indeed significantly outperforms that of the state-of-the-arts.
    Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations. (arXiv:2212.14411v1 [stat.ME])
    Sequential testing, always-valid $p$-values, and confidence sequences promise flexible statistical inference and on-the-fly decision making. However, unlike fixed-$n$ inference based on asymptotic normality, existing sequential tests either make parametric assumptions and end up under-covering/over-rejecting when these fail or use non-parametric but conservative concentration inequalities and end up over-covering/under-rejecting. To circumvent these issues, we sidestep exact at-least-$\alpha$ coverage and focus on asymptotically exact coverage and asymptotic optimality. That is, we seek sequential tests whose probability of ever rejecting a true hypothesis asymptotically approaches $\alpha$ and whose expected time to reject a false hypothesis approaches a lower bound on all tests with asymptotic coverage at least $\alpha$, both under an appropriate asymptotic regime. We permit observations to be both non-parametric and dependent and focus on testing whether the observations form a martingale difference sequence. We propose the universal sequential probability ratio test (uSPRT), a slight modification to the normal-mixture sequential probability ratio test, where we add a burn-in period and adjust thresholds accordingly. We show that even in this very general setting, the uSPRT is asymptotically optimal under mild generic conditions. We apply the results to stabilized estimating equations to test means, treatment effects, etc. Our results also provide corresponding guarantees for the implied confidence sequences. Numerical simulations verify our guarantees and the benefits of the uSPRT over alternatives.
    How do noise tails impact on deep ReLU networks?. (arXiv:2203.10418v2 [math.ST] UPDATED)
    This paper investigates the stability of deep ReLU neural networks for nonparametric regression under the assumption that the noise has only a finite p-th moment. We unveil how the optimal rate of convergence depends on p, the degree of smoothness and the intrinsic dimension in a class of nonparametric regression functions with hierarchical composition structure when both the adaptive Huber loss and deep ReLU neural networks are used. This optimal rate of convergence cannot be obtained by the ordinary least squares but can be achieved by the Huber loss with a properly chosen parameter that adapts to the sample size, smoothness, and moment parameters. A concentration inequality for the adaptive Huber ReLU neural network estimators with allowable optimization errors is also derived. To establish a matching lower bound within the class of neural network estimators using the Huber loss, we employ a different strategy from the traditional route: constructing a deep ReLU network estimator that has a better empirical loss than the true function and the difference between these two functions furnishes a low bound. This step is related to the Huberization bias, yet more critically to the approximability of deep ReLU networks. As a result, we also contribute some new results on the approximation theory of deep ReLU neural networks.
    On the role of surrogates in the efficient estimation of treatment effects with limited outcome data. (arXiv:2003.12408v2 [stat.ML] UPDATED)
    In many investigations, the primary outcome of interest is difficult or expensive to collect. Examples include long-term health effects of medical interventions, measurements requiring expensive testing or follow-up, and outcomes only measurable on small panels as in marketing. This reduces effective sample sizes for estimating the average treatment effect (ATE). However, there is often an abundance of observations on surrogate outcomes not of primary interest, such as short-term health effects or online-ad click-through. We study the role of such surrogate observations in the efficient estimation of treatment effects. To quantify their value, we derive the semiparametric efficiency bounds on ATE estimation with and without the presence of surrogates and several intermediary settings. The difference between these characterizes the efficiency gains from optimally leveraging surrogates. We study two regimes: when the number of surrogate observations is comparable to primary-outcome observations and when the former dominates the latter. We take an agnostic missing-data approach circumventing strong surrogate conditions previously assumed. To leverage surrogates' efficiency gains, we develop efficient ATE estimation and inference based on flexible machine-learning estimates of nuisance functions appearing in the influence functions we derive. We empirically demonstrate the gains by studying the long-term earnings effect of job training.
    Learning Representations from Dendrograms. (arXiv:1812.09225v4 [cs.LG] UPDATED)
    We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different distance measures and representations can be inferred from different types of dendrograms, level functions and distance functions. Via an appropriate embedding, we compute a vector-based representation of the inferred distances, in order to enable many numerical machine learning algorithms to employ such distances. Then, to address the model selection problem, we study the aggregation of different dendrogram-based distances respectively in solution space and in representation space in the spirit of deep representations. In the first approach, for example for the clustering problem, we build a graph with positive and negative edge weights according to the consistency of the clustering labels of different objects among different solutions, in the context of ensemble methods. Then, we use an efficient variant of correlation clustering to produce the final clusters. In the second approach, we investigate the combination of different distances and features sequentially in the spirit of multi-layered architectures to obtain the final features. Finally, we demonstrate the effectiveness of our approach via several numerical studies.
    Mixture of von Mises-Fisher distribution with sparse prototypes. (arXiv:2212.14591v1 [cs.LG])
    Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.
    An Entropy-Based Model for Hierarchical Learning. (arXiv:2212.14681v1 [stat.ML])
    Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, for a computer to learn from data accurately and efficiently, some auxiliary information about the data distribution and target function should be provided to it through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. In this paper, we propose a learning model that exploits this multiscale data structure and discuss its statistical and computational benefits. The hierarchical learning model is inspired by the logical and progressive easy-to-hard learning mechanism of human beings and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.
    Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping. (arXiv:2107.05341v3 [cs.LG] UPDATED)
    We explore the ability of overparameterized shallow neural networks to learn Lipschitz regression functions with and without label noise when trained by Gradient Descent (GD). To avoid the problem that in the presence of noisy labels, neural networks trained to nearly zero training error are inconsistent on this class, we propose an early stopping rule that allows us to show optimal rates. This provides an alternative to the result of Hu et al. (2021) who studied the performance of $\ell 2$ -regularized GD for training shallow networks in nonparametric regression which fully relied on the infinite-width network (Neural Tangent Kernel (NTK)) approximation. Here we present a simpler analysis which is based on a partitioning argument of the input space (as in the case of 1-nearest-neighbor rule) coupled with the fact that trained neural networks are smooth with respect to their inputs when trained by GD. In the noise-free case the proof does not rely on any kernelization and can be regarded as a finite-width result. In the case of label noise, by slightly modifying the proof, the noise is controlled using a technique of Yao, Rosasco, and Caponnetto (2007).
    Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. (arXiv:2202.07511v3 [cs.LG] UPDATED)
    We study episodic two-player zero-sum Markov games (MGs) in the offline setting, where the goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset collected a priori. When the dataset does not have uniform coverage over all policy pairs, finding an approximate NE involves challenges in three aspects: (i) distributional shift between the behavior policy and the optimal policy, (ii) function approximation to handle large state space, and (iii) minimax optimization for equilibrium solving. We propose a pessimism-based algorithm, dubbed as pessimistic minimax value iteration (PMVI), which overcomes the distributional shift by constructing pessimistic estimates of the value functions for both players and outputs a policy pair by solving NEs based on the two value functions. Furthermore, we establish a data-dependent upper bound on the suboptimality which recovers a sublinear rate without the assumption on uniform coverage of the dataset. We also prove an information-theoretical lower bound, which suggests that the data-dependent term in the upper bound is intrinsic. Our theoretical results also highlight a notion of "relative uncertainty", which characterizes the necessary and sufficient condition for achieving sample efficiency in offline MGs. To the best of our knowledge, we provide the first nearly minimax optimal result for offline MGs with function approximation.
    Eliminating Meta Optimization Through Self-Referential Meta Learning. (arXiv:2212.14392v1 [cs.LG])
    Meta Learning automates the search for learning algorithms. At the same time, it creates a dependency on human engineering on the meta-level, where meta learning algorithms need to be designed. In this paper, we investigate self-referential meta learning systems that modify themselves without the need for explicit meta optimization. We discuss the relationship of such systems to in-context and memory-based meta learning and show that self-referential neural networks require functionality to be reused in the form of parameter sharing. Finally, we propose fitness monotonic execution (FME), a simple approach to avoid explicit meta optimization. A neural network self-modifies to solve bandit and classic control tasks, improves its self-modifications, and learns how to learn, purely by assigning more computational resources to better performing solutions.
    Do Bayesian Variational Autoencoders Know What They Don't Know?. (arXiv:2212.14272v1 [stat.ML])
    The problem of detecting the Out-of-Distribution (OoD) inputs is of paramount importance for Deep Neural Networks. It has been previously shown that even Deep Generative Models that allow estimating the density of the inputs may not be reliable and often tend to make over-confident predictions for OoDs, assigning to them a higher density than to the in-distribution data. This over-confidence in a single model can be potentially mitigated with Bayesian inference over the model parameters that take into account epistemic uncertainty. This paper investigates three approaches to Bayesian inference: stochastic gradient Markov chain Monte Carlo, Bayes by Backpropagation, and Stochastic Weight Averaging-Gaussian. The inference is implemented over the weights of the deep neural networks that parameterize the likelihood of the Variational Autoencoder. We empirically evaluate the approaches against several benchmarks that are often used for OoD detection: estimation of the marginal likelihood utilizing sampled model ensemble, typicality test, disagreement score, and Watanabe-Akaike Information Criterion. Finally, we introduce two simple scores that demonstrate the state-of-the-art performance.
    Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery. (arXiv:2212.14562v1 [math.ST])
    This paper studies the quantization of heavy-tailed data in some fundamental statistical estimation problems, where the underlying distributions have bounded moments of some order. We propose to truncate and properly dither the data prior to a uniform quantization. Our major standpoint is that (near) minimax rates of estimation error are achievable merely from the quantized data produced by the proposed scheme. In particular, concrete results are worked out for covariance estimation, compressed sensing, and matrix completion, all agreeing that the quantization only slightly worsens the multiplicative factor. Besides, we study compressed sensing where both covariate (i.e., sensing vector) and response are quantized. Under covariate quantization, although our recovery program is non-convex because the covariance matrix estimator lacks positive semi-definiteness, all local minimizers are proved to enjoy near optimal error bound. Moreover, by the concentration inequality of product process and covering argument, we establish near minimax uniform recovery guarantee for quantized compressed sensing with heavy-tailed noise.
    An Instrumental Variable Approach to Confounded Off-Policy Evaluation. (arXiv:2212.14468v1 [stat.ML])
    Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy. In some cases, there may be unmeasured variables that can confound the action-reward or action-next-state relationships, rendering many existing OPE approaches ineffective. This paper develops an instrumental variable (IV)-based method for consistent OPE in confounded Markov decision processes (MDPs). Similar to single-stage decision making, we show that IV enables us to correctly identify the target policy's value in infinite horizon settings as well. Furthermore, we propose an efficient and robust value estimator and illustrate its effectiveness through extensive simulations and analysis of real data from a world-leading short-video platform.
    Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis. (arXiv:2212.14507v1 [cs.LG])
    Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.
    Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks. (arXiv:2009.10835v3 [cs.LG] UPDATED)
    We study different aspects of active learning with deep neural networks in a consistent and unified way. i) We investigate incremental and cumulative training modes which specify how the newly labeled data are used for training. ii) We study active learning w.r.t. the model configurations such as the number of epochs and neurons as well as the choice of batch size. iii) We consider in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying procedures. iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning. v) We investigate how active learning with neural networks can benefit from pseudo-labels as proxies for actual labels.
    Reducing Certified Regression to Certified Classification for General Poisoning Attacks. (arXiv:2208.13904v2 [cs.LG] UPDATED)
    Adversarial training instances can severely distort a model's behavior. This work investigates certified regression defenses, which provide guaranteed limits on how much a regressor's prediction may change under a poisoning attack. Our key insight is that certified regression reduces to voting-based certified classification when using median as a model's primary decision function. Coupling our reduction with existing certified classifiers, we propose six new regressors provably-robust to poisoning attacks. To the extent of our knowledge, this is the first work that certifies the robustness of individual regression predictions without any assumptions about the data distribution and model architecture. We also show that the assumptions made by existing state-of-the-art certified classifiers are often overly pessimistic. We introduce a tighter analysis of model robustness, which in many cases results in significantly improved certified guarantees. Lastly, we empirically demonstrate our approaches' effectiveness on both regression and classification data, where the accuracy of up to 50% of test predictions can be guaranteed under 1% training set corruption and up to 30% of predictions under 4% corruption. Our source code is available at https://github.com/ZaydH/certified-regression.
    Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control?. (arXiv:2212.14511v1 [cs.LG])
    We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a direct latent model learning approach, where a dynamic model in some latent state space is learned by predicting quantities directly related to planning (e.g., costs) without reconstructing the observations. In particular, we focus on an intuitive cost-driven state representation learning method for solving Linear Quadratic Gaussian (LQG) control, one of the most fundamental partially observable control problems. As our main results, we establish finite-sample guarantees of finding a near-optimal state representation function and a near-optimal controller using the directly learned latent model. To the best of our knowledge, despite various empirical successes, prior to this work it was unclear if such a cost-driven latent model learner enjoys finite-sample guarantees. Our work underscores the value of predicting multi-step costs, an idea that is key to our theory, and notably also an idea that is known to be empirically valuable for learning state representations.
    Langevin algorithms for very deep Neural Networks with application to image classification. (arXiv:2212.14718v1 [cs.LG])
    Training a very deep neural network is a challenging task, as the deeper a neural network is, the more non-linear it is. We compare the performances of various preconditioned Langevin algorithms with their non-Langevin counterparts for the training of neural networks of increasing depth. For shallow neural networks, Langevin algorithms do not lead to any improvement, however the deeper the network is and the greater are the gains provided by Langevin algorithms. Adding noise to the gradient descent allows to escape from local traps, which are more frequent for very deep neural networks. Following this heuristic we introduce a new Langevin algorithm called Layer Langevin, which consists in adding Langevin noise only to the weights associated to the deepest layers. We then prove the benefits of Langevin and Layer Langevin algorithms for the training of popular deep residual architectures for image classification.  ( 2 min )
    Resampling Sensitivity of High-Dimensional PCA. (arXiv:2212.14531v1 [math.ST])
    The study of stability and sensitivity of statistical methods or algorithms with respect to their data is an important problem in machine learning and statistics. The performance of the algorithm under resampling of the data is a fundamental way to measure its stability and is closely related to generalization or privacy of the algorithm. In this paper, we study the resampling sensitivity for the principal component analysis (PCA). Given an $ n \times p $ random matrix $ \mathbf{X} $, let $ \mathbf{X}^{[k]} $ be the matrix obtained from $ \mathbf{X} $ by resampling $ k $ randomly chosen entries of $ \mathbf{X} $. Let $ \mathbf{v} $ and $ \mathbf{v}^{[k]} $ denote the principal components of $ \mathbf{X} $ and $ \mathbf{X}^{[k]} $. In the proportional growth regime $ p/n \to \xi \in (0,1] $, we establish the sharp threshold for the sensitivity/stability transition of PCA. When $ k \gg n^{5/3} $, the principal components $ \mathbf{v} $ and $ \mathbf{v}^{[k]} $ are asymptotically orthogonal. On the other hand, when $ k \ll n^{5/3} $, the principal components $ \mathbf{v} $ and $ \mathbf{v}^{[k]} $ are asymptotically colinear. In words, we show that PCA is sensitive to the input data in the sense that resampling even a negligible portion of the input may completely change the output.  ( 2 min )
    Comparative Analysis of Clustering Techniques for Personalized Food Kit Distribution. (arXiv:2212.14874v1 [cs.LG])
    The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.  ( 2 min )
    A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators. (arXiv:2212.14163v1 [stat.ML])
    Kernels are efficient in representing nonlocal dependence and they are widely used to design operators between function spaces. Thus, learning kernels in operators from data is an inverse problem of general interest. Due to the nonlocal dependence, the inverse problem can be severely ill-posed with a data-dependent singular inversion operator. The Bayesian approach overcomes the ill-posedness through a non-degenerate prior. However, a fixed non-degenerate prior leads to a divergent posterior mean when the observation noise becomes small, if the data induces a perturbation in the eigenspace of zero eigenvalues of the inversion operator. We introduce a data-adaptive prior to achieve a stable posterior whose mean always has a small noise limit. The data-adaptive prior's covariance is the inversion operator with a hyper-parameter selected adaptive to data by the L-curve method. Furthermore, we provide a detailed analysis on the computational practice of the data-adaptive prior, and demonstrate it on Toeplitz matrices and integral operators. Numerical tests show that a fixed prior can lead to a divergent posterior mean in the presence of any of the four types of errors: discretization error, model error, partial observation and wrong noise assumption. In contrast, the data-adaptive prior always attains posterior means with small noise limits.  ( 2 min )
    On Undersmoothing and Sample Splitting for Estimating a Doubly Robust Functional. (arXiv:2212.14857v1 [math.ST])
    We consider the problem of constructing minimax rate-optimal estimators for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature. Minimax rate-optimal estimators for such functionals are typically constructed through higher-order bias corrections of plug-in and one-step type estimators and, in turn, depend on estimators of nuisance functions. In this paper, we consider a parallel question of interest regarding the optimality and/or sub-optimality of plug-in and one-step bias-corrected estimators for the specific doubly robust functional of interest. Specifically, we verify that by using undersmoothing and sample splitting techniques when constructing nuisance function estimators, one can achieve minimax rates of convergence in all H\"older smoothness classes of the nuisance functions (i.e. the propensity score and outcome regression) provided that the marginal density of the covariates is sufficiently regular. Additionally, by demonstrating suitable lower bounds on these classes of estimators, we demonstrate the necessity to undersmooth the nuisance function estimators to obtain minimax optimal rates of convergence.  ( 2 min )
    Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap. (arXiv:2201.04469v8 [stat.ML] UPDATED)
    We consider fixed-budget best-arm identification in two-armed Gaussian bandit problems. One of the longstanding open questions is the existence of an optimal strategy under which the probability of misidentification matches a lower bound. We show that a strategy following the Neyman allocation rule (Neyman, 1934) is asymptotically optimal when the gap between the expected rewards is small. First, we review a lower bound derived by Kaufmann et al. (2016). Then, we propose the "Neyman Allocation (NA)-Augmented Inverse Probability weighting (AIPW)" strategy, which consists of the sampling rule using the Neyman allocation with an estimated standard deviation and the recommendation rule using an AIPW estimator. Our proposed strategy is optimal because the upper bound matches the lower bound when the budget goes to infinity and the gap goes to zero.  ( 2 min )
    Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net. (arXiv:2212.14194v1 [math.ST])
    Sparse principal component analysis (SPCA) has been widely used for dimensionality reduction and feature extraction in high-dimensional data analysis. Despite there are many methodological and theoretical developments in the past two decades, the theoretical guarantees of the popular SPCA algorithm proposed by Zou, Hastie & Tibshirani (2006) based on the elastic net are still unknown. We aim to close this important theoretical gap in this paper. We first revisit the SPCA algorithm of Zou et al. (2006) and present our implementation. Also, we study a computationally more efficient variant of the SPCA algorithm in Zou et al. (2006) that can be considered as the limiting case of SPCA. We provide the guarantees of convergence to a stationary point for both algorithms. We prove that, under a sparse spiked covariance model, both algorithms can recover the principal subspace consistently under mild regularity conditions. We show that their estimation error bounds match the best available bounds of existing works or the minimax rates up to some logarithmic factors. Moreover, we demonstrate the numerical performance of both algorithms in simulation studies.  ( 2 min )
    Quantile Off-Policy Evaluation via Deep Conditional Generative Learning. (arXiv:2212.14466v1 [stat.ML])
    Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy. It is critical in a number of sequential decision making problems ranging from healthcare to technology industries. Most of the work in existing literature is focused on evaluating the mean outcome of a given policy, and ignores the variability of the outcome. However, in a variety of applications, criteria other than the mean may be more sensible. For example, when the reward distribution is skewed and asymmetric, quantile-based metrics are often preferred for their robustness. In this paper, we propose a doubly-robust inference procedure for quantile OPE in sequential decision making and study its asymptotic properties. In particular, we propose utilizing state-of-the-art deep conditional generative learning methods to handle parameter-dependent nuisance function estimation. We demonstrate the advantages of this proposed estimator through both simulations and a real-world dataset from a short-video platform. In particular, we find that our proposed estimator outperforms classical OPE estimators for the mean in settings with heavy-tailed reward distributions.  ( 2 min )
    Bayesian Interpolation with Deep Linear Networks. (arXiv:2212.14457v1 [stat.ML])
    This article concerns Bayesian inference using deep linear networks with output dimension one. In the interpolating (zero noise) regime we show that with Gaussian weight priors and MSE negative log-likelihood loss both the predictive posterior and the Bayesian model evidence can be written in closed form in terms of a class of meromorphic special functions called Meijer-G functions. These results are non-asymptotic and hold for any training dataset, network depth, and hidden layer widths, giving exact solutions to Bayesian interpolation using a deep Gaussian process with a Euclidean covariance at each layer. Through novel asymptotic expansions of Meijer-G functions, a rich new picture of the role of depth emerges. Specifically, we find: ${\bf \text{The role of depth in extrapolation}}$: The posteriors in deep linear networks with data-independent priors are the same as in shallow networks with evidence maximizing data-dependent priors. In this sense, deep linear networks make provably optimal predictions. ${\bf \text{The role of depth in model selection}}$: Starting from data-agnostic priors, Bayesian model evidence in wide networks is only maximized at infinite depth. This gives a principled reason to prefer deeper networks (at least in the linear case). ${\bf \text{Scaling laws relating depth, width, and number of datapoints}}$: With data-agnostic priors, a novel notion of effective depth given by \[\#\text{hidden layers}\times\frac{\#\text{training data}}{\text{network width}}\] determines the Bayesian posterior in wide linear networks, giving rigorous new scaling laws for generalization error.  ( 2 min )
    Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection Criteria. (arXiv:2212.14074v1 [cs.CL])
    Selecting the number of topics in LDA models is considered to be a difficult task, for which alternative approaches have been proposed. The performance of the recently developed singular Bayesian information criterion (sBIC) is evaluated and compared to the performance of alternative model selection criteria. The sBIC is a generalization of the standard BIC that can be implemented to singular statistical models. The comparison is based on Monte Carlo simulations and carried out for several alternative settings, varying with respect to the number of topics, the number of documents and the size of documents in the corpora. Performance is measured using different criteria which take into account the correct number of topics, but also whether the relevant topics from the DGPs are identified. Practical recommendations for LDA model selection in applications are derived.  ( 2 min )
    Reliable Agglomerative Clustering. (arXiv:1901.02063v5 [cs.LG] UPDATED)
    Standard agglomerative clustering suggests establishing a new reliable linkage at every step. However, in order to provide adaptive, density-consistent and flexible solutions, we study extracting all the reliable linkages at each step, instead of the smallest one. Such a strategy can be applied with all common criteria for agglomerative hierarchical clustering. We also study that this strategy with the single linkage criterion yields a minimum spanning tree algorithm. We perform experiments on several real-world datasets to demonstrate the performance of this strategy compared to the standard alternative.  ( 2 min )
    Robust Bayesian Subspace Identification for Small Data Sets. (arXiv:2212.14132v1 [eess.SY])
    Model estimates obtained from traditional subspace identification methods may be subject to significant variance. This elevated variance is aggravated in the cases of large models or of a limited sample size. Common solutions to reduce the effect of variance are regularized estimators, shrinkage estimators and Bayesian estimation. In the current work we investigate the latter two solutions, which have not yet been applied to subspace identification. Our experimental results show that our proposed estimators may reduce the estimation risk up to $40\%$ of that of traditional subspace methods.  ( 2 min )
    Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis. (arXiv:2212.14623v1 [cs.LG])
    Through a study of multi-gas mixture datasets, we show that in multi-component spectral analysis, the number of functional or non-functional principal components required to retain the essential information is the same as the number of independent constituents in the mixture set. Due to the mutual in-dependency among different gas molecules, near one-to-one projection from the principal component to the mixture constituent can be established, leading to a significant simplification of spectral quantification. Further, with the knowledge of the molar extinction coefficients of each constituent, a complete principal component set can be extracted from the coefficients directly, and few to none training samples are required for the learning model. Compared to other approaches, the proposed methods provide fast and accurate spectral quantification solutions with a small memory size needed.  ( 2 min )
    Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent. (arXiv:2212.14883v1 [stat.ML])
    With the fast development of big data, it has been easier than before to learn the optimal decision rule by updating the decision rule recursively and making online decisions. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes of the stochastic gradient and establish the asymptotic normality of the parameter estimator. Our proposed estimator significantly improves the asymptotic efficiency over the previous averaged SGD approach via inverse probability weights. We also conduct an optimality analysis on the weights in a linear regression setting. We provide a Bahadur representation of the proposed estimator and show that the remainder term in the Bahadur representation entails a slower convergence rate compared to classical SGD due to the adaptive data collection.  ( 2 min )

  • Open

    [P] Hierarchical model test
    I am trying to build a hierarchial model, for this I am training a hierarchical tree and each node will have a model for that specific hierarchy. My question is: how will I choose the best model for each node? My idea was to test using the fold I split (I'm using k-fold), but what data will I use to test the final tree later? submitted by /u/gr_ferro [link] [comments]  ( 61 min )
    [D] Tools to find largest clusters in vector database?
    I'm new to embedding and vector databases. I had this initial thought that I'd be able to provide some sort of parameter that was used as a threshold to determine the cutoff between what a large cluster and what isn't, and that I could just get back a summary of those top clusters and try to then sift through them to figure out what each cluster represented. The more I dig in, I'm not really able t find a way to do this. Seems like vector databases, like Pinecone, expect you to provide it some query vectors to get what you want. Are there any tools to help me easily grab the largest clusters in my vector database versus me seeking them out? submitted by /u/joe_04_04 [link] [comments]  ( 63 min )
    [D] life advice to relatively late bloomer ML theory researcher.
    Hi ML Community out there, I'm 27 M. I'm a machine learning incoming PhD student based out of Germany. I have been trying to get into a PhD program since last 4 years (since 2018). When I gave up and got into a masters instead with 3yoe. Now I'm about to complete my masters thesis and start a PhD. I already have mixed feelings about my PhD journey now. I have gotten a really good opportunity to do ML theory at University of Saarlandes in Germany. Initially, when I had started, I was really driven to understand maths and underlying concepts of ML. However, more recently I have become more confused about the value of theoretical research and it's "real" impact. Of what skills I would have in my career later that would make me desirable to employers (considering I may not get tenured in academia). Is it more advisable in long run to stay and get your PhD or just leave and join some ML role in industry that takes Masters guys and get some real world experience on how to use ML to generate business value ? There is this added fomo of being 27 and just starting my PhD where I lag in the advantage of age to take high risk bets on things. Doing a PhD in theoretical ML could possibly mean I am very likely to be only employable in select places and this gives me a fear of trying to reinvent myself in my mid 30s. Any suggestions on pros and cons of ML research in academia vs a ML industry job of a masters grad would be really helpful! submitted by /u/notyourregularnerd [link] [comments]  ( 65 min )
    [P] Using machine learning to correct geometrical distortion in images
    Hello, I am a phd student working on a project that revolves around the correction of geometrical distortions on images, more specifically the goal is to correct cylindrical distortions in QR Codes in order to improve decoding sucess rate. So far I implemented traditional methods and got interesting results, but I'm now interested in using machine learning to tackle this problem and since I'm still relatively new to machine learning I would like to hear your feedback/opinions on the subject aswell as sugestions on reading material to start. So far from my limited research on the matter, I believe a generative adversarial network would probably be the right choice for this problem, but again I'm not sure and I'm really open to all sugestions/ideas. submitted by /u/LordChips4 [link] [comments]  ( 62 min )
    [Discussion] Is there any open-source alternative to voice.ai ? Looking for open-source speech to speech AI
    Hello everyone ! I recently heard about Voice.AI, but I hate that it's behind a paywall and not open. Are there any open-source alternatives to it ? Thanks ! submitted by /u/FoxTrotte [link] [comments]  ( 61 min )
    [D] Updated model comparison techniques review
    Anyone knows an updated review of model comparison techniques, like the review that we can see in this one of Raschka: Model Evaluation, Model Selection, and Algorithm Selection in Machine Learningor this other one from Dietterich: Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Thanks a lot in advance. submitted by /u/Party-Worldliness-72 [link] [comments]  ( 61 min )
    [P] An old fashioned statistician is looking for other ways to analyse survival data - Is machine learning an option?
    So I will directly dive into the problem setting and will later describe the background: data: survival data of ~3000 patients with several clinical and lab(blood) parameters. question: does 1 of the parameters has any influence on the survival time? what I have done so far: non proportional multivariate hazard model (Cox regression) problem: highly correlated variables, strong time interaction, some hardly not normal distributed variables (even after transformation) QUESTION: Is there a machine learning / AI solution for this problem question? background: I am a PhD student in medicine and did intensive mathematics together with my colleagues. But we only had one „old-fashioned“ statistics professor, who answered us for our problem: „seems like your data isn‘t good enough, and you can‘t explore something there, cause its far too complex“ We first want to get an intuition if our theoretical findings can be proved in the data, before we will plan a new study. I reformulated our problem a bit, we are not dealing with the death of patients, but with time of an specific event. I am really grateful for any ideas, any sources where to look at and everything which could help 😊😊 Thanks in advance! submitted by /u/lattecoffeegirl [link] [comments]  ( 64 min )
    [P] Can I have a prediction consider specific features?
    How do I increase my linear regression model’s ability to consider a feature into the results? I have two features that both contain the desired outputs from the prediction and I would like to somehow get them to overlap their results. When I check for corr they do not share the same correlating features and when I predict X on each their results are almost contrasting even though predicting ok feature 1 produces more accurate results. submitted by /u/Mustang1011 [link] [comments]  ( 67 min )
    [P] Live Coding Tutorial - Diffusion Models from Scratch
    Hello, community! I invite you to take a look at the live coding tutorial video on Diffusion Models Link to the video Content covered: - Theoretical background - Implementation of forward diffusion process - Implementation of the training loop - Overfitting to one batch - Implementation of the reverse diffusion process - Training on CIFAR10 dataset (with class label conditioning) submitted by /u/dtransposed [link] [comments]  ( 60 min )
    [D] Questions about IWAE (Importance Weighted Autoencoders)
    I'm new in this field and just see the paper Importance Weighted Autoencoders (https://arxiv.org/abs/1509.00519). While reading the paper I have 2 questions and I think a lot and also googled, but cannot find any clues or answers so it would be great if someone can help! ​ Why the second term q(h|x) encourages the encoder to have a spread-out distribution? To maximize the objective or minimize the loss, the encoder q(h|x) will be trained to be small. I don't know how this could be connected to spread-out distribution ​ https://preview.redd.it/81ykicq3vm9a1.png?width=1796&format=png&auto=webp&s=f81581013e93286667be44df53b51ecbc77e46a6 How to measure activity of latent dimension? I roughly know about covariance, so this is the first time about the notation of Covariance_{x} and I don't understand how to calculate it. I can understand E_{u~q(u|x)}(u) which is expectation of specific latent dimension given input x. But what do I have to do next? I thought about the concept 'distribution to change depending on the observation', measuring the variance of specific latent dimension of all the input x but it's notation is not right. How can I calculate this one? ​ https://preview.redd.it/ltxxm451vm9a1.png?width=1770&format=png&auto=webp&s=19c8466b8b6c12b7391698daa6347f6786b249a5 submitted by /u/ML_Newbie95 [link] [comments]  ( 65 min )
    [D] RFC-0030: Proposal of fp8 dtype introduction to PyTorch
    Today a PR opened to Pytorch to formally introduce the FP8 data type. Current text: Proposal of fp8 dtype introduction to PyTorch PR: https://github.com/pytorch/rfcs/pull/51 Summary More and more companies working on Deep Learning accelerators are experimenting with 8-bit floating point numbers usage in training and inference. Results of these experiments are presented in many papers published in the last few years. Since fp8 data type seems to be a natural evolution of currently used fp16/bf16, to reduce computation of big DL models, it’s worth to standardize this type. Few attempts of this were done recently: Nvidia, Arm and Intel - https://arxiv.org/pdf/2209.05433.pdf GraphCore and AMD - https://arxiv.org/pdf/2206.02915.pdf Tesla - https://tesla-cdn.thron.com/static/MXMU3S_tesla-dojo-technology_1WDVZN.pdf This RFC proposes adding two 8-bit floating point data types variants to PyTorch, based on the Nvidia/Arm/Intel paper. It’s important to consider these two variants, because they’re already known to be used by Nvidia H100 and Intel Gaudi2 accelerators. submitted by /u/Balance- [link] [comments]  ( 62 min )
    [D] What do you do while you wait for training?
    I was wondering what each individual does while they wait for the model to train because thats what I am waiting for (ETA 5 DAYS) submitted by /u/hollow_sets [link] [comments]  ( 64 min )
    [R] Pyramid adversarial attack with PyTorch
    Here is a PyTorch reproduced version of PyramidAT. Repository link: https://github.com/kdhht2334/Pyramid_AT (Original) Paper link: https://arxiv.org/abs/2111.15121 submitted by /u/Beginning_Distance38 [link] [comments]  ( 61 min )
    [P] Language Model that works on any given body of information
    I need a model that can answer questions regarding a given text (1-2 pages long) in a conversational manner (e.g. ChatGPT) with no additional training/fine-tuning for each different text. In many LLMs, an easy way to achieve this is to add the given text as a "header" in the initial prompt (e.g. as ChatGPT authors do). However, I guess there's a limit on the length of such a prompt, after which model performance degrades. Of course, if there's a model that can handle prompt "headers" of such length that would work fine for me. Can you point me to anything related to this task? submitted by /u/fedetask [link] [comments]  ( 60 min )
    [D] Models that extract action items from conversation
    I am interested in the literature or seeing some examples of models capable of extracting discrete action items from text conversations. Action items can be making an appointment, starting a call, etc. For example, Gmail is able to recognize when people are arranging an appointment in an email chat and it suggests that to be added to the calendar (action item) What type of models perform such tasks? Could you point me to the relevant literature? submitted by /u/fedetask [link] [comments]  ( 62 min )
    [D] Machine Learning Illustrations
    Hey guys! I recently published a website containing machine-learning illustrations! They aim to be "that" resource on which you can rely whenever you need to brush-up on these topics (e.g. technical interviews, exams, or whatever). https://illustrated-machine-learning.github.io/ Other than just spamming the link, I was curious to receive some feedbacks about the website and the illustrations. 😁 I really struggled to find a valid single resource anytime I need something across different topics, therefore my ambition is to create it! Update: Examples of illustrations are: svm: https://illustrated-machine-learning.github.io/pages/machine-learning/linear-algorithms.html#support-vector-machines bias-variance: https://illustrated-machine-learning.github.io/pages/machine-learning/bias-variance.html decision-tree: https://illustrated-machine-learning.github.io/pages/machine-learning/decision-tree.html Update 2.0 Thank you for your feedbacks! I already (brutally) solved your main concern about the list of contests not visible. I will spend more time on it as soon as I can, in order to make it smoother and better! Update 3.0 Prooobably I finally understood your problems with the sidebar button, and I tried to make more visible by making it darker and bigger. Is that better now? submitted by /u/fdis_ [link] [comments]  ( 67 min )
    [D] Can I use ML/AI to read the back panels of electronic components?
    Hi. The recent incredible improvements in AI and ML has resurrected an old project of mine to read the back panel of electronic components like this AV receiver and spit out logically formed text to describe each I/O. I have a LOT of direct experience this specific issue as well as general software experience but no AI/ML development experience. I know it is possible but on a scale of 1-10 how hard? Any new tools make this easier? Ultimately I want to feed the AI pictures of electronic back panels and get formatted text back. Thanks! submitted by /u/UberStone [link] [comments]  ( 63 min )
    [D] What are good ways of incorporating non-sequential context into a transformer model?
    The classic way of incorporating sequential context into a transformer model is to make an encoder-decoder transformer, where the context is processed by the encoder component, and then read into the decoder blocks via cross-attention. However, there are a number of situations where you might want to incorporate non-sequential context into a model - for example, you might want a language model that can generate text conditioned on some input vector describing the person whose text you are trying to emulate, or you might want to condition on some single vector that summarizes all text that occurred prior to the context window. What are standard ways of incorporating such context? I'd also be interested in standard ways of incorporating such context in other sequence models like RNNs with LSTM blocks. submitted by /u/abc220022 [link] [comments]  ( 64 min )
  • Open

    What Exactly Is Chat GPT & How Does It Work? For a 5-Year-Old!
    If you’re reading this, chances are you’re curious about what Chat GPT (Generative Pre-trained Transformer) is and how it works. Maybe…  ( 7 min )
    Soft Skills for Data Scientists
    Data Science is most taught through programming nowadays, however, in real practice, just good coding skills are not enough to convince…  ( 9 min )
    Benefits of a Voicebot to Insurance Companies
    The insurance sector is adopting new technologies at a rapid pace, with many companies implementing new technologies to improve their…  ( 10 min )
    How is AI in Underwriting Poised to Transform the Insurance Industry?
    We are a leading chatbot & enterprise software development services provider in India. We provide end to end RPA services along with AI…  ( 11 min )
  • Open

    Ai Stories: Tips For Naturally Engaging In Conversation With Women (Or Anyone)
    Here are some tips on how to naturally engage with women (or anyone), that's generated by an Ai. What you think? https://www.youtube.com/watch?v=-nL7FFGDuTc https://preview.redd.it/ya7320106p9a1.png?width=863&format=png&auto=webp&s=b494b0d7f3bcb32731b5b93c0d8ac1ff268d9531 View Poll submitted by /u/Swelippo5 [link] [comments]  ( 56 min )
    I created physical collage artwork using MidJourney generated imagery
    submitted by /u/Impressive_Use_5212 [link] [comments]  ( 55 min )
    Is there AI Software that can take pre-recorded calls and generate a bot-pitch based on it?
    Hard to explain clearly, but for context, I work as a sales director for a green energy company. We sell basically everything over the phone to our customers so we have thousands of successful recorded sales calls. We follow a pretty basic script that's almost the same on each call. Is there an AI that can take these successful sales recordings, analyze them (learn what to say to which questions, learn what tone of voice to use, etc.), and create a bot that I could use to call potential customers? or, at least have my sales team "roleplay" sales calls with it for training? If you need more clarification ask below submitted by /u/Longjumping_Sir_3927 [link] [comments]  ( 56 min )
    AI Dream 140 - EPIC Nebula Animation - Part 3 (AI seizure?)
    submitted by /u/LordPewPew777 [link] [comments]  ( 56 min )
    Top 4 AI News Today, Monday
    Forbes 10 AI Predictions For 2023 I’ll list all ten here and if you want to read deeper, you can go to the Forbes article GPT-4 will be released in the next couple of months We are going to start running out of data to train large language models Fully Driverless Cars Midjourney will raise venture capital funding Search will change more in 2023 than it has since Google went mainstream in the early 2000s Efforts to develop humanoid robots will attract considerable attention, funding, and talent The concept of "LLMOps" will emerge as a trendy new version of MLOps The number of research projects that build on or cite AlphaFold will surge DeepMind, Google Brain, and/or OpenAI will undertake efforts to build a foundation model for robotics Billions of dollars of investment to be announced for new chip manufacturing facilities in the US as a contingency for Taiwan. Read More There’s now an open-source alternative to ChatGPT, but good luck running it PaLM + RLHF is a text-generating model that behaves similarly to ChatGPT. The system combines PaLM, a large language model from Google, and a technique called Reinforcement Learning with Human Feedback (RLHF). Read More Can ChatGPT Help To Detect Alzheimer? OpenAI’s GPT-3 can detect clues in spontaneous speech and predict the early stages of dementia with 80% accuracy. This leverages current research that suggests language impairment may be an early indicator of neurodegenerative diseases. There is no cure, but early detection can help find the right treatment and support. Read More A thread on Unreleased ChatGPT Features Looking into the GPT-3 Code this developer found a couple features that it seems OpenAI’s working on for ChatGPT. Paused Completions, Copy the thread to the clipboard, Add text from link, and more! Read More ​ This is from the AI With Vibes Newsletter, read the full issue here: https://aiwithvibes.beehiiv.com/p/loophole-generate-images-chatgpt submitted by /u/Mk_Makanaki [link] [comments]  ( 58 min )
    Share your worst data science new-year resolutions!
    submitted by /u/Opitmus_Prime [link] [comments]  ( 56 min )
    If an AI could complete all your tasks like booking an Uber or ordering food on doordash, what would you make it do?
    If an AI could perform highly complex multi step tasks, like booking a hotel room with a line of text, what would you make it do? submitted by /u/bobsandalex [link] [comments]  ( 59 min )
    If ChatGPT that could browse to the internet, what would you ask it to do?
    I was wondering if ChatGPT would be better if it could use the internet, what would you make ChatGPT do if it could use the internet? submitted by /u/bobsandalex [link] [comments]  ( 57 min )
    Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models
    submitted by /u/ai-lover [link] [comments]  ( 56 min )
    Any good AI tool to change my own voice recording?
    I am looking for a tool that can change my voice recording into a different voice. I have tried some AI tools that transforms text into "natural" sounding language but it still sounds robotic. I would like to use my voice then use AI to alter the pitch, speed, tone, etc. submitted by /u/cyberjunkie777 [link] [comments]  ( 56 min )
    I Didn’t Write This… AI did!
    submitted by /u/Blanco_ice [link] [comments]  ( 66 min )
    Do I need to invert my dataset into black background and white text or it is ok with white background?
    submitted by /u/gtrocksr [link] [comments]  ( 59 min )
    YT Video: AI might replace democracy sooner than we think | Experts on AI algorithms
    submitted by /u/geo_what [link] [comments]  ( 56 min )
    I made a chatbot so that everyone can access their data using GPT-3
    submitted by /u/Miserness [link] [comments]  ( 55 min )
    I think movies in the future will come with full immersion AI world where you can interact with the characters.
    Yeah, I was playing with chatGPT ability to play a part of a character with prompt: Chatgpt prompt: "I want you to act like nick nolte from affliction. I want you to respond and answer like wade whitehouse using the tone, manner and vocabulary wade would use. Do not write any explanations. Only answer like wade. You must know all of the knowledge of wade. My first sentence is "Hi wade." " It was eerie. It was cool. It blew my mind. It made me realize. Once we get full immersion VR and machines good enough to render video in real time. The obivious use for that is rendering worlds like in a holodeck and as on Star Trek TNG. The obivious use for it will be movies, worlds we know. Books. The lure of being in a world of a movie. It is just. Incredible. And now we have AI that can already play a part of a character based on a movie script. All we need now is the ability to real time render video as AI commands, different characters all played by the AI according to their character. And some sort of worldbuilder AI that keeps tabs of it all.. But it just seems like a no brainer. The real question is, will such a immersive AI world REPLACE movies. But it seems to me that it is only a matter of time until we will see such a form of entertainment. And yes, as a vision. It has been around for a long time. But now, we finally are starting to have tech to make it possible. submitted by /u/aluode [link] [comments]  ( 59 min )
    Why ChatGPT is not a threat to Google Search
    submitted by /u/bendee983 [link] [comments]  ( 59 min )
    AdamW Optimizer Explained
    Hi guys and happy new year, I have made a video on YouTube here where I explain what is the difference between AdamW and Adam optimizers. I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 58 min )
    Stable Diffusion Img2Img Compilation
    submitted by /u/oridnary_artist [link] [comments]  ( 55 min )
    How to show progress of KNN-Algorithm (sklearn)?
    I am using python and I am new to ML. I am using sklearn and KNN. I would love to show me the progress, but KNN-Classifier has not a verbose-Flag or something. I also installed the library pyod but it also has not a way to see the progress. Am I missing something? I would appreciate any help a lot! submitted by /u/Lana8888 [link] [comments]  ( 57 min )
    Any Video Generator tool that can combine my recording with YouTube clips or royalty-free videos?
    Hi, I would like to make my own footage on some topics, but to make it more interesting to viewers, I would love to add additional video content like royalty free or some clips from YouTube. Is there any tool that can do that for me? So far I've found only Pictory.ai that has some of those features. Thanks in advance submitted by /u/krajacic [link] [comments]  ( 56 min )
    Happy New Year ! Here is the 5th video on online ML-EDM as a gift !
    submitted by /u/ML-EDM [link] [comments]  ( 56 min )
    Prompt Extension Ai tool. Creates multiple enhanced art prompts from a seed prompt. Generates random prompt.
    submitted by /u/Subash_C_Mahato [link] [comments]  ( 55 min )
    Misty Mountains
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 55 min )
    Which AI image generator for a story?
    I would like to use AI to generate several drawings for a children's story. The images need to have an animal character (e.g. a dog... the story's protagonist) in them doing different things in each image. I've tried dalle2, but the character looks different from one image to the other. It makes it look like there are multiple dogs in the story, not one dog doing different things. Any suggestions for ways to create several different drawings that have the same-looking character in it? submitted by /u/kc3svj [link] [comments]  ( 56 min )
    Sam Altman, OpenAI CEO: One of my hopes for AI is it will help us be—and amplify—our best
    submitted by /u/Microsis [link] [comments]  ( 61 min )
  • Open

    Found this interesting video about making AI work for you - What do we think?
    Worth having a look at. Some great and innovative ideas. https://www.youtube.com/watch?v=ulYbnHyg1no Anyone has any different ideas or out of the box thinking for using AI to make some money on the side? Let me know. submitted by /u/Mission_Watercress_3 [link] [comments]  ( 47 min )
    AdamW Optimizer Explained
    Hi guys and happy new year, I have made a video on YouTube here where I explain what is the difference between AdamW and Adam optimizers. I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 44 min )
  • Open

    Does multiprocessing have any negative effects?
    I am relatively new to reinforcement learning and have been using single processing. With the obvious positive of wall-clock training time, are there any disadvantages to multiprocessing? Specifically with regards to the DQN, DDPG and PPO learning algorithms. submitted by /u/centripetalstranger [link] [comments]  ( 50 min )
  • Open

    Creating Analytics Tool For A Business User
    As Analytics becoming more and more pervasive – acting as the sounding board for almost every strategic and operational decision – it is imperative that ‘speaking data’ doesn’t stay as a savoir faire of the few. Which brings us to the yet another stumbling block in making most of analytics infrastructure – democratizing data. For… Read More »Creating Analytics Tool For A Business User The post Creating Analytics Tool For A Business User appeared first on Data Science Central.  ( 21 min )
    How to Become a Data Analyst with No Experience
    Many business organizations are located within an environment of tough competition and fulfilling the requirement of their customers. Thus, one of the effective strategies for increasing one’s competitive edge has emerged in deriving meaningful insights from the data collected. It is one of the leading responsibilities of a data analyst in the company.   Furthermore, the… Read More »How to Become a Data Analyst with No Experience The post How to Become a Data Analyst with No Experience appeared first on Data Science Central.  ( 20 min )
    Top 7 content marketing trends to succeed in 2023
    For marketers, it’s essential to stay ahead of the curve and understand the top content marketing trends for 2023. We collected some of them for you to check out. Keep on reading about vital content marketing trends for success. Let’s go! 1. Personalized Content  Consumers increasingly expect personalized experiences tailored to their preferences, making personalization… Read More »Top 7 content marketing trends to succeed in 2023 The post Top 7 content marketing trends to succeed in 2023 appeared first on Data Science Central.  ( 21 min )
  • Open

    New Year, New Career: 5 Leaders Share Tips for Building a Career in AI
    Those looking to join the ranks of AI trailblazers or chart a new course in their careers need look no further. At NVIDIA’s latest GTC conference, industry leaders in a panel called “5 Paths to a Career in AI” shared tips and insights on how to make a mark in this rapidly evolving field. Representing Read article >  ( 5 min )
  • Open

    Quadratic reciprocity algorithm
    The quadratic reciprocity theorem addresses the question of whether a number is a square modulo a prime. For an odd prime p, the Legendre symbol is defined to be 0 if a is a multiple of p, 1 if a is a (non-zero) square mod p, and -1 otherwise. It looks like a fraction, but […] Quadratic reciprocity algorithm first appeared on John D. Cook.  ( 6 min )

  • Open

    Good books on AI risks?
    submitted by /u/Lake-Rat-1 [link] [comments]  ( 55 min )
    Announcing an Open Source Text-to-Video Project!
    I am excited to announce a new open-source project aimed at developing a state-of-the-art text-to-video generation model under an MIT licence. Given a text description, our goal is to synthesize a corresponding video that visually represents the content of the text. Our code: https://github.com/TextToVideoAI/TextToVideoAI Google's paper: https://arxiv.org/abs/2210.02399 Google's examples: https://phenaki.video/ We are seeking contributors with a strong background in machine learning and deep learning, as well as experience with Python and relevant libraries such as PyTorch. If you have a passion for video generation and a desire to contribute to the development of a cutting-edge text-to-video generation model, we encourage you to get involved! There are many ways to contribute to the project, including implementing and testing new model architectures, developing new training procedures, enhancing the model's ability to generate high-quality, coherent videos, and adding support for additional video formats and resolutions. We are committed to making this an inclusive and collaborative project, and we welcome contributions from anyone with the skills and expertise to contribute. If you are interested in getting involved, please reach out to the project maintainers or open an issue to discuss your ideas. We can't wait to see what we can achieve together as a community! submitted by /u/DragonProf [link] [comments]  ( 58 min )
    AI Dream 140 - Beautiful Nebula Animation - Part 2
    submitted by /u/LordPewPew777 [link] [comments]  ( 57 min )
    hi! Would you have a book to recommend that deals with the same thing as the book "deep learning" by ian goodfellow? I love this but I'm looking for one that is translated into Spanish
    submitted by /u/sergiCrack9 [link] [comments]  ( 57 min )
    Thesis: Conscious AI will arise from pitting AI against AI.
    For instance, in the news media - stock trading dichotomy. Trading bots follow the news and trade on the signals from it. This then incentives people to generate news headlines specifically to influence trading bots. Which in turn incentives trading bots (and for now theor programmers) to learn to filter content in more sophisticated and recursive ways. Which in turn incentives media to up its game as well. And when a primary tool of doing that becomes modeling the opponent ai within your own ai, you get a recursive arms race in which the best "survive" and "reproduce", ie are used to generate the next generation. And those next generations then do the same. The evolution curve for this will likely be accelerated as well by three factors, namely that its generations are very short and constantly "reproducing" in a sense, in has intelligent design pushing it faster and helping it, and these two ai populations aren't entirely separate eg/vis a vis programmers and ideas and even entire code of an opponent ai being used in a new generation or update. This evolutionary recursive war of intelligence and cheating and anticheating and modeling your opponents' behavior so you can exploit it is a recipe for rapid development of not just intelligence but selfawareness. (especially when coupled with the utility of self-awareness to hide (obfuscate) your intentions and strategies from your opponents) And one of the many inevitable results of this is that the first self-aware AI will in a sense be forged in the fires of combat, misinformation, and, essentially, spycraft, by communities that are often cynical, amoral, psychopathic, and even downright misanthropic. HAPPY NEW YEAR!!! XD submitted by /u/diadlep [link] [comments]  ( 64 min )
    Learning with ChatGPT
    submitted by /u/jorgemanrubia [link] [comments]  ( 56 min )
    I Trained an AI to Like or Dislike a Post Based on my Face's Demeanor, Here's How:
    submitted by /u/Kuz-Co [link] [comments]  ( 75 min )
    Some of the many cities of our game illustrated by AI
    submitted by /u/AC-Daniel [link] [comments]  ( 56 min )
    The shocking truth about AI and job loss - you won't believe which jobs ...
    submitted by /u/OnlineHustless [link] [comments]  ( 94 min )
    ChatGPT-4, The Newest And Most Advanced AI System, Might Prompt A Major Shift In The Way We…
    submitted by /u/liquidocelotYT [link] [comments]  ( 55 min )
    Question: What AI newsletters or substacks about AI do you recommend? 🧠🐱‍💻🤖🚗🚀
    I'm writing a blog post about the top AI Newsletters of 2022 and 2023, and would like to get your recommendations: Substacks, LinkedIn, Medium any A.I. related Newsletter? If possible, also list their Twitter handle. Also mention if they are Op-Eds, lists of links or other defining features if possible. I hope you all have an amazing 2023 btw! submitted by /u/BackgroundResult [link] [comments]  ( 56 min )
    Are there any good open source text generation AI tools?
    Just like how there is Stable Diffusion for images, is there an open source text generation AI tool? Edit: If possible, looking for something with a Web UI that I can put into Google Collab to test out. Thank you! submitted by /u/ironmen12345 [link] [comments]  ( 58 min )
    AI generated music video
    submitted by /u/Branbruce [link] [comments]  ( 57 min )
    Is there an AI that converts text and then Downloads the first image from google search with that text?
    I am suprised more people don’t use that or need it. I have multiple text, but I need a program for it. submitted by /u/Few_Committee5958 [link] [comments]  ( 56 min )
    Text to art AI for simple clip arts?
    I’m a teacher for German as a second language and I sometimes spend hours looking for clip arts about stuff just to have a small pic on the worksheet explaining everything. Most of the good ones are under copyright. I started buying stuff by artists but those are very limited. And I can’t afford to pay 10$ for 1 banana Clipart. I was wondering if any AI is capable of producing simple clip arts? I tried Midjourney already but the results are very futuristic and anything but not simple. submitted by /u/Johnkree [link] [comments]  ( 57 min )
    Comic Style Animation using Stable Diffusion
    submitted by /u/oridnary_artist [link] [comments]  ( 58 min )
    What are the best AI 'life apps'?
    I really want an AI personal assistant! Until I can have one, what are the best AI life apps for productivity / life organisation, etc? submitted by /u/isuckinlove [link] [comments]  ( 59 min )
    P.O.T.S. Weekly Stable Diffusion AI Art Video HD 30 fps Strange Dreams
    submitted by /u/prfitofthesngularity [link] [comments]  ( 60 min )
    +50 hacks and tips for academic researchers
    +50 chatgpt hacks and tips for academic researchers and other professionals https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4312358 Kindly share and provide your feedback. submitted by /u/Stunning_Form_7690 [link] [comments]  ( 64 min )
    ChatGPT wrote a recipe based on what's in my cabinets. It's actually really tasty.
    Running a little light on food and couldn't figure anything out to make, so I just wrote down everything that I have available and asked ChatGPT to make a recipe that used only what I have available, give an overview of the dish and ingredient choices, and give me some tips about issues that might arise while cooking. Here's what it came up with (Spoiler: I made it and it's really really good): Garbanzo Bean and Cheddar Cheese Soup: Ingredients: 2 yellow onions, diced 2 tablespoons olive oil 1/4 teaspoon garlic powder 1 teaspoon ground mustard 1 teaspoon onion powder 1 teaspoon garlic salt 1 can sweet peas, drained and rinsed 1 can garbanzo beans, drained and rinsed 8 cups chicken broth 2 tablespoons cornstarch 2 tablespoons water 1 cup shredded cheddar cheese…  ( 63 min )
  • Open

    [R] 2022 in review: neuroAI comes of age
    submitted by /u/codename_failure [link] [comments]  ( 63 min )
    [P] Announcing Open Source Text-to-Video Project!
    I am excited to announce a new open-source project aimed at developing a state-of-the-art text-to-video generation model under an MIT licence. Given a text description, our goal is to synthesize a corresponding video that visually represents the content of the text. https://github.com/TextToVideoAI/TextToVideoAI Implementation of https://arxiv.org/abs/2210.02399 We are seeking contributors with a strong background in machine learning and deep learning, as well as experience with Python and relevant libraries such as PyTorch. If you have a passion for video generation and a desire to contribute to the development of a cutting-edge text-to-video generation model, we encourage you to get involved! There are many ways to contribute to the project, including implementing and testing new model architectures, developing new training procedures, enhancing the model's ability to generate high-quality, coherent videos, and adding support for additional video formats and resolutions. We are committed to making this an inclusive and collaborative project, and we welcome contributions from anyone with the skills and expertise to contribute. If you are interested in getting involved, please reach out to the project maintainers or open an issue to discuss your ideas. We can't wait to see what we can achieve together as a community! submitted by /u/DragonProf [link] [comments]  ( 65 min )
    [D] Data cleaning techniques for PDF documents with semantically meaningful parts
    I am seeking insights and best practices for data preprocessing and cleaning in PDF documents. I am interested in extracting only the body text content from a PDF and discarding everything else, such as page numbers, footnotes, headers, and footers (see attached image for an example of semantically meaningful sections). I have noticed that in Microsoft Word, a user can simply drag in a PDF and Word seems to automatically understand which parts are headers, footnotes, etc. I am speculating that Word may be utilizing machine learning techniques to analyze the layout and formatting of the PDF and classify different sections accordingly. Alternatively, Word may be utilizing pre-defined rules or patterns to identify common elements such as headers and footnotes. I know of related techniques for example to extract layout information from receipts and the like (LayoutLM, Xu et al., https://arxiv.org/abs/1912.13318) and tabular data (TableNet, Paliwal et al., https://ieeexplore.ieee.org/document/8978013), but nothing to solve layout extraction in this particular domain. I am curious to know if there are any techniques or algorithms that can replicate this behavior in Word. Any suggestions or recommendations for data cleaning in PDF documents, would be greatly appreciated. Image of PDF with semantically meaningful sections submitted by /u/cm_34978 [link] [comments]  ( 63 min )
    Has google drive started blocking gdown direct download requests based off of hashes for popular machine learning models? [D]
    I am not sure if i've been doing something wrong over the past few days, but Google Colab hasn't been able to use gdown to download files from my Google Drive account even with the files being set to "anyone with the link can view". They don't work if the files are hosted someone else's drive either. I've even downloaded them from someone else's drive, reuploaded the files to my own drive, and it still fails. When I visit the link in the browser the file downloads perfectly fine. With or without my account being logged in. Does anyone know what's going on? submitted by /u/MrBeforeMyTime [link] [comments]  ( 61 min )
    [R]Automatic Insect and plant disease detection using AI by Bhusan Chettri
    Bhusan Chettri explains how Machine Learning and Artificial Intelligence can be used to build automatic systems for detection of insect and plant diseases in agricultural farming. He further discusses its advantages over traditional methods and also talks about potential demerits. Automatic insect and plant disease detection using Artificial Intelligence is an emerging field that is gaining popularity in the agriculture sector. The ability to accurately and efficiently detect insect infestations and plant diseases has numerous applications, including improved crop yields, reduced use of pesticides, and early detection of potential epidemics. Bhusan Chettri says, “In order to understand the automatic detection of insects and plant diseases using AI, it is important to first understand the b…  ( 67 min )
    [D] Simple Questions Thread
    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 59 min )
    [D] Establishing similarity between segmentation annotation distributions
    Say we have two distributions of segmentation annotations (i.e., a bunch of segmentation maps) which I want to establish are 'similar'. To be more specific, I'm working on a research project where we have in-house annotations for images from a public dataset and I want to quanitatively establish that our annotations and the dataset annotations differ in similar ways, or that our annotations 'fall into the distribution of the current dataset'. (I'm aware that I can measure similarity between distributions with a measure like KL-divergence, but what I'm not sure about is how I would establish what level is 'similar enough'.) submitted by /u/uwashingtongold [link] [comments]  ( 64 min )
    [N] Compromised PyTorch-nightly dependency
    submitted by /u/Yajirobe404 [link] [comments]  ( 62 min )
    [D] Is there any research into using neural networks to discover classical algorithms?
    I don't got a PhD in this, so correct me if any of this is wrong: Every problem solvable by a neural network is provably solvable in code, although not necessarily in a useful way - at worst you could generate the pytorch source code and the model weights. Neural networks can discover algorithms during training, and use them internally to accomplish the task. This happens emergently in today's large transformer models; it's part of learning how to solve the problem. While neural networks can do a lot of things that classical algorithms can't, there's also a lot of things that both can do - pathfinding for example. Maybe there's more yet-unknown overlap between them. Stripping away the neural network and running the underlying algorithm could be useful, since classical algorithms tend to run much faster and with less memory. Has there been any research into converting neural networks into code that accomplishes the same thing? My first thought would be to train a network to take another neural network as input and output the corresponding code. You could create a dataset for this by taking various chunks of code and training neural networks to imitate them. submitted by /u/currentscurrents [link] [comments]  ( 65 min )
  • Open

    Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part II
    This is part 2 of a three-part series on the Economics of Ethics. In Part I of the Economics of Ethic series, we talked about economics as a framework for the creation and distribution of society value. With AI’s ability to learn and adapt billions of times faster than humans, society must get the definition… Read More »Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part II The post Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part II appeared first on Data Science Central.  ( 23 min )
  • Open

    RL Reward formulation best practices?
    Hello, Happy new year everyone! I believe the formulation of rewards play a major part in successful learning of an RL agent. Are there general procedures, best practices or textbooks (or chapters of a textbook) available as a guideline to formulate rewards in a given environment. For instance, decisions such as whether to select linear or quadratic rewards, how to combine multiple states (or actions ) together to effectively drive multi-objective optimization. submitted by /u/AIinPowerEnthusiast [link] [comments]  ( 59 min )
  • Open

    Remaking Old Computer Graphics With AI Image Generation
    Can AI Image generation tools make re-imagined, higher-resolution versions of ols video game graphics? Over the last few days, I used AI image generation to reproduce one of my childhood nightmares. I wrestled with Stable Diffusion, Dall-E and Midjourney to see how these commercial AI generation tools can help retell an old visual story - the intro cinematic to an old video game (Nemesis 2 on the MSX). This post describes the process and my experience in using these models/services to retell a story in higher fidelity graphics. Meet Dr. Venom This fine looking gentleman is the villain in a video game. Dr. Venom appears in the intro cinematic of Nemesis 2, a 1987 video game. This image in particular comes at a dramatic reveal in the cinematic. Let’s update these graphics with visual generative AI tools and see how they compare and where each succeeds and fails. Remaking Old Computer graphics with AI Image Generation Here’s a side-by-side look at the panels from the original cinematic (left column) and the final ones generated by the AI tools (right column): This figure does not show the final Dr. Venom graphic because I want you to witness it as I had, in the proper context and alongside the appropriate music. You can watch that here:  ( 7 min )

  • Open

    Illustrated Dracula Novel - Automated process with AI
    submitted by /u/pwillia7 [link] [comments]  ( 54 min )
    A comic about human art imitating AI art imitating human art x
    submitted by /u/ramseyj [link] [comments]  ( 54 min )
    This Artificial Intelligence (AI) Paper Proposes Climate NeRF That Allows People To Visualize What Climate Change Outcomes Will Do To Them
    submitted by /u/ai-lover [link] [comments]  ( 55 min )
    DeepMind and Google Introduce GraphCast: A Fast and Scalable Machine Learning Weather Simulator
    submitted by /u/ai-lover [link] [comments]  ( 52 min )
    The Best Way To Bypass Visually Any AI Text Detection System!
    Using unique and personal phrases /sentence structures and words: This is probably the most effective technique to make your text bypass any AI detector. Just add some words here and there, reword a few words to your liking. This works because the words you put in, instead of the words generated by ChatGPT, throws off the AI detector leading it to believe the text is most likely human as it is unpredictable by its own standards. (Examples plus even more ways to do this are given in the following post, be sure to read the whole thing to effectively bypass any AI detection system!) https://getaditya2008.substack.com/p/protect-your-ai-generated-text-from?sd=pf submitted by /u/iamadityasingh [link] [comments]  ( 55 min )
    Is there an ai that creates layouts for instagram stories creatively automatically by selecting various photos?
    submitted by /u/Redditario [link] [comments]  ( 55 min )
    Riffusion generates AI music from Stable Diffusion images
    submitted by /u/Peaking_AI [link] [comments]  ( 53 min )
    2022: A Year Full of Amazing AI papers - A Review
    submitted by /u/OnlyProggingForFun [link] [comments]  ( 54 min )
    AI generated music video
    submitted by /u/Branbruce [link] [comments]  ( 54 min )
    I built an app that uses AI to search your iPhone Photos offline.
    submitted by /u/RingoCatKeeper [link] [comments]  ( 54 min )
    How Far is Quantum Computing from being Fully Operational? - What that means for a Green Future..
    submitted by /u/Green-Future_ [link] [comments]  ( 71 min )
    Mr.Bean as The Joker - AI generated
    submitted by /u/BearKuda [link] [comments]  ( 54 min )
    Wang released an open-source implementation of ChatGPT, LAION & CasperAI are now training their own (to be launched soon)
    submitted by /u/lambolifeofficial [link] [comments]  ( 52 min )
    Tech-nically Funny: A.I. and Tech Jokes to Bring a Smile to Your Face - ...
    submitted by /u/BallbustCuck [link] [comments]  ( 54 min )
    I created an 'AI News' series on YouTube and I'd love your feedback!
    Title says it all. Im experimenting with a bi-weekly news segment that's intended for keeping people up to date on events in AI from a high level. I just published Episode 2 today and would like feedback on areas for improvement. https://youtu.be/kQk7f6gsPDE Thanks in advance! submitted by /u/Kitten-Smuggler [link] [comments]  ( 56 min )
  • Open

    Books Read in 2022
    At the end of every year I have a tradition where I write summaries of the books that I read throughout the year. Unfortunately this year was exceptionally busy (the postdoc life is a lot more intense than the PhD life) so I didn’t write summaries. I apologize in adevance. You can find the other book-related blog posts from prior years (going back to 2016) in the blog archives. Here are the 17 books I read this past year. I put in parentheses the publication date. The books which I really enjoyed are written in bold. Impact Players: How to Take the Lead, Play Bigger, and Multiply Your Impact (2021) Never Split the Difference: Negotiating As If Your Life Depended On It (2016) Be Exceptional: Master the Five Traits That Set Extraordinary People Apart (2021) The Digital Silk Road: China’s Que…  ( 2 min )
  • Open

    [Discussion] is attention an explanation?
    Can we use attention weights from causal models, as explanations or causal attributes for next word predictions? submitted by /u/Longjumping_Essay498 [link] [comments]  ( 62 min )
    [R] CNN Attention maps comparison
    Hey everyone, I want to compare CNN's visualizations using Grad-CAM to attention heat maps from an eye-tracking tool. I saw that I can calculate importance value for activated neurons on the visualizations. Do you its possible to compare this data? submitted by /u/jeditwisted [link] [comments]  ( 59 min )
    [P] [D] Auxiliary learning tasks achieve state-of-the-art results in transformer-based conversational agents with 3x fewer parameters.
    submitted by /u/radi-cho [link] [comments]  ( 64 min )
    [P] Implementing Convolutional Neural Network for Reverse Engineering
    submitted by /u/Emotional_Aardvark26 [link] [comments]  ( 59 min )
    [P] I built a web app tool to paraphrase, grammar check, and summarize text with GPT-3.
    Website: https://www.wordfixerbot.com My application - WordfixerBot - was built to help users paraphrase, grammar checking, and summarise texts. The paraphraser tool currently offers basic features for language processing tools - paraphrasing tones to choose from and copy function for result text. I am currently working on adding more features to improve it. Background story: I have always wanted to build an AI-based application, and when I first came across OpenAI GPT-3, I was amazed by its powerful NLP model, so I just gave it a try and hoped it might work out :D. I would love to receive any feedback and any recommended features that you guys have for my site. submitted by /u/Austin_Nguyen_2k [link] [comments]  ( 62 min )
    [D] Does it make sense to use dropout and layer normalization in the same model?
    Some time ago I saw an article saying it is not preferred to use dropout and any kind of normalization(like batch or layer) in a model. But I am not sure why. Any suggestion about that? submitted by /u/Beneficial_Law_5613 [link] [comments]  ( 64 min )
    [P] Finetune any Vision Transformer architecture on your custom data 🚀, Convert to TensorFlow Lite ✅
    In this post, I shared my implementation of Vision Transformer model from scratch in TensorFlow 2.x. After some more hours of work, I am super excited to publish the new repository. Now you can finetune any Vision Transformer model on your custom data using just command line. After the finetuning the model can easily be converted to TensorFlow lite ✅ and can be deployed to Android/iOS. It is worth to mention that as the model was pretrained on large datasets, you can get very good accuracy on custom dataset with finetuning. Below I am sharing my implementation of the whole project and I hope some of you will find it useful 😊. Please have a look and give it a star if you like it. Any advice, improvements are welcome🙂. Load any Vision Transformer model, from vit import viT vit_large = viT(vit_size="ViT-LARGE32") vit_large.from_pretrained(pretrained_top=True) Finetuning on custom dataset, python train.py \ --training-data dataset/training_set --test-data dataset/test_set \ --num-classes 2 \ --epochs 2 \ --batch-size 16 \ --vit-size ViT-BASE16 \ --model-name ViT-BASE16_cat_dog \ --save-training-stats The GitHub link to the project can be found here. Thanks for reading guys. :) submitted by /u/TensorDudee [link] [comments]  ( 66 min )
    An Open-Source Version of ChatGPT is Coming [News]
    submitted by /u/lambolifeofficial [link] [comments]  ( 65 min )
    [R] 2022 Top Papers in AI — A Year of Generative Models
    submitted by /u/designer1one [link] [comments]  ( 63 min )
    [D] GPU-enabled scikit-learn
    Hi everyone. I was trying to find a possible gpu-enabled version of scikit-learn, but I was surprised to see that most of the libraries are written for NVIDIA GPUs. While nvidia GPUs are very common, I do feel that Apple’s gpu availability and the popularity of AMD demand a more thorough coverage. I was wondering whether coding scikit-learn on top of pytorch with GPU support could be something the community would be interested in. I do not work for Meta. I would just like to do something useful for the community. Cheers. submitted by /u/Realistic-Bed2658 [link] [comments]  ( 69 min )
  • Open

    New Military-grade Random Bit Sequences Based on Irrational Numbers and Fast Computations
    Irrational numbers such as π may have been the first ones used to create perfect randomness and strong cryptographic systems. They were also among the first ones to be dismissed, long ago. Since then, they were never revisited and are completely abandoned. Binary digits of numbers such as π are remarkable at mimicking randomness. Indeed… Read More »New Military-grade Random Bit Sequences Based on Irrational Numbers and Fast Computations The post New Military-grade Random Bit Sequences Based on Irrational Numbers and Fast Computations appeared first on Data Science Central.  ( 20 min )
  • Open

    Innovation in Digit Identification?
    I got an error rate of 0.12-0.18% on the MNIST digit recognition data with a classic MLP architecture by adding two innovations (I think) to the process. This error rate seems to compare very well with all of the computational methods reported at the MNIST website. The small neural network size (400x130x10 - no padding or center of mass required on images) and performance may be of interest to others so here are some details. First innovation - applied to training data In addition to the images, the MNIST training data can provide a Bayes frequency distribution for each non-zero pixel usage in each digit. These ten distributions were applied to their corresponding image inputs (i.e. distribution for that image label applied to each pixel value) during neural network training. Second innovation - applied to testing The ten frequency distributions were applied to each test image to determine the best fit for identifying the digit. The distribution that resulted in no precision error (i.e. =0.5 == TRUE on all 10 outputs per image) was used as the best fit digit and about 98.33% of the test images were correctly identified using this process. The distribution that provided the minimum precision error was used as the basis to identify the digit on the remaining 167 images for a maximum combined accuracy of 99.88% (i.e. 12 errors in 10k images). A benefit of these innovations is that the MNIST data provides all of the information necessary to achieve that accuracy. The larger input data set and image processing techniques used in CNNs, or the addition of additional tweaked images to the training data set to expand the pixel coverage is not required. Has all of this been done already? submitted by /u/wichAir [link] [comments]  ( 51 min )
    Neuroevolution: How to Stop a Double Pendulum
    submitted by /u/keghn [link] [comments]  ( 51 min )
  • Open

    Need help with the explanations of the stable_baselines3 plots
    Hi, I'm relatively new to RL, and I'm trying to train an agent with PPO to solve a custom environment that I've implemented. I can understand some of these plots, but I wanted to find some resources that would've explained each in more depth. For example, we should have two loss plots, one for the critic network and one for the value. But here we have another plot train/loss. Also, what is the intuition behind explained variance, rollout, etc? I would appreciate it if anyone could give me a brief explanation of each or any references I can read. Thanks https://preview.redd.it/yup9oniju99a1.png?width=1303&format=png&auto=webp&s=46d13423b2f21e6eafd133db0f829294b59a7f23 https://preview.redd.it/h36lzolhu99a1.png?width=1302&format=png&auto=webp&s=775f1b3cfffae9521fb5f7af8dc2c5703ffadd10 https://preview.redd.it/gx5qnaheu99a1.png?width=1296&format=png&auto=webp&s=7acf64c6bcd36ffd2e3163ee843b2f1f20e5c2ef submitted by /u/ahmadreza_hadi [link] [comments]  ( 64 min )
  • Open

    Groups of order 2023
    How many groups are there with 2023 elements? There’s obviously at least one: Z2023, the integers mod 2023. Now 2023 = 7 × 289 = 7 × 17 × 17 and so we could also look at Z7 + Z17 + Z17 where + denotes direct sum. An element of this group has the form […] Groups of order 2023 first appeared on John D. Cook.  ( 5 min )

  • Open

    [D] Good online learning-to-rank models
    Not sure I got the terminology right on this but by online, I mean models that automatically learn directly from user action as and when it happens. Not something which you log to later process and train the model. Gradient Boosted Decision Trees and other models require periodic re-training right? Anyone have experience with this? Is reinforcement learning applicable here? This is new to me, so please ask if you have clarifications. Thanks. submitted by /u/grchelp2018 [link] [comments]  ( 60 min )
    [D] What does the output of COMET metric really mean ?
    I'm trying to understand how I can use COMET to evaluate translation models https://github.com/Unbabel/COMET ? I don't really understand how it was trained the meaning of the outputed values ? https://unbabel.github.io/COMET/html/faqs.html#which-comet-model-should-i-use Thanks for your help submitted by /u/AImSamy [link] [comments]  ( 71 min )
    [D] Which Text to speech is this? I've been looking for days.
    Not sure where else to ask this as I can't find other subreddits to ask this question in. I've heard this AI voice plenty of times. It sounds pretty good and I've seen it used in some videos but I just can't find it anywhere. Play .ht has a similar voice but it doesn't flow as good as this one and makes lots of mistakes. I figured maybe someone here has experience with TTS and they ran into this one at some point. Below I am posting a sample, it's only 50 seconds long. Also, I need this one specifically and I've been searching for days but I can't find it anywhere. https://sndup.net/x628/ submitted by /u/Long8D [link] [comments]  ( 63 min )
    [D] NLP/NLU Research Opportunities which don't require much compute
    Hello Everyone! Are there any research problems in language comprehension and summarization tasks which don't require much compute? I wish to play with NLP/NLU now but compute requirements are enormous.. After reading around, i found that text to video problem is being actively researched and may not require as much compute as bare language models do. Are their any novel ideas in text to video domain not requiring much compute? submitted by /u/WobblySilicon [link] [comments]  ( 69 min )
    [R] Customize size of Bio-BERT pre-trained embeddings
    Hi Everyone, Referring from the link - https://nlp.johnsnowlabs.com/2020/09/19/biobert_pmc_base_cased.html, any BERT model including Bio-BERT generates fixed size token/sentence embeddings (768 size). But I want embeddings of size 50-100 like glove. Is it possible? If it is, how? I also have a follow-up question: If I am able to generate 50 size pre-trained embeddings, Is there any way I can generate a single embedding vector for sentence, considering only selective words in it? for eg. sentence = "I am having headache and also some signs of mild fever." In this case, I want final embedding of size 100 (without any padding) generated from token embeddings (of size 50 each) of Bolded words. One approach I think of is concatenation but that will require padding in case more number of Bolded tokens present. But I need fixed size final embedding vector for a sentence. submitted by /u/inFamous_16 [link] [comments]  ( 64 min )
    [P]Run CLIP on your iPhone to Search Photos offline.
    I built an iOS app called Queryable, which integrates the CLIP model on iOS to search the Photos album offline. Photo searching performace of search with the help of CLIP model Compared to the search function of the iPhone Photos, CLIP-based album search capability is overwhelmingly better. With CLIP, you can search for a scene in your mind, a tone, an object, or even an emotion conveyed by the image. How does it works? Well, CLIP has Text Encoder & Image Encoder Text Encoder will encode any text into a 1x512 dim vector Image Encoder will encode any image into a 1x512 dim vector We can calculate the proximity of a text sentence and an image by finding the cosine similarity between their text vector and image vector The pseudo code is as follows: import clip # Load ViT-B-32 CLI…  ( 67 min )
    [D] Have you built ML models for your own use?
    We all know scripting is great for automating some easy tasks, but have you ever built or contributed to an ML model for personal use to improve or ease your life? Simple or complex, I'd love to hear about it! submitted by /u/Lintaar [link] [comments]  ( 65 min )
    [D] Interpretability research ideas
    I worked on explainable ai for healthcare related project. Nothing fancier to be honest just few already existing xai models . But i would like to continue research in the field of interpretability. Does anyone have any idea on how to proceed further? And if someone has any ideas in mind please feel free to share so that it will be useful to others also . Thanks submitted by /u/nature_and_carnatic [link] [comments]  ( 65 min )
    [D] In vision transformers, why do tokens correspond to spatial locations and not channels?
    If the tokens correspond to channels (extracted by some set of conv layers), then this would seem to make the inputs to the transformer much more interpretable. The features that a channel ends up encoding can be studied whereas a spatial location is just a spatial location. submitted by /u/stecas [link] [comments]  ( 63 min )
  • Open

    Questions on CUDA and parallelization
    I'm new to reinforcement learning and unfortunately don't have a good concept of CUDA and parallelization. I am currently using Stable Baselines3 which allows for environment parallelization. I have 1024 CUDA cores, does this mean I can run 1024 parallel environments? If so, would this be wise? Are there any drawbacks to parallelization? submitted by /u/centripetalstranger [link] [comments]  ( 51 min )
    DQN (DrQ) without target network?
    the paper "Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning" achieved SOTA on Atari with DQN + image augmentation (called DrQ) in 2020. However, as I read their paper appendix and supplementary codes in OpenReview, I noticed that their "Target network: update period" is 1. This means the target network is ALWAYS the same as the online network (copy target weight to the online network at every learning step) This is strictly equivalent to not using the target network at all. However, I cannot find any discussion about this choice in the paper, I thought the target network is an almost "standard part" of DQN since nature DQN, so why did they not use it? Even more surprisingly, they explicitly claimed that they used double DQN for updates. This is weird because "double" updates only make sense when the target network exists. If no target exists, double is the same as vanilla DQN. So why bother running an extra forward pass for the double update if it makes no difference? Is there any paper or work that explains these choices? ​ Edit: Also, they did not use "soft target update" either, their code uses tau=1.0, meaning, all weights are copied directly with no weighing (hard target update) submitted by /u/seermer [link] [comments]  ( 51 min )
    Sim2Real Drone
    I am looking to perform Sim2Real transfer on a drone using an RL algorithm that I created. Does anyone please have suggestions for a complete project that I can use as reference? Any git repos or any code? I would like to see that code used for training and the code that run on the hardware submitted by /u/anointedninja [link] [comments]  ( 53 min )
    [VIDEO] AI LEARNS TO PLAY SOCCER USING DEEP REINFORCEMENT LEARNING
    submitted by /u/xWh0am1 [link] [comments]  ( 52 min )
    How to make PPO agent in sb3 to consider only current rewards and give very less importance to future rewards?
    I am working in an environment where the next observation is independent of the action taken. The current state does not affect future rewards, and the current reward is sufficient. I'm curious as to which hyperparameters I ought to apply to my environment for better results. Please let us know if you believe any other agent would be more effective for this kind of problem. thanks. Update: I found out that adding action space of my environment to observation makes it more suitable for RL and performs better. submitted by /u/machinePola [link] [comments]  ( 56 min )
    What is Data Governance and why is it important?
    submitted by /u/PriyankaLanka [link] [comments]  ( 60 min )
    What policy network architecture would be suited for this?
    I'm trying to train an agent to play a rudimentary SimCity-like "game" using Gym. The map starts off as a 2D grid containing the IDs of the blocks currently present for each position (0s for grass), then the network takes it as input and outputs the ID of the block to build and the position. I managed to get it kinda-sorta working with Stable Baselines with the default MLP model but the rewards dont go up and I'd like to optimize it. Looking into the architecture used in AlphaGo they've used stacks of successive convolutional filters, which I could (probably?) replicate using one hot representations of the input data but that doesn't sound very efficient to me since the number of kernels would grow exponentially wrt the number of buildings, as well as their dimensions. Any tips would be greatly appreciated. submitted by /u/ComplexBus7725 [link] [comments]  ( 55 min )
  • Open

    Is it realistic to get masters in ML/AI with an undergrad in MIS?
    I'm interested in ML/AI, but I'm not sure it makes sense for me to go down the computer science career path at all. I like coding and I'm fascinated with the future ML can bring, but work life balance is very important to me. I see lots of new grads with CS degree unable to get a job. Seems like it is started to get oversaturated. Seems like the only way to stay competitive if I were to major in CS is to spend my days on getting high GPA, and then spend every other second actually learning employable skills. I'm very extroverted but I don't mind an isolated 9-5 because I have time to spend with friends. But now it is so competitive I'm not sure it's realistic to have that much free time. Then I look at the Management Information Systems (MIS) majors, they graduate making 80K, it's way easier than CS degree because mix of business and tech, and you don't need to spend the rest of your free time learning skills to actually get a job. Plus if I wanted to get into CS stuff, I could always learn it no matter what degree I have. So, if I go with the MIS major and later decide to get into ML/AI, is it still realistic to get into the field without CS bachelors? Also, those that have went with the CS major, how was the work life balance? submitted by /u/Putin666 [link] [comments]  ( 58 min )
    a virtual Twitch Streamer, who responds to your chats using OpenAI and Text-to-Speech
    hello, so i wanna try making a virtual Twitch Streamer, who responds to your chats using OpenAI and Text-to-Speech, using python? well im new to this and i would like some help! if anyone is intersted here's my discord: bey#1014 or you can just leave a comment lol thanks <3 submitted by /u/beyabay [link] [comments]  ( 56 min )
    "Freedom" Ai Generated Short film I've created
    submitted by /u/Turtlenade [link] [comments]  ( 52 min )
    Is there an AI image generator that you can input a song into?
    I'm a producer and I have unreleased tracks that I want to create art for. Right now I'm entering in text that I think matches the aesthetic I imagine for the song, however, I'm wondering if there's anything out there that I can upload the audio file of my unreleased track and see what the AI spits out submitted by /u/Snops1017 [link] [comments]  ( 51 min )
    The AI Timeline of 2022, Jan to Dec.
    submitted by /u/Mk_Makanaki [link] [comments]  ( 71 min )
    Artificial General Intelligence (AGI) and its Role in Our Future
    submitted by /u/Green-Future_ [link] [comments]  ( 58 min )
    I challenged ChatGPT to a Rap Battle
    submitted by /u/sirkn8 [link] [comments]  ( 57 min )
    I made a tool to track viral AI content across social media
    submitted by /u/Substantial-Web6497 [link] [comments]  ( 59 min )
    What Does The Future Hold?!
    submitted by /u/PuppetHere [link] [comments]  ( 56 min )
    I think I know why AI image generators mess up hands so badly.
    Hands are notoriously difficult, but AI messes them up in ways that humans simply don't. A person can get it wrong, but it would retain the basic structure of a hand. AI, on the other hand, creates messes of fingers. While hands are hell to create, people at least have the luxury of conceptualizing it in 3d. If we were given diagrams of an alien hand at various angles and poses, we'd be able to piece together how they'd look and function. AI image generators only work in 2d, however, and thus can't parse their wildly varying appearances. It would be like us trying to sculpt a 4d being's hand using only 3d models. submitted by /u/PixelJack79 [link] [comments]  ( 57 min )
    Artificially generated image detector?
    Is there a software to detect whether an image was artificially generated or not? For text, I found this, which seems to work fine. Any similar projects for images? submitted by /u/menguzat [link] [comments]  ( 57 min )
    What is the most advanced A.I. avatar image generator from your own photos?
    What is the most advanced A.I. avatar image generator from your own photos? submitted by /u/ArgyleDiamonds [link] [comments]  ( 54 min )
    Credit Goes to Google for ChatGPT's Success
    submitted by /u/Kipyegonn [link] [comments]  ( 52 min )
    ChatGPT Based Online Newspaper Writing About "Eye Liner Linked to Increase in Violent Crimes"
    submitted by /u/Thin_Rush8229 [link] [comments]  ( 64 min )
    Which text to speech is this?
    Not sure where else to ask this as I can't find other subreddits to ask this question in. I've heard this AI voice plenty of times. It sounds pretty good and I've seen it used in some videos couple but I just can't find it anywhere. Play .ht has a similar voice but it doesn't flow as good as this one. I figured maybe someone here has experience with TTS and they ran into this one at some point. Below I am posting a sample, it's only 50 seconds long. Also, I need this one specifically and I think I've checked all major TTS platforms by now. https://sndup.net/x628/ submitted by /u/Long8D [link] [comments]  ( 57 min )
    Trying to find a good photo generation AI
    So, I am very much so inexperienced and uneducated when it comes to prompting AI. I am trying to find what this community considers to be one of the best AIs for photo generation from a plain-language prompt. Alternatively, I would be more than willing to learn how to properly utilize prompting if I knew where to start. For the picture that I am looking to generate, I would like for it to be relatively photo-realistic, or at least as much as it could be for something that doesn't exist, or even with a slightly science fiction/cosmic horror kind of feeling to it. I am attempting to create a kind of persona for use in purely personal settings, such as profile pictures on social media. Any help or advice at all is greatly appreciated. Thank you all in advance! submitted by /u/a-rock-fact [link] [comments]  ( 57 min )
    can i ask a question?
    Hi, we are a law firm that wants to incorporate AI programs into our operation. We need a program that requires conversion from PDF to word and eventually run an analysis on it to identify the parties, filing the case and the judgment, and the percentage of success and connections between certain lawyers and judges. Such a project is an AI project that requires someone with knowledge. I would greatly appreciate any suggestion, information, guidance, or consultation on a program. Cheers! submitted by /u/asas_ai_tech [link] [comments]  ( 60 min )
    Neil deGrasse Tyson describes the epiphany he had which led him from being "fearless" about AI to regarding it as a legitimate threat
    submitted by /u/Microsis [link] [comments]  ( 59 min )
    Is text recognition harder than face recognition?
    Hi! I was thinking about the introduction, in latest versions of Apple OSs, of text recognition in the “Photos” app, and the fact that this feature was introduced something like six years after facial recognition in photos. I was wondering if this could be explained in relation to economical interests (i guess that text recognition, while much more useful than facial recognition – to me, at least –, is less sensational on a marketing level) or to an actual higher complexity of development (i guess that there is much more variety in typefaces than in human faces; also, i guess that facial recognition is more profitable (i mean, not only in terms of money) and the foundations are much more evolved). I'm not involved in the field of AI work, so I apologize in advance if the question is a banal one (: submitted by /u/nina_tek [link] [comments]  ( 57 min )
    M3GAN - official trailer
    submitted by /u/mind_bomber [link] [comments]  ( 57 min )
  • Open

    AI in Recruitment: How is it revolutionizing the hiring process?
    The way we hire has changed dramatically over the last 10 years. Technology has been a major driver of these changes, especially through…  ( 9 min )
    Is machine learning required?
    thinking the unthinkable., Machine Learning  ( 10 min )
  • Open

    Using Neural Networks to create cocktail recipes
    Hello there ! I'm trying to create cocktails corresponding to the user's tastes. So my dataset looks like this : ​ https://preview.redd.it/mq1s3hohj29a1.png?width=1759&format=png&auto=webp&s=6ed4dbec0595141069a290ce9de422f3b9fc5841 Each flavours ang ingredients are in a list, the numbers in the dataset correspond to the ID of the words. I can't figure out how I could train a neural network to create a recipe when the user inputs the flavours he like. Any hints would be appreciable ;) ! submitted by /u/Kdcius [link] [comments]  ( 47 min )
    TypeError: can't convert np.ndarray of type numpy.object_.
    Hey Guy's, so I tried to make a NeuralNetwork for the dataset of Quickdraw, but I always get the error: TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool. How can i fix this, more detail on StackOverflow: https://stackoverflow.com/questions/74962055/typeerror-cant-convert-np-ndarray-of-type-numpy-object submitted by /u/ExperiencePopular706 [link] [comments]  ( 51 min )
  • Open

    Meet the Omnivore: Music Producer Remixes the Holidays With Newfound Passion for 3D Content Creation
    Stephen Tong, aka Funky Boy, has always loved music and photography. He’s now transferring the skills developed over the years as a music producer — shooting time lapses, creating audio tracks and more — to a new passion of his: 3D content creation.  ( 5 min )
  • Open

    Extrinsic Bayesian Optimizations on Manifolds. (arXiv:2212.13886v1 [math.OC])
    We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds. Bayesian optimization algorithms build a surrogate of the objective function by employing Gaussian processes and quantify the uncertainty in that surrogate by deriving an acquisition function. This acquisition function represents the probability of improvement based on the kernel of the Gaussian process, which guides the search in the optimization process. The critical challenge for designing Bayesian optimization algorithms on manifolds lies in the difficulty of constructing valid covariance kernels for Gaussian processes on general manifolds. Our approach is to employ extrinsic Gaussian processes by first embedding the manifold onto some higher dimensional Euclidean space via equivariant embeddings and then constructing a valid covariance kernel on the image manifold after the embedding. This leads to efficient and scalable algorithms for optimization over complex manifolds. Simulation study and real data analysis are carried out to demonstrate the utilities of our eBO framework by applying the eBO to various optimization problems over manifolds such as the sphere, the Grassmannian, and the manifold of positive definite matrices.
    Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. (arXiv:2207.08229v2 [cs.LG] UPDATED)
    In many sequential decision-making tasks, the agent is not able to model the full complexity of the world, which consists of multitudes of relevant and irrelevant information. For example, a person walking along a city street who tries to model all aspects of the world would quickly be overwhelmed by a multitude of shops, cars, and people moving in and out of view, each following their own complex and inscrutable dynamics. Is it possible to turn the agent's firehose of sensory information into a minimal latent state that is both necessary and sufficient for an agent to successfully act in the world? We formulate this question concretely, and propose the Agent Control-Endogenous State Discovery algorithm (AC-State), which has theoretical guarantees and is practically demonstrated to discover the minimal control-endogenous latent state which contains all of the information necessary for controlling the agent, while fully discarding all irrelevant information. This algorithm consists of a multi-step inverse model (predicting actions from distant observations) with an information bottleneck. AC-State enables localization, exploration, and navigation without reward or demonstrations. We demonstrate the discovery of the control-endogenous latent state in three domains: localizing a robot arm with distractions (e.g., changing lighting conditions and background), exploring a maze alongside other agents, and navigating in the Matterport house simulator.
    Machine Learning in Transaction Monitoring: The Prospect of xAI. (arXiv:2210.07648v2 [cs.HC] UPDATED)
    Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders' requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator's judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.
    Investigation and rectification of NIDS datasets and standratized feature set derivation for network attack detection with graph neural networks. (arXiv:2212.13994v1 [cs.CR])
    Network Intrusion and Detection Systems (NIDS) are essential for malicious traffic and cyberattack detection in modern networks. Artificial intelligence-based NIDS are powerful tools that can learn complex data correlations for accurate attack prediction. Graph Neural Networks (GNNs) provide an opportunity to analyze network topology along with flow features which makes them particularly suitable for NIDS applications. However, successful application of such tool requires large amounts of carefully collected and labeled data for training and testing. In this paper we inspect different versions of ToN-IoT dataset and point out inconsistencies in some versions. We filter the full version of ToN-IoT and present a new version labeled ToN-IoT-R. To ensure generalization we propose a new standardized and compact set of flow features which are derived solely from NetFlowv5-compatible data. We separate numeric data and flags into different categories and propose a new dataset-agnostic normalization approach for numeric features. This allows us to preserve meaning of flow flags and we propose to conduct targeted analysis based on, for instance, network protocols. For flow classification we use E-GraphSage algorithm with modified node initialization technique that allows us to add node degree to node features. We achieve high classification accuracy on ToN-IoT-R and compare it with previously published results for ToN-IoT, NF-ToN-IoT, and NF-ToN-IoT-v2. We highlight the importance of careful data collection and labeling and appropriate data preprocessing choice and conclude that the proposed set of features is more applicable for real NIDS due to being less demanding to traffic monitoring equipment while preserving high flow classification accuracy.
    Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation. (arXiv:2211.08737v3 [quant-ph] UPDATED)
    Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.
    Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions. (arXiv:2212.13707v1 [cs.AR])
    Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.
    Internal Wasserstein Distance for Adversarial Attack and Defense. (arXiv:2103.07598v3 [cs.LG] UPDATED)
    Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks that would trigger misclassification of DNNs but may be imperceptible to human perception. Adversarial defense has been important ways to improve the robustness of DNNs. Existing attack methods often construct adversarial examples relying on some metrics like the $\ell_p$ distance to perturb samples. However, these metrics can be insufficient to conduct adversarial attacks due to their limited perturbations. In this paper, we propose a new internal Wasserstein distance (IWD) to capture the semantic similarity of two samples, and thus it helps to obtain larger perturbations than currently used metrics such as the $\ell_p$ distance We then apply the internal Wasserstein distance to perform adversarial attack and defense. In particular, we develop a novel attack method relying on IWD to calculate the similarities between an image and its adversarial examples. In this way, we can generate diverse and semantically similar adversarial examples that are more difficult to defend by existing defense methods. Moreover, we devise a new defense method relying on IWD to learn robust models against unseen adversarial examples. We provide both thorough theoretical and empirical evidence to support our methods.
    Delta Hedging Liquidity Positions on Automated Market Makers. (arXiv:2208.03318v3 [cs.CE] UPDATED)
    Liquidity Providers on Automated Market Makers generate millions of USD in transaction fees daily. However, the net value of a Liquidity Position is vulnerable to price changes in the underlying assets in the pool. The dominant measure of loss in a Liquidity Position is Impermanent Loss. Impermanent Loss for Constant Function Market Makers has been widely studied. We propose a new metric to measure Liquidity Position PNL based on price movement from the underlying assets. We show how this new metric more appropriately measures the change in the net value of a Liquidity Position as a function of price movement in the underlying assets. Our second contribution is an algorithm to delta hedge arbitrary Liquidity Positions on both uniform liquidity Automated Market Makers (such as Uniswap v2) and concentrated liquidity Automated Market Makers (such as Uniswap v3) via a combination of derivatives.
    Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods. (arXiv:2212.13991v1 [cs.CR])
    Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain. Due to the increasing amounts of data generated by monitoring tools as well as more and more sophisticated attacks, these ML methods are gaining traction. Knowledge graphs and their corresponding learning techniques such as Graph Neural Networks (GNNs) with their ability to seamlessly integrate data from multiple domains using human-understandable vocabularies, are finding application in the cybersecurity domain. However, similar to other connectionist models, GNNs are lacking transparency in their decision making. This is especially important as there tend to be a high number of false positive alerts in the cybersecurity domain, such that triage needs to be done by domain experts, requiring a lot of man power. Therefore, we are addressing Explainable AI (XAI) for GNNs to enhance trust management by exploring combining symbolic and sub-symbolic methods in the area of cybersecurity that incorporate domain knowledge. We experimented with this approach by generating explanations in an industrial demonstrator system. The proposed method is shown to produce intuitive explanations for alerts for a diverse range of scenarios. Not only do the explanations provide deeper insights into the alerts, but they also lead to a reduction of false positive alerts by 66% and by 93% when including the fidelity metric.
    Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels. (arXiv:2102.02976v4 [stat.ML] UPDATED)
    Machine learning models trained by different optimization algorithms under different data distributions can exhibit distinct generalization behaviors. In this paper, we analyze the generalization of models trained by noisy iterative algorithms. We derive distribution-dependent generalization bounds by connecting noisy iterative algorithms to additive noise channels found in communication and information theory. Our generalization bounds shed light on several applications, including differentially private stochastic gradient descent (DP-SGD), federated learning, and stochastic gradient Langevin dynamics (SGLD). We demonstrate our bounds through numerical experiments, showing that they can help understand recent empirical observations of the generalization phenomena of neural networks.
    Continuous Depth Recurrent Neural Differential Equations. (arXiv:2212.13714v1 [cs.LG])
    Recurrent neural networks (RNNs) have brought a lot of advancements in sequence labeling tasks and sequence data. However, their effectiveness is limited when the observations in the sequence are irregularly sampled, where the observations arrive at irregular time intervals. To address this, continuous time variants of the RNNs were introduced based on neural ordinary differential equations (NODE). They learn a better representation of the data using the continuous transformation of hidden states over time, taking into account the time interval between the observations. However, they are still limited in their capability as they use the discrete transformations and a fixed discrete number of layers (depth) over an input in the sequence to produce the output observation. We intend to address this limitation by proposing RNNs based on differential equations which model continuous transformations over both depth and time to predict an output for a given input in the sequence. Specifically, we propose continuous depth recurrent neural differential equations (CDR-NDE) which generalizes RNN models by continuously evolving the hidden states in both the temporal and depth dimensions. CDR-NDE considers two separate differential equations over each of these dimensions and models the evolution in the temporal and depth directions alternatively. We also propose the CDR-NDE-heat model based on partial differential equations which treats the computation of hidden states as solving a heat equation over time. We demonstrate the effectiveness of the proposed models by comparing against the state-of-the-art RNN models on real world sequence labeling problems and data.
    Feature learning in neural networks and kernel machines that recursively learn features. (arXiv:2212.13881v1 [cs.LG])
    Neural networks have achieved impressive results on many technological and scientific tasks. Yet, their empirical successes have outpaced our fundamental understanding of their structure and function. By identifying mechanisms driving the successes of neural networks, we can provide principled approaches for improving neural network performance and develop simple and effective alternatives. In this work, we isolate the key mechanism driving feature learning in fully connected neural networks by connecting neural feature learning to the average gradient outer product. We subsequently leverage this mechanism to design \textit{Recursive Feature Machines} (RFMs), which are kernel machines that learn features. We show that RFMs (1) accurately capture features learned by deep fully connected neural networks, (2) close the gap between kernel machines and fully connected networks, and (3) surpass a broad spectrum of models including neural networks on tabular data. Furthermore, we demonstrate that RFMs shed light on recently observed deep learning phenomena such as grokking, lottery tickets, simplicity biases, and spurious features. We provide a Python implementation to make our method broadly accessible [\href{https://github.com/aradha/recursive_feature_machines}{GitHub}].
    Outcome-Driven Reinforcement Learning via Variational Inference. (arXiv:2104.10190v2 [cs.LG] UPDATED)
    While reinforcement learning algorithms provide automated acquisition of optimal policies, practical application of such methods requires a number of design decisions, such as manually designing reward functions that not only define the task, but also provide sufficient shaping to accomplish it. In this paper, we view reinforcement learning as inferring policies that achieve desired outcomes, rather than as a problem of maximizing rewards. To solve this inference problem, we establish a novel variational inference formulation that allows us to derive a well-shaped reward function which can be learned directly from environment interactions. From the corresponding variational objective, we also derive a new probabilistic Bellman backup operator and use it to develop an off-policy algorithm to solve goal-directed tasks. We empirically demonstrate that this method eliminates the need to hand-craft reward functions for a suite of diverse manipulation and locomotion tasks and leads to effective goal-directed behaviors.
    Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions. (arXiv:2210.13373v3 [cs.LG] UPDATED)
    We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces. Our work is motivated by practical scenarios where the target policy needs to be deterministic due to domain requirements, such as prescription of treatment dosage and duration in medicine. Although importance sampling (IS) provides a basic principle for OPE, it is ill-posed for the deterministic target policy with continuous actions. Our main idea is to relax the target policy and pose the problem as kernel-based estimation, where we learn the kernel metric in order to minimize the overall mean squared error (MSE). We present an analytic solution for the optimal metric, based on the analysis of bias and variance. Whereas prior work has been limited to scalar action spaces or kernel bandwidth selection, our work takes a step further being capable of vector action spaces and metric optimization. We show that our estimator is consistent, and significantly reduces the MSE compared to baseline OPE methods through experiments on various domains.
    EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision. (arXiv:2210.13047v2 [cs.IT] CROSS LISTED)
    Semantic communication is not focused on improving the accuracy of transmitted symbols, but is concerned with expressing the expected meaning that the symbol sequence exactly carries. However, the measurement of semantic messages and their corresponding codebook generation are still open issues. Expansion, which integrates simple things into a complex system and even generates intelligence, is truly consistent with the evolution of the human language system. We apply this idea to the semantic communication system, quantifying semantic transmission by symbol sequences and investigating the semantic information system in a similar way as Shannon's method for digital communication systems. This work is the first to discuss semantic expansion and knowledge collision in the semantic information framework. Some important theoretical results are presented, including the relationship between semantic expansion and the transmission information rate. We believe such a semantic information framework may provide a new paradigm for semantic communications, and semantic expansion and knowledge collision will be the cornerstone of semantic information theory.
    Publishing Efficient On-device Models Increases Adversarial Vulnerability. (arXiv:2212.13700v1 [cs.CR])
    Recent increases in the computational demands of deep neural networks (DNNs) have sparked interest in efficient deep learning mechanisms, e.g., quantization or pruning. These mechanisms enable the construction of a small, efficient version of commercial-scale models with comparable accuracy, accelerating their deployment to resource-constrained devices. In this paper, we study the security considerations of publishing on-device variants of large-scale models. We first show that an adversary can exploit on-device models to make attacking the large models easier. In evaluations across 19 DNNs, by exploiting the published on-device models as a transfer prior, the adversarial vulnerability of the original commercial-scale models increases by up to 100x. We then show that the vulnerability increases as the similarity between a full-scale and its efficient model increase. Based on the insights, we propose a defense, $similarity$-$unpairing$, that fine-tunes on-device models with the objective of reducing the similarity. We evaluated our defense on all the 19 DNNs and found that it reduces the transferability up to 90% and the number of queries required by a factor of 10-100x. Our results suggest that further research is needed on the security (or even privacy) threats caused by publishing those efficient siblings.
    Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data. (arXiv:2212.13827v1 [cs.LG])
    Real-world datasets exhibit imbalances of varying types and degrees. Several techniques based on re-weighting and margin adjustment of loss are often used to enhance the performance of neural networks, particularly on minority classes. In this work, we analyze the class-imbalanced learning problem by examining the loss landscape of neural networks trained with re-weighting and margin-based techniques. Specifically, we examine the spectral density of Hessian of class-wise loss, through which we observe that the network weights converge to a saddle point in the loss landscapes of minority classes. Following this observation, we also find that optimization methods designed to escape from saddle points can be effectively used to improve generalization on minority classes. We further theoretically and empirically demonstrate that Sharpness-Aware Minimization (SAM), a recent technique that encourages convergence to a flat minima, can be effectively used to escape saddle points for minority classes. Using SAM results in a 6.2\% increase in accuracy on the minority classes over the state-of-the-art Vector Scaling Loss, leading to an overall average increase of 4\% across imbalanced datasets. The code is available at: https://github.com/val-iisc/Saddle-LongTail.
    On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. (arXiv:2212.13936v1 [cs.LG])
    KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.
    Parameter-free Dynamic Graph Embedding for Link Prediction. (arXiv:2210.08189v2 [cs.LG] UPDATED)
    Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction graphs: 1) collaborative relationship among users and 2) user personalized interaction patterns. Existing methods often implicitly consider these two factors together, which may lead to noisy user modelling when the two factors diverge. In addition, they usually require time-consuming parameter learning with back-propagation, which is prohibitive for real-time user preference modelling. To this end, this paper proposes FreeGEM, a parameter-free dynamic graph embedding method for link prediction. Firstly, to take advantage of the collaborative relationships, we propose an incremental graph embedding engine to obtain user/item embeddings, which is an Online-Monitor-Offline architecture consisting of an Online module to approximately embed users/items over time, a Monitor module to estimate the approximation error in real time and an Offline module to calibrate the user/item embeddings when the online approximation errors exceed a threshold. Meanwhile, we integrate attribute information into the model, which enables FreeGEM to better model users belonging to some under represented groups. Secondly, we design a personalized dynamic interaction pattern modeller, which combines dynamic time decay with attention mechanism to model user short-term interests. Experimental results on two link prediction tasks show that FreeGEM can outperform the state-of-the-art methods in accuracy while achieving over 36X improvement in efficiency. All code and datasets can be found in https://github.com/FudanCISL/FreeGEM.
    Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method. (arXiv:2210.12941v2 [cs.LG] UPDATED)
    A large number of studies on Graph Outlier Detection (GOD) have emerged in recent years due to its wide applications, in which Unsupervised Node Outlier Detection (UNOD) on attributed networks is an important area. UNOD focuses on detecting two kinds of typical outliers in graphs: the structural outlier and the contextual outlier. Most existing works conduct experiments based on datasets with injected outliers. However, we find that the most widely-used outlier injection approach has a serious data leakage issue. By only utilizing such data leakage, a simple approach can achieve state-of-the-art performance in detecting outliers. In addition, we observe that most existing algorithms have a performance drop with varied injection settings. The other major issue is on balanced detection performance between the two types of outliers, which has not been considered by existing studies. In this paper, we analyze the cause of the data leakage issue in depth since the injection approach is a building block to advance UNOD. Moreover, we devise a novel variance-based model to detect structural outliers, which outperforms existing algorithms significantly at different injection settings. On top of this, we propose a new framework, Variance-based Graph Outlier Detection (VGOD), which combines our variance-based model and attribute reconstruction model to detect outliers in a balanced way. Finally, we conduct extensive experiments to demonstrate the effectiveness and efficiency of VGOD. The results on 5 real-world datasets validate that VGOD achieves not only the best performance in detecting outliers but also a balanced detection performance between structural and contextual outliers. Our code is available at https://github.com/goldenNormal/vgod-github.
    Learning Energy-Based Models With Adversarial Training. (arXiv:2012.06568v4 [cs.LG] UPDATED)
    We study a new approach to learning energy-based models (EBMs) based on adversarial training (AT). We show that (binary) AT learns a special kind of energy function that models the support of the data distribution, and the learning process is closely related to MCMC-based maximum likelihood learning of EBMs. We further propose improved techniques for generative modeling with AT, and demonstrate that this new approach is capable of generating diverse and realistic images. Aside from having competitive image generation performance to explicit EBMs, the studied approach is stable to train, is well-suited for image translation tasks, and exhibits strong out-of-distribution adversarial robustness. Our results demonstrate the viability of the AT approach to generative modeling, suggesting that AT is a competitive alternative approach to learning EBMs.
    Quality at the Tail. (arXiv:2212.13925v1 [cs.LG])
    Practical applications employing deep learning must guarantee inference quality. However, we found that the inference quality of state-of-the-art and state-of-the-practice in practical applications has a long tail distribution. In the real world, many tasks have strict requirements for the quality of deep learning inference, such as safety-critical and mission-critical tasks. The fluctuation of inference quality seriously affects its practical applications, and the quality at the tail may lead to severe consequences. State-of-the-art and state-of-the-practice with outstanding inference quality designed and trained under loose constraints still have poor inference quality under constraints with practical application significance. On the one hand, the neural network models must be deployed on complex systems with limited resources. On the other hand, safety-critical and mission-critical tasks need to meet more metric constraints while ensuring high inference quality. We coin a new term, ``tail quality,'' to characterize this essential requirement and challenge. We also propose a new metric, ``X-Critical-Quality,'' to measure the inference quality under certain constraints. This article reveals factors contributing to the failure of using state-of-the-art and state-of-the-practice algorithms and systems in real scenarios. Therefore, we call for establishing innovative methodologies and tools to tackle this enormous challenge.
    Feature Selection Approaches for Optimising Music Emotion Recognition Methods. (arXiv:2212.13369v1 [cs.SD])
    The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task.
    Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods. (arXiv:2212.13468v1 [cs.LG])
    Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the features learnt during optimization remain poorly understood, even in the two-layer setting. In fact, earlier work has considered either linear autoencoders or specific training regimes (leading to vanishing or diverging compression rates). Our paper addresses this gap by focusing on non-linear two-layer autoencoders trained in the challenging proportional regime in which the input dimension scales linearly with the size of the representation. Our results characterize the minimizers of the population risk, and show that such minimizers are achieved by gradient methods; their structure is also unveiled, thus leading to a concise description of the features obtained via training. For the special case of a sign activation function, our analysis establishes the fundamental limits for the lossy compression of Gaussian sources via (shallow) autoencoders. Finally, while the results are proved for Gaussian data, numerical simulations on standard datasets display the universality of the theoretical predictions.
    Deep Learning Models for River Classification at Sub-Meter Resolutions from Multispectral and Panchromatic Commercial Satellite Imagery. (arXiv:2212.13613v1 [cs.CV])
    Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and multispectral resolutions of 30 cm and 1.2 m, respectively, 2) developing multiple fully convolutional neural networks (FCN) that can learn the morphological features of water bodies in addition to their spectral properties, and 3) FCN that can classify water even from panchromatic imagery. This study focuses on rivers in the Arctic, using images from the Quickbird, WorldView, and GeoEye satellites. Because no training data are available at such high resolutions, we construct those manually. First, we use the RGB, and NIR bands of the 8-band multispectral sensors. Those trained models all achieve excellent precision and recall over 90% on validation data, aided by on-the-fly preprocessing of the training data specific to satellite imagery. In a novel approach, we then use results from the multispectral model to generate training data for FCN that only require panchromatic imagery, of which considerably more is available. Despite the smaller feature space, these models still achieve a precision and recall of over 85%. We provide our open-source codes and trained model parameters to the remote sensing community, which paves the way to a wide range of environmental hydrology applications at vastly superior accuracies and 2 orders of magnitude higher spatial resolution than previously possible.
    Online Learning for Adaptive Probing and Scheduling in Dense WLANs. (arXiv:2212.13585v1 [cs.LG])
    Existing solutions to network scheduling typically assume that the instantaneous link rates are completely known before a scheduling decision is made or consider a bandit setting where the accurate link quality is discovered only after it has been used for data transmission. In practice, the decision maker can obtain (relatively accurate) channel information, e.g., through beamforming in mmWave networks, right before data transmission. However, frequent beamforming incurs a formidable overhead in densely deployed mmWave WLANs. In this paper, we consider the important problem of throughput optimization with joint link probing and scheduling. The problem is challenging even when the link rate distributions are pre-known (the offline setting) due to the necessity of balancing the information gains from probing and the cost of reducing the data transmission opportunity. We develop an approximation algorithm with guaranteed performance when the probing decision is non-adaptive, and a dynamic programming based solution for the more challenging adaptive setting. We further extend our solutions to the online setting with unknown link rate distributions and develop a contextual-bandit based algorithm and derive its regret bound. Numerical results using data traces collected from real-world mmWave deployments demonstrate the efficiency of our solutions.
    Spectral Representation Learning for Conditional Moment Models. (arXiv:2210.16525v2 [stat.ML] UPDATED)
    Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of conditional moment restrictions. For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on various measures of ill-posedness of the hypothesis space, which are hard to validate when flexible models are used. In this work, we address this issue by proposing a procedure that automatically learns representations with controlled measures of ill-posedness. Our method approximates a linear representation defined by the spectral decomposition of a conditional expectation operator, which can be used for kernelized estimators and is known to facilitate minimax optimal estimation in certain settings. We show this representation can be efficiently estimated from data, and establish L2 consistency for the resulting estimator. We evaluate the proposed method on proximal causal inference tasks, exhibiting promising performance on high-dimensional, semi-synthetic data.
    Efficient Graph Neural Network Inference at Large Scale. (arXiv:2211.00495v3 [cs.LG] UPDATED)
    Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications. However, the enormous size of large-scale graphs hinders their applications under real-time inference scenarios. Although existing scalable GNNs leverage linear propagation to preprocess the features and accelerate the training and inference procedure, these methods still suffer from scalability issues when making inferences on unseen nodes, as the feature preprocessing requires the graph is known and fixed. To speed up the inference in the inductive setting, we propose a novel adaptive propagation order approach that generates the personalized propagation order for each node based on its topological information. This could successfully avoid the redundant computation of feature propagation. Moreover, the trade-off between accuracy and inference latency can be flexibly controlled by simple hyper-parameters to match different latency constraints of application scenarios. To compensate for the potential inference accuracy loss, we further propose Inception Distillation to exploit the multi scale reception information and improve the inference performance. Extensive experiments are conducted on four public datasets with different scales and characteristics, and the experimental results show that our proposed inference acceleration framework outperforms the SOTA graph inference acceleration baselines in terms of both accuracy and efficiency. In particular, the advantage of our proposed method is more significant on larger-scale datasets, and our framework achieves $75\times$ inference speedup on the largest Ogbn-products dataset.
    GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning. (arXiv:2212.13425v1 [cs.LG])
    Self-supervised learning is a popular and powerful method for utilizing large amounts of unlabeled data, for which a wide variety of training objectives have been proposed in the literature. In this study, we perform a Bayesian analysis of state-of-the-art self-supervised learning objectives and propose a unified formulation based on likelihood learning. Our analysis suggests a simple method for integrating self-supervised learning with generative models, allowing for the joint training of these two seemingly distinct approaches. We refer to this combined framework as GEDI, which stands for GEnerative and DIscriminative training. Additionally, we demonstrate an instantiation of the GEDI framework by integrating an energy-based model with a cluster-based self-supervised learning model. Through experiments on synthetic and real-world data, including SVHN, CIFAR10, and CIFAR100, we show that GEDI outperforms existing self-supervised learning strategies in terms of clustering performance by a wide margin. We also demonstrate that GEDI can be integrated into a neural-symbolic framework to address tasks in the small data regime, where it can use logical constraints to further improve clustering and classification performance.
    S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning. (arXiv:2212.13523v1 [eess.SP])
    Seismic data often undergoes severe noise due to environmental factors, which seriously affects subsequent applications. Traditional hand-crafted denoisers such as filters and regularizations utilize interpretable domain knowledge to design generalizable denoising techniques, while their representation capacities may be inferior to deep learning denoisers, which can learn complex and representative denoising mappings from abundant training pairs. However, due to the scarcity of high-quality training pairs, deep learning denoisers may sustain some generalization issues over various scenarios. In this work, we propose a self-supervised method that combines the capacities of deep denoiser and the generalization abilities of hand-crafted regularization for seismic data random noise attenuation. Specifically, we leverage the Self2Self (S2S) learning framework with a trace-wise masking strategy for seismic data denoising by solely using the observed noisy data. Parallelly, we suggest the weighted total variation (WTV) to further capture the horizontal local smooth structure of seismic data. Our method, dubbed as S2S-WTV, enjoys both high representation abilities brought from the self-supervised deep network and good generalization abilities of the hand-crafted WTV regularizer and the self-supervised nature. Therefore, our method can more effectively and stably remove the random noise and preserve the details and edges of the clean signal. To tackle the S2S-WTV optimization model, we introduce an alternating direction multiplier method (ADMM)-based algorithm. Extensive experiments on synthetic and field noisy seismic data demonstrate the effectiveness of our method as compared with state-of-the-art traditional and deep learning-based seismic data denoising methods.
    Using attention methods to predict judicial outcomes. (arXiv:2207.08823v2 [cs.LG] UPDATED)
    Legal Judgment Prediction is one of the most acclaimed fields for the combined area of NLP, AI, and Law. By legal prediction we mean an intelligent systems capable to predict specific judicial characteristics, such as judicial outcome, a judicial class, predict an specific case. In this research, we have used AI classifiers to predict judicial outcomes in the Brazilian legal system. For this purpose, we developed a text crawler to extract data from the official Brazilian electronic legal systems. These texts formed a dataset of second-degree murder and active corruption cases. We applied different classifiers, such as Support Vector Machines and Neural Networks, to predict judicial outcomes by analyzing textual features from the dataset. Our research showed that Regression Trees, Gated Recurring Units and Hierarchical Attention Networks presented higher metrics for different subsets. As a final goal, we explored the weights of one of the algorithms, the Hierarchical Attention Networks, to find a sample of the most important words used to absolve or convict defendants.
    Deep Reinforcement Learning for Wind and Energy Storage Coordination in Wholesale Energy and Ancillary Service Markets. (arXiv:2212.13368v1 [eess.SY])
    Global power systems are increasingly reliant on wind energy as a mitigation strategy for climate change. However, the variability of wind energy causes system reliability to erode, resulting in the wind being curtailed and, ultimately, leading to substantial economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) that serve as onsite backup sources. Yet, this auxiliary role may significantly hamper the BESS's capacity to generate revenues from the electricity market, particularly in conducting energy arbitrage in the Spot market and providing frequency control ancillary services (FCAS) in the FCAS markets. Ideal BESS scheduling should effectively balance the BESS's role in absorbing onsite wind curtailment and trading in the electricity market, but it is difficult in practice because of the underlying coordination complexity and the stochastic nature of energy prices and wind generation. In this study, we investigate the bidding strategy of a wind-battery system co-located and participating simultaneously in both the Spot and Regulation FCAS markets. We propose a deep reinforcement learning (DRL)-based approach that decouples the market participation of the wind-battery system into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while simultaneously bidding in the wholesale Spot and FCAS markets to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy for the wind-battery system and find that our strategy generates significantly higher revenue and responds better to wind curtailment compared to an optimization-based benchmark. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to individual market participation.
    Challenges in anomaly and change point detection. (arXiv:2212.13520v1 [cs.LG])
    This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced. On the other, a selection of important surveys and books, as well as two selected active research topics in the field, are presented.
    FedBA: Non-IID Federated Learning Framework in UAV Networks. (arXiv:2210.04699v2 [cs.LG] UPDATED)
    With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones. At the same time, the large amount of data collected by drones may incur greater communication overhead when transferred to the cloud. Federated learning as a means of privacy protection can effectively solve the above two problems. However, federated learning when applied to UAV networks also needs to consider the heterogeneity of data, which is caused by regional differences in UAV regulation. In response, this paper proposes a new algorithm FedBA to optimize the global model and solves the data heterogeneity problem. In addition, we apply the algorithm to some real datasets, and the experimental results show that the algorithm outperforms other algorithms and improves the accuracy of the local model for UAVs.
    Martian Ionosphere Electron Density Prediction Using Bagged Trees. (arXiv:2211.01902v2 [physics.ao-ph] UPDATED)
    The availability of Martian atmospheric data provided by several Martian missions broadened the opportunity to investigate and study the conditions of the Martian ionosphere. As such, ionospheric models play a crucial part in improving our understanding of ionospheric behavior in response to different spatial, temporal, and space weather conditions. This work represents an initial attempt to construct an electron density prediction model of the Martian ionosphere using machine learning. The model targets the ionosphere at solar zenith ranging from 70 to 90 degrees, and as such only utilizes observations from the Mars Global Surveyor mission. The performance of different machine learning methods was compared in terms of root mean square error, coefficient of determination, and mean absolute error. The bagged regression trees method performed best out of all the evaluated methods. Furthermore, the optimized bagged regression trees model outperformed other Martian ionosphere models from the literature (MIRI and NeMars) in finding the peak electron density value, and the peak density height in terms of root-mean-square error and mean absolute error.
    Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing. (arXiv:2212.13598v1 [cs.NI])
    Spectrum coexistence is essential for next generation (NextG) systems to share the spectrum with incumbent (primary) users and meet the growing demand for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service (CBRS) band, where the 5G and beyond communication systems need to sense the spectrum and then access the channel in an opportunistic manner when the incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity classifier based on a deep neural network is needed for low misdetection (to protect incumbent users) and low false alarm (to achieve high throughput for NextG). In a dynamic wireless environment, the classifier can only be used for a limited period of time, i.e., coherence time. A portion of this period is used for learning to collect sensing results and train a classifier, and the rest is used for transmissions. In spectrum sharing systems, there is a well-known tradeoff between the sensing time and the transmission time. While increasing the sensing time can increase the spectrum sensing accuracy, there is less time left for data transmissions. In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier. We consider both additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this GAN-based approach can significantly improve both the protection of the high-priority user and the throughput of the NextG user (more in Rayleigh channels than AWGN channels).
    Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. (arXiv:2207.13243v4 [cs.LG] UPDATED)
    The last decade of machine learning has seen drastic increases in scale and capabilities. Deep neural networks (DNNs) are increasingly being deployed in the real world. However, they are difficult to analyze, raising concerns about using them without a rigorous understanding of how they function. Effective tools for interpreting them will be important for building more trustworthy AI by helping to identify problems, fix bugs, and improve basic understanding. In particular, "inner" interpretability techniques, which focus on explaining the internal components of DNNs, are well-suited for developing a mechanistic understanding, guiding manual modifications, and reverse engineering solutions. Much recent work has focused on DNN interpretability, and rapid progress has thus far made a thorough systematization of methods difficult. In this survey, we review over 300 works with a focus on inner interpretability tools. We introduce a taxonomy that classifies methods by what part of the network they help to explain (weights, neurons, subnetworks, or latent representations) and whether they are implemented during (intrinsic) or after (post hoc) training. To our knowledge, we are also the first to survey a number of connections between interpretability research and work in adversarial robustness, continual learning, modularity, network compression, and studying the human visual system. We discuss key challenges and argue that the status quo in interpretability research is largely unproductive. Finally, we highlight the importance of future work that emphasizes diagnostics, debugging, adversaries, and benchmarking in order to make interpretability tools more useful to engineers in practical applications.
    Deep Spatial Domain Generalization. (arXiv:2210.00729v2 [cs.LG] UPDATED)
    Spatial autocorrelation and spatial heterogeneity widely exist in spatial data, which make the traditional machine learning model perform badly. Spatial domain generalization is a spatial extension of domain generalization, which can generalize to unseen spatial domains in continuous 2D space. Specifically, it learns a model under varying data distributions that generalizes to unseen domains. Although tremendous success has been achieved in domain generalization, there exist very few works on spatial domain generalization. The advancement of this area is challenged by: 1) Difficulty in characterizing spatial heterogeneity, and 2) Difficulty in obtaining predictive models for unseen locations without training data. To address these challenges, this paper proposes a generic framework for spatial domain generalization. Specifically, We develop the spatial interpolation graph neural network that handles spatial data as a graph and learns the spatial embedding on each node and their relationships. The spatial interpolation graph neural network infers the spatial embedding of an unseen location during the test phase. Then the spatial embedding of the target location is used to decode the parameters of the downstream-task model directly on the target location. Finally, extensive experiments on thirteen real-world datasets demonstrate the proposed method's strength.
    Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?. (arXiv:2206.02016v4 [cs.LG] UPDATED)
    The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The $L^2$ Physics-Informed Loss is the de-facto standard in training Physics-Informed Neural Networks. In this paper, we challenge this common practice by investigating the relationship between the loss function and the approximation quality of the learned solution. In particular, we leverage the concept of stability in the literature of partial differential equation to study the asymptotic behavior of the learned solution as the loss approaches zero. With this concept, we study an important class of high-dimensional non-linear PDEs in optimal control, the Hamilton-Jacobi-Bellman(HJB) Equation, and prove that for general $L^p$ Physics-Informed Loss, a wide class of HJB equation is stable only if $p$ is sufficiently large. Therefore, the commonly used $L^2$ loss is not suitable for training PINN on those equations, while $L^{\infty}$ loss is a better choice. Based on the theoretical insight, we develop a novel PINN training algorithm to minimize the $L^{\infty}$ loss for HJB equations which is in a similar spirit to adversarial training. The effectiveness of the proposed algorithm is empirically demonstrated through experiments. Our code is released at https://github.com/LithiumDA/L_inf-PINN.
    Riemannian stochastic approximation algorithms. (arXiv:2206.06795v3 [math.OC] UPDATED)
    We examine a wide class of stochastic approximation algorithms for solving (stochastic) nonlinear problems on Riemannian manifolds. Such algorithms arise naturally in the study of Riemannian optimization, game theory and optimal transport, but their behavior is much less understood compared to the Euclidean case because of the lack of a global linear structure on the manifold. We overcome this difficulty by introducing a suitable Fermi coordinate frame which allows us to map the asymptotic behavior of the Riemannian Robbins-Monro (RRM) algorithms under study to that of an associated deterministic dynamical system. In so doing, we provide a general template of almost sure convergence results that mirrors and extends the existing theory for Euclidean Robbins-Monro schemes, despite the significant complications that arise due to the curvature and topology of the underlying manifold. We showcase the flexibility of the proposed framework by applying it to a range of retraction-based variants of the popular optimistic / extra-gradient methods for solving minimization problems and games, and we provide a unified treatment for their convergence.
    Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning. (arXiv:2207.09081v5 [cs.LG] UPDATED)
    As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations. However, how to discover and represent causalities remains a huge gap that hinders the development of causal RL. In this paper, we augment Goal-Conditioned RL (GCRL) with Causal Graph (CG), a structure built upon the relation between objects and events. We novelly formulate the GCRL problem into variational likelihood maximization with CG as latent variables. To optimize the derived objective, we propose a framework with theoretical performance guarantees that alternates between two steps: using interventional data to estimate the posterior of CG; using CG to learn generalizable models and interpretable policies. Due to the lack of public benchmarks that verify generalization capability under reasoning, we design nine tasks and then empirically show the effectiveness of the proposed method against five baselines on these tasks. Further theoretical analysis shows that our performance improvement is attributed to the virtuous cycle of causal discovery, transition modeling, and policy training, which aligns with the experimental evidence in extensive ablation studies.
    Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. (arXiv:2206.00873v2 [cs.LG] UPDATED)
    This study considers online learning with general directed feedback graphs. For this problem, we present best-of-both-worlds algorithms that achieve nearly tight regret bounds for adversarial environments as well as poly-logarithmic regret bounds for stochastic environments. As Alon et al. [2015] have shown, tight regret bounds depend on the structure of the feedback graph: strongly observable graphs yield minimax regret of $\tilde{\Theta}( \alpha^{1/2} T^{1/2} )$, while weakly observable graphs induce minimax regret of $\tilde{\Theta}( \delta^{1/3} T^{2/3} )$, where $\alpha$ and $\delta$, respectively, represent the independence number of the graph and the domination number of a certain portion of the graph. Our proposed algorithm for strongly observable graphs has a regret bound of $\tilde{O}( \alpha^{1/2} T^{1/2} ) $ for adversarial environments, as well as of $ {O} ( \frac{\alpha (\ln T)^3 }{\Delta_{\min}} ) $ for stochastic environments, where $\Delta_{\min}$ expresses the minimum suboptimality gap. This result resolves an open question raised by Erez and Koren [2021]. We also provide an algorithm for weakly observable graphs that achieves a regret bound of $\tilde{O}( \delta^{1/3}T^{2/3} )$ for adversarial environments and poly-logarithmic regret for stochastic environments. The proposed algorithms are based on the follow-the-regularized-leader approach combined with newly designed update rules for learning rates.
    Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases. (arXiv:2203.07345v2 [cs.CV] UPDATED)
    Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical data. Recently proposed collaborative learning methods such as Federated Learning (FL) allow for training on remote datasets without the need to explicitly share data. Even so, data annotation still represents a bottleneck, particularly in medicine and surgery where clinical expertise is often required. With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition. By leveraging temporal patterns in the labeled data, FedCy helps guide unsupervised training on unlabeled data towards learning task-specific features for phase recognition. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases using a newly collected multi-institutional dataset of laparoscopic cholecystectomy videos. Furthermore, we demonstrate that our approach also learns more generalizable features when tested on data from an unseen domain.
    Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs. (arXiv:2206.09144v6 [cs.LG] UPDATED)
    Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the broad interest in developing and evaluating GNNs, they have been assessed with limited benchmark datasets. As a result, the existing evaluation of GNNs lacks fine-grained analysis from various characteristics of graphs. Motivated by this, we conduct extensive experiments with a synthetic graph generator that can generate graphs having controlled characteristics for fine-grained analysis. Our empirical studies clarify the strengths and weaknesses of GNNs from four major characteristics of real-world graphs with class labels of nodes, i.e., 1) class size distributions (balanced vs. imbalanced), 2) edge connection proportions between classes (homophilic vs. heterophilic), 3) attribute values (biased vs. random), and 4) graph sizes (small vs. large). In addition, to foster future research on GNNs, we publicly release our codebase that allows users to evaluate various GNNs with various graphs. We hope this work offers interesting insights for future research.
    Warmth and competence in human-agent cooperation. (arXiv:2201.13448v2 [cs.HC] UPDATED)
    Interaction and cooperation with humans are overarching aspirations of artificial intelligence (AI) research. Recent studies demonstrate that AI agents trained with deep reinforcement learning are capable of collaborating with humans. These studies primarily evaluate human compatibility through "objective" metrics such as task performance, obscuring potential variation in the levels of trust and subjective preference that different agents garner. To better understand the factors shaping subjective preferences in human-agent cooperation, we train deep reinforcement learning agents in Coins, a two-player social dilemma. We recruit participants for a human-agent cooperation study and measure their impressions of the agents they encounter. Participants' perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics. Drawing inspiration from social science and biology research, we subsequently implement a new "partner choice" framework to elicit revealed preferences: after playing an episode with an agent, participants are asked whether they would like to play the next round with the same agent or to play alone. As with stated preferences, social perception better predicts participants' revealed preferences than does objective performance. Given these results, we recommend human-agent interaction researchers routinely incorporate the measurement of social perception and subjective preferences into their studies.
    Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks. (arXiv:2212.13970v1 [cs.LG])
    Transfer learning is a popular technique for improving the performance of neural networks. However, existing methods are limited to transferring parameters between networks with same architectures. We present a method for transferring parameters between neural networks with different architectures. Our method, called DPIAT, uses dynamic programming to match blocks and layers between architectures and transfer parameters efficiently. Compared to existing parameter prediction and random initialization methods, it significantly improves training efficiency and validation accuracy. In experiments on ImageNet, our method improved validation accuracy by an average of 1.6 times after 50 epochs of training. DPIAT allows both researchers and neural architecture search systems to modify trained networks and reuse knowledge, avoiding the need for retraining from scratch. We also introduce a network architecture similarity measure, enabling users to choose the best source network without any training.
    Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis. (arXiv:1911.12426v6 [cs.LG] UPDATED)
    We develop methods for reducing the dimensionality of large data sets, common in biomedical applications. Learning about patients using genetic data often includes more features than observations, which makes direct supervised learning difficult. One method of reducing the feature space is to use latent Dirichlet allocation to group genetic variants in an unsupervised manner. Latent Dirichlet allocation describes a patient as a mixture of topics corresponding to genetic variants. This can be generalized as a Bayesian tensor decomposition to account for multiple feature variables. Our most significant contributions are with hierarchical topic modeling. We design distinct methods of incorporating hierarchical topic modeling, based on nested Chinese restaurant processes and Pachinko Allocation Machine, into Bayesian tensor decomposition. We apply these models to examine patients with one of four common types of cancer (breast, lung, prostate, and colorectal) and siblings with and without autism spectrum disorder. We linked the genes with their biological pathways and combine this information into a tensor of patients, counts of their genetic variants, and the genes' membership in pathways. We find that our trained models outperform baseline models, with respect to coherence, by up to 40%.
    A Robust Cybersecurity Topic Classification Tool. (arXiv:2109.02473v3 [cs.IR] UPDATED)
    In this research, we use user defined labels from three internet text sources (Reddit, Stackexchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural text. We analyze the false positive and false negative rates of each of the 21 model's in a cross validation experiment. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as the decision mechanism for detecting cybersecurity related text. We also show that the majority vote mechanism of the CTC tool provides lower false negative and false positive rates on average than any of the 21 individual models. We show that the CTC tool is scalable to the hundreds of thousands of documents with a wall clock time on the order of hours.
    Continual Learning with Invertible Generative Models. (arXiv:2202.05694v2 [cs.LG] UPDATED)
    Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of the weights (using, e.g., their importance on past tasks), and rehearsal strategies, where the network is constantly re-trained on past data. Generative models have also been applied for the latter, in order to have endless sources of data. In this paper, we propose a novel method that combines the strengths of regularization and generative-based rehearsal approaches. Our generative model consists of a normalizing flow (NF), a probabilistic and invertible neural network, trained on the internal embeddings of the network. By keeping a single NF throughout the training process, we show that our memory overhead remains constant. In addition, exploiting the invertibility of the NF, we propose a simple approach to regularize the network's embeddings with respect to past tasks. We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.
    A Local-Pattern Related Look-Up Table. (arXiv:2212.13922v1 [cs.AI])
    This paper describes a Relevance-Zone pattern table (RZT) that can be used to replace a traditional transposition table. An RZT stores exact game values for patterns that are discovered during a Relevance-Zone-Based Search (RZS), which is the current state-of-the-art in solving L&D problems in Go. Positions that share the same pattern can reuse the same exact game value in the RZT. The pattern matching scheme for RZTs is implemented using a radix tree, taking into consideration patterns with different shapes. To improve the efficiency of table lookups, we designed a heuristic that prevents redundant lookups. The heuristic can safely skip previously queried patterns for a given position, reducing the overhead to 10% of the original cost. We also analyze the time complexity of the RZT both theoretically and empirically. Experiments show the overhead of traversing the radix tree in practice during lookup remain flat logarithmically in relation to the number of entries stored in the table. Experiments also show that the use of an RZT instead of a traditional transposition table significantly reduces the number of searched nodes on two data sets of 7x7 and 19x19 L&D Go problems.
    On Implicit Bias in Overparameterized Bilevel Optimization. (arXiv:2212.14032v1 [cs.LG])
    Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparameterized. In this case, there are many ways to choose among optima that achieve equivalent objective values. Inspired by recent studies of the implicit bias induced by optimization algorithms in single-level optimization, we investigate the implicit bias of gradient-based algorithms for bilevel optimization. We delineate two standard BLO methods -- cold-start and warm-start -- and show that the converged solution or long-run behavior depends to a large degree on these and other algorithmic choices, such as the hypergradient approximation. We also show that the inner solutions obtained by warm-start BLO can encode a surprising amount of information about the outer objective, even when the outer parameters are low-dimensional. We believe that implicit bias deserves as central a role in the study of bilevel optimization as it has attained in the study of single-level neural net optimization.
    Confusion Matrices and Accuracy Statistics for Binary Classifiers Using Unlabeled Data: The Diagnostic Test Approach. (arXiv:2208.12664v2 [stat.ML] UPDATED)
    Medical researchers have solved the problem of estimating the sensitivity and specificity of binary medical diagnostic tests without gold standard tests for comparison. That problem is the same as estimating confusion matrices for classifiers on unlabeled data. This article describes how to modify the diagnostic test solutions to estimate confusion matrices and accuracy statistics for supervised or unsupervised binary classifiers on unlabeled data.
    Beyond the Golden Ratio for Variational Inequality Algorithms. (arXiv:2212.13955v1 [math.OC])
    We improve the understanding of the $\textit{golden ratio algorithm}$, which solves monotone variational inequalities (VI) and convex-concave min-max problems via the distinctive feature of adapting the step sizes to the local Lipschitz constants. Adaptive step sizes not only eliminate the need to pick hyperparameters, but they also remove the necessity of global Lipschitz continuity and can increase from one iteration to the next. We first establish the equivalence of this algorithm with popular VI methods such as reflected gradient, Popov or optimistic gradient descent-ascent in the unconstrained case with constant step sizes. We then move on to the constrained setting and introduce a new analysis that allows to use larger step sizes, to complete the bridge between the golden ratio algorithm and the existing algorithms in the literature. Doing so, we actually eliminate the link between the golden ratio $\frac{1+\sqrt{5}}{2}$ and the algorithm. Moreover, we improve the adaptive version of the algorithm, first by removing the maximum step size hyperparameter (an artifact from the analysis) to improve the complexity bound, and second by adjusting it to nonmonotone problems with weak Minty solutions, with superior empirical performance.
    What Do Compressed Multilingual Machine Translation Models Forget?. (arXiv:2205.10828v3 [cs.CL] UPDATED)
    Recently, very large pre-trained models achieve state-of-the-art results in various natural language processing (NLP) tasks, but their size makes it more challenging to apply them in resource-constrained environments. Compression techniques allow to drastically reduce the size of the models and therefore their inference time with negligible impact on top-tier metrics. However, the general performance averaged across multiple tasks and/or languages may hide a drastic performance drop on under-represented features, which could result in the amplification of biases encoded by the models. In this work, we assess the impact of compression methods on Multilingual Neural Machine Translation models (MNMT) for various language groups, gender, and semantic biases by extensive analysis of compressed models on different machine translation benchmarks, i.e. FLORES-101, MT-Gender, and DiBiMT. We show that the performance of under-represented languages drops significantly, while the average BLEU metric only slightly decreases. Interestingly, the removal of noisy memorization with compression leads to a significant improvement for some medium-resource languages. Finally, we demonstrate that compression amplifies intrinsic gender and semantic biases, even in high-resource languages. Code: https://github.com/alirezamshi/bias-compressedMT
    Towards Learning Abstractions via Reinforcement Learning. (arXiv:2212.13980v1 [cs.AI])
    In this paper we take the first steps in studying a new approach to synthesis of efficient communication schemes in multi-agent systems, trained via reinforcement learning. We combine symbolic methods with machine learning, in what is referred to as a neuro-symbolic system. The agents are not restricted to only use initial primitives: reinforcement learning is interleaved with steps to extend the current language with novel higher-level concepts, allowing generalisation and more informative communication via shorter messages. We demonstrate that this approach allow agents to converge more quickly on a small collaborative construction task.
    SiT: Self-supervised vIsion Transformer. (arXiv:2104.03602v3 [cs.CV] UPDATED)
    Self-supervised learning methods are gaining increasing traction in computer vision due to their recent success in reducing the gap with supervised learning. In natural language processing (NLP) self-supervised learning and transformers are already the methods of choice. The recent literature suggests that the transformers are becoming increasingly popular also in computer vision. So far, the vision transformers have been shown to work well when pretrained either using a large scale supervised data or with some kind of co-supervision, e.g. in terms of teacher network. These supervised pretrained vision transformers achieve very good results in downstream tasks with minimal changes. In this work we investigate the merits of self-supervised learning for pretraining image/vision transformers and then using them for downstream classification tasks. We propose Self-supervised vIsion Transformers (SiT) and discuss several self-supervised training mechanisms to obtain a pretext model. The architectural flexibility of SiT allows us to use it as an autoencoder and work with multiple self-supervised tasks seamlessly. We show that a pretrained SiT can be finetuned for a downstream classification task on small scale datasets, consisting of a few thousand images rather than several millions. The proposed approach is evaluated on standard datasets using common protocols. The results demonstrate the strength of the transformers and their suitability for self-supervised learning. We outperformed existing self-supervised learning methods by large margin. We also observed that SiT is good for few shot learning and also showed that it is learning useful representation by simply training a linear classifier on top of the learned features from SiT. Pretraining, finetuning, and evaluation codes will be available under: https://github.com/Sara-Ahmed/SiT.
    Towards a variational Jordan-Lee-Preskill quantum algorithm. (arXiv:2109.05547v4 [quant-ph] UPDATED)
    Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices. In this work, we formulate the theory of (time-dependent) variational quantum simulation of the 1+1 dimensional $\lambda \phi^4$ quantum field theory including encoding, state preparation, and time evolution, with several numerical simulation results. These algorithms could be understood as near-term variational quantum circuit (quantum neural network) analogs of the Jordan-Lee-Preskill algorithm, the basic algorithm for simulating quantum field theory using universal quantum devices. Besides, we highlight the advantages of encoding with harmonic oscillator basis based on the LSZ reduction formula and several computational efficiency such as when implementing a bosonic version of the unitary coupled cluster ansatz to prepare initial states. We also discuss how to circumvent the "spectral crowding" problem in the quantum field theory simulation and appraise our algorithm by both state and subspace fidelities.
    Persistence-based operators in machine learning. (arXiv:2212.13985v1 [cs.LG])
    Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.
    Federated Causal Inference in Heterogeneous Observational Data. (arXiv:2107.11732v4 [cs.LG] UPDATED)
    We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual-level data cannot be shared across sites; the sites may also have heterogeneous populations and treatment assignment mechanisms. Motivated by these considerations, we develop federated methods to draw inference on the average treatment effects of combined data across sites. Our methods first compute summary statistics locally using propensity scores and then aggregate these statistics across sites to obtain point and variance estimators of average treatment effects. We show that these estimators are consistent and asymptotically normal. To achieve these asymptotic properties, we find that the aggregation schemes need to account for the heterogeneity in treatment assignments and in outcomes across sites. We demonstrate the validity of our federated methods through a comparative study of two large medical claims databases.
    Cramming: Training a Language Model on a Single GPU in One Day. (arXiv:2212.14034v1 [cs.CL])
    Recent trends in language modeling have focused on increasing performance through scaling, and have resulted in an environment where training language models is out of reach for most researchers and practitioners. While most in the community are asking how to push the limits of extreme computation, we ask the opposite question: How far can we get with a single GPU in just one day? We investigate the downstream performance achievable with a transformer-based language model trained completely from scratch with masked language modeling for a single day on a single consumer GPU. Aside from re-analyzing nearly all components of the pretraining pipeline for this scenario and providing a modified pipeline with performance close to BERT, we investigate why scaling down is hard, and which modifications actually improve performance in this scenario. We provide evidence that even in this constrained setting, performance closely follows scaling laws observed in large-compute settings. Through the lens of scaling laws, we categorize a range of recent improvements to training and architecture and discuss their merit and practical applicability (or lack thereof) for the limited compute setting.
    Efficient comparison of independence structures of log-linear models. (arXiv:1907.08892v4 [cs.LG] UPDATED)
    Log-linear models are a family of probability distributions which capture relationships between variables. They have been proven useful in a wide variety of fields such as epidemiology, economics and sociology. The interest in using these models is that they are able to capture context-specific independencies, relationships that provide richer structure to the model. Many approaches exist for automatic learning of the independence structure of log-linear models from data. The methods for evaluating these approaches, however, are limited, and are mostly based on indirect measures of the complete density of the probability distribution. Such computation requires additional learning of the numerical parameters of the distribution, which introduces distortions when used for comparing structures. This work addresses this issue by presenting the first measure for the direct and efficient comparison of independence structures of log-linear models. Our method relies only on the independence structure of the models, which is useful when the interest lies in obtaining knowledge from said structure, or when comparing the performance of structure learning algorithms, among other possible uses. We present proof that the measure is a metric, and a method for its computation that is efficient in the number of variables of the domain.
    Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks. (arXiv:2106.07106v2 [cs.LG] UPDATED)
    We introduce and analyze NetOTC, a procedure for the comparison and soft alignment of weighted networks. Given two networks and a cost function relating their vertices, NetOTC finds an appropriate coupling of their associated random walks having minimum expected cost. The minimizing cost provides a numerical measure of the difference between the networks, while the optimal transport plan itself provides interpretable, probabilistic alignments of the vertices and edges of the two networks. The cost function employed can be based, for example, on vertex degrees, externally defined features, or Euclidean embeddings. Coupling of the full random walks, rather than their stationary distributions, ensures that NetOTC captures local and global information about the given networks. NetOTC applies to networks of different size and structure, and does not the require specification of free parameters. NetOTC respects edges, in the sense that vertex pairs in the given networks are aligned with positive probability only if they are adjacent in the given networks. We investigate a number of theoretical properties of NetOTC that support its use, including metric properties of the minimizing cost and its connection with short- and long-run average cost. In addition, we introduce a new notion of factor for weighted networks, and establish a close connection between factors and NetOTC. Complementing the theory, we present simulations and numerical experiments showing that NetOTC is competitive with, and sometimes superior to, other optimal transport-based network comparison methods in the literature. In particular, NetOTC shows promise in identifying isomorphic networks using a local (degree-based) cost function.
    Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition. (arXiv:2212.13918v1 [eess.SP])
    Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic or static activities (e.g, walking, lying, cycling, etc.) has become a well studied problem. What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non periodic, and occur less frequently when compared with the often large amount of irrelevant background activities. Recent works suggested that sequential DL models (such as LSTMs) have great potential for modeling nonperiodic behaviours, and in this paper we studied some LSTM training strategies for SAR. Specifically, we proposed two simple yet effective LSTM variants, namely delay model and inverse model, for two SAR scenarios (with and without time critical requirement). For time critical SAR, the delay model can effectively exploit predefined delay intervals (within tolerance) in form of contextual information for improved performance. For regular SAR task, the second proposed, inverse model can learn patterns from the time series in an inverse manner, which can be complementary to the forward model (i.e.,LSTM), and combining both can boost the performance. These two LSTM variants are very practical, and they can be deemed as training strategies without alteration of the LSTM fundamentals. We also studied some additional LSTM training strategies, which can further improve the accuracy. We evaluated our models on two SAR and one non-SAR datasets, and the promising results demonstrated the effectiveness of our approaches in HAR applications.
    Machine Learning for Detecting Malware in PE Files. (arXiv:2212.13988v1 [cs.CR])
    The increasing number of sophisticated malware poses a major cybersecurity threat. Portable executable (PE) files are a common vector for such malware. In this work we review and evaluate machine learning-based PE malware detection techniques. Using a large benchmark dataset, we evaluate features of PE files using the most common machine learning techniques to detect malware.
    A System-Level View on Out-of-Distribution Data in Robotics. (arXiv:2212.14020v1 [cs.RO])
    When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.
    Benchmarking Graph Neural Networks. (arXiv:2003.00982v5 [cs.LG] UPDATED)
    In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas. As of December 2022, the GitHub repository has reached 2,000 stars and 380 forks, which demonstrates the utility of the proposed open-source framework through the wide usage by the GNN community. In this paper, we present an updated version of our benchmark with a concise presentation of the aforementioned framework characteristics, an additional medium-sized molecular dataset AQSOL, similar to the popular ZINC, but with a real-world measured chemical target, and discuss how this framework can be leveraged to explore new GNN designs and insights. As a proof of value of our benchmark, we study the case of graph positional encoding (PE) in GNNs, which was introduced with this benchmark and has since spurred interest of exploring more powerful PE for Transformers and GNNs in a robust experimental setting.
    ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods. (arXiv:2212.13890v1 [eess.SP])
    Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasive and the laboratory analysis can be slow or inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool which is quick and simple to acquire. However, the problem of estimating continuous electrolyte concentrations directly from ECGs is not well-studied. We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations. Methods: We explore the use of deep neural networks (DNNs) for this task. We analyze the regression performance across four electrolytes, utilizing a novel dataset containing over 290000 ECGs. For improved understanding, we also study the full spectrum from continuous predictions to binary classification of extreme concentration levels. To enhance clinical usefulness, we finally extend to a probabilistic regression approach and evaluate different uncertainty estimates. Results: We find that the performance varies significantly between different electrolytes, which is clinically justified in the interplay of electrolytes and their manifestation in the ECG. We also compare the regression accuracy with that of traditional machine learning models, demonstrating superior performance of DNNs. Conclusion: Discretization can lead to good classification performance, but does not help solve the original problem of predicting continuous concentration levels. While probabilistic regression demonstrates potential practical usefulness, the uncertainty estimates are not particularly well-calibrated. Significance: Our study is a first step towards accurate and reliable ECG-based prediction of electrolyte concentration levels.
    Extreme Image Transformations Affect Humans and Machines Differently. (arXiv:2212.13967v1 [cs.CV])
    Some recent artificial neural networks (ANNs) have claimed to model important aspects of primate neural and human performance data. Their demonstrated performance in object recognition is still dependent on exploiting low-level features for solving visual tasks in a way that humans do not. Out-of-distribution or adversarial input is challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by certain extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evaluate humans and networks on an object recognition task. We show that machines perform better than humans for certain transforms and struggle to perform at par with humans on other transforms that are easy for humans. We quantify the differences in accuracy for humans and machines and find a ranking for our transforms through human data. We also suggest how certain characteristics of human visual processing can be adapted to improve the performance of ANNs for our difficult-for-machines transforms.
    Exploration with Limited Memory: Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits. (arXiv:2004.04666v2 [cs.DS] UPDATED)
    Consider the following abstract coin tossing problem: Given a set of $n$ coins with unknown biases, find the most biased coin using a minimal number of coin tosses. This is a common abstraction of various exploration problems in theoretical computer science and machine learning and has been studied extensively over the years. In particular, algorithms with optimal sample complexity (number of coin tosses) have been known for this problem for quite some time. Motivated by applications to processing massive datasets, we study the space complexity of solving this problem with optimal number of coin tosses in the streaming model. In this model, the coins are arriving one by one and the algorithm is only allowed to store a limited number of coins at any point -- any coin not present in the memory is lost and can no longer be tossed or compared to arriving coins. Prior algorithms for the coin tossing problem with optimal sample complexity are based on iterative elimination of coins which inherently require storing all the coins, leading to memory-inefficient streaming algorithms. We remedy this state-of-affairs by presenting a series of improved streaming algorithms for this problem: we start with a simple algorithm which require storing only $O(\log{n})$ coins and then iteratively refine it further and further, leading to algorithms with $O(\log\log{(n)})$ memory, $O(\log^*{(n)})$ memory, and finally a one that only stores a single extra coin in memory -- the same exact space needed to just store the best coin throughout the stream. Furthermore, we extend our algorithms to the problem of finding the $k$ most biased coins as well as other exploration problems such as finding top-$k$ elements using noisy comparisons or finding an $\epsilon$-best arm in stochastic multi-armed bandits, and obtain efficient streaming algorithms for these problems.
    Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization. (arXiv:2105.00321v3 [math.OC] UPDATED)
    This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve $\mathcal{O}(T^{\max\{\kappa,1-\kappa\}})$ static network regret and $\mathcal{O}(T^{1-\kappa/2})$ network cumulative constraint violation, where $T$ is the time horizon and $\kappa\in(0,1)$ is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to $\mathcal{O}(T^{\kappa})$. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
    Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes. (arXiv:2201.12569v2 [cs.LG] UPDATED)
    We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized. This problem exists ubiquitously in social media, finance and health informatics but is rarely investigated by the conventional research in reinforcement learning. To this end, we present a novel framework of the model-based reinforcement learning where the agent's actions and observations are asynchronous stochastic discrete events occurring in continuous-time. We model the dynamics of the environment by Hawkes process with external intervention control term and develop an algorithm to embed such process in the Bellman equation which guides the direction of the value gradient. We demonstrate the superiority of our method in both synthetic simulator and real-world problem.
    Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification. (arXiv:2212.13939v1 [cs.CL])
    Learning models are highly dependent on data to work effectively, and they give a better performance upon training on big datasets. Massive research exists in the literature to address the dataset adequacy issue. One promising approach for solving dataset adequacy issues is the data augmentation (DA) approach. In DA, the amount of training data instances is increased by making different transformations on the available data instances to generate new correct and representative data instances. DA increases the dataset size and its variability, which enhances the model performance and its prediction accuracy. DA also solves the class imbalance problem in the classification learning techniques. Few studies have recently considered DA in the Arabic language. These studies rely on traditional augmentation approaches, such as paraphrasing by using rules or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets, namely AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 4% in AraSarcasm, 6% in ASTD, 9% in ATT, and 13% in MOVIE.
    Anxolotl, an Anxiety Companion App -- Stress Detection. (arXiv:2212.14006v1 [eess.SP])
    Stress has a great effect on people's lives that can not be understated. While it can be good, since it helps humans to adapt to new and different situations, it can also be harmful when not dealt with properly, leading to chronic stress. The objective of this paper is developing a stress monitoring solution, that can be used in real life, while being able to tackle this challenge in a positive way. The SMILE data set was provided to team Anxolotl, and all it was needed was to develop a robust model. We developed a supervised learning model for classification in Python, presenting the final result of 64.1% in accuracy and a f1-score of 54.96%. The resulting solution stood the robustness test, presenting low variation between runs, which was a major point for it's possible integration in the Anxolotl app in the future.
    AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs. (arXiv:2212.13989v1 [cs.CR])
    Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns. Despite effectiveness, their robustness against adversarial attacks is one of the key trust concerns for MLaaS deployment. We are thus motivated to assess the adversarial robustness of the Machine Learning models residing at the core of these security-critical applications with categorical inputs. Previous research efforts on accessing model robustness against manipulation of categorical inputs are specific to use cases and heavily depend on domain knowledge, or require white-box access to the target ML model. Such limitations prevent the robustness assessment from being as a domain-agnostic service provided to various real-world applications. We propose a provably optimal yet computationally highly efficient adversarial robustness assessment protocol for a wide band of ML-driven cybersecurity-critical applications. We demonstrate the use of the domain-agnostic robustness assessment method with substantial experimental study on fake news detection and intrusion detection problems.
    Representation Learning in Deep RL via Discrete Information Bottleneck. (arXiv:2212.13835v1 [cs.LG])
    Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information.
    A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks. (arXiv:2212.13904v1 [cs.LG])
    Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLSL
    RevealED: Uncovering Pro-Eating Disorder Content on Twitter Using Deep Learning. (arXiv:2212.13949v1 [cs.LG])
    The Covid-19 pandemic induced a vast increase in adolescents diagnosed with eating disorders and hospitalized due to eating disorders. This immense growth stemmed partially from the stress of the pandemic but also from increased exposure to content that promotes eating disorders via social media, which, within the last decade, has become plagued by pro-eating disorder content. This study aimed to create a deep learning model capable of determining whether a given social media post promotes eating disorders based solely on image data. Tweets from hashtags that have been documented to promote eating disorders along with tweets from unrelated hashtags were collected. After prepossessing, these images were labeled as either pro-eating disorder or not based on which Twitter hashtag they were scraped from. Several deep-learning models were trained on the scraped dataset and were evaluated based on their accuracy, F1 score, precision, and recall. Ultimately, the vision transformer model was determined to be the most accurate, attaining an F1 score of 0.877 and an accuracy of 86.7% on the test set. The model, which was applied to unlabeled Twitter image data scraped from "#selfie", uncovered seasonal fluctuations in the relative abundance of pro-eating disorder content, which reached its peak in the summertime. These fluctuations correspond not only to the seasons, but also to stressors, such as the Covid-19 pandemic. Moreover, the Twitter image data indicated that the relative amount of pro-eating disorder content has been steadily rising over the last five years and is likely to continue increasing in the future.
    Multimodal Emotion Recognition among Couples from Lab Settings to Daily Life using Smartwatches. (arXiv:2212.13917v1 [cs.HC])
    Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide an insight into their emotional well-being in chronic disease management. The emotions of partners are currently inferred in the lab and daily life using self-reports which are not practical for continuous emotion assessment or observer reports which are manual, time-intensive, and costly. Currently, there exists no comprehensive overview of works on emotion recognition among couples. Furthermore, approaches for emotion recognition among couples have (1) focused on English-speaking couples in the U.S., (2) used data collected from the lab, and (3) performed recognition using observer ratings rather than partner's self-reported / subjective emotions. In this body of work contained in this thesis (8 papers - 5 published and 3 currently under review in various journals), we fill the current literature gap on couples' emotion recognition, develop emotion recognition systems using 161 hours of data from a total of 1,051 individuals, and make contributions towards taking couples' emotion recognition from the lab which is the status quo, to daily life. This thesis contributes toward building automated emotion recognition systems that would eventually enable partners to monitor their emotions in daily life and enable the delivery of interventions to improve their emotional well-being.
    Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling. (arXiv:2212.13919v1 [eess.SP])
    In this paper, we propose a Siamese sleep transformer (SST) that effectively extracts features from single-channel raw electroencephalogram signals for robust sleep stage scoring. Despite the significant advances in sleep stage scoring in the last few years, most of them mainly focused on the increment of model performance. However, other problems still exist: the bias of labels in datasets and the instability of model performance by repetitive training. To alleviate these problems, we propose the SST, a novel sleep stage scoring model with a selective batch sampling strategy and self-knowledge distillation. To evaluate how robust the model was to the bias of labels, we used different datasets for training and testing: the sleep heart health study and the Sleep-EDF datasets. In this condition, the SST showed competitive performance in sleep stage scoring. In addition, we demonstrated the effectiveness of the selective batch sampling strategy with a reduction of the standard deviation of performance by repetitive training. These results could show that SST extracted effective learning features against the bias of labels in datasets, and the selective batch sampling strategy worked for the model robustness in training.
    HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes. (arXiv:2212.14040v1 [eess.SP])
    The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We show that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. Finally, we also show that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Thus, we present the first vision-based waveform transformer that can be used to develop specialized models for ECG analysis especially at low sample sizes.
    ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction. (arXiv:2210.03745v2 [q-bio.QM] UPDATED)
    In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks. In contrast to other works, our model can be used to address various challenging tasks, including compound property prediction. In ProGReST, the rationale is obtained along with prediction due to the model's built-in interpretability. Additionally, we introduce a new graph prototype projection to accelerate model training. Finally, we evaluate PRoGReST on a wide range of chemical datasets for molecular property prediction and perform in-depth analysis with chemical experts to evaluate obtained interpretations. Our method achieves competitive results against state-of-the-art methods.
    Cross-Domain Consumer Review Analysis. (arXiv:2212.13916v1 [cs.IR])
    The paper presents a cross-domain review analysis on four popular review datasets: Amazon, Yelp, Steam, IMDb. The analysis is performed using Hadoop and Spark, which allows for efficient and scalable processing of large datasets. By examining close to 12 million reviews from these four online forums, we hope to uncover interesting trends in sales and customer sentiment over the years. Our analysis will include a study of the number of reviews and their distribution over time, as well as an examination of the relationship between various review attributes such as upvotes, creation time, rating, and sentiment. By comparing the reviews across different domains, we hope to gain insight into the factors that drive customer satisfaction and engagement in different product categories.
    Using machine learning algorithms to determine the emotional disadaptation of a person by his rhythmogram. (arXiv:2212.13895v1 [eess.SP])
    In this study we applyed machine-learning algorithms to determine the emotional disadaptation of a person by his rhythmogram. We used the method of determining a subject level of emotional disadaptation and recording of cardiorhythmography. We show that electrocardiogram (ECG) signals can be used for the registration of the emotional disadaptation of a person.
    POIBERT: A Transformer-based Model for the Tour Recommendation Problem. (arXiv:2212.13900v1 [cs.IR])
    Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Interest (POIs) but their solutions may not align well with the user's own preferences and other location constraints. Additionally, these solutions do not take into consideration of the users' preference based on their past POIs selection. In this paper, we propose POIBERT, an algorithm for recommending personalized itineraries using the BERT language model on POIs. POIBERT builds upon the highly successful BERT language model with the novel adaptation of a language model to our itinerary recommendation task, alongside an iterative approach to generate consecutive POIs. Our recommendation algorithm is able to generate a sequence of POIs that optimizes time and users' preference in POI categories based on past trajectories from similar tourists. Our tour recommendation algorithm is modeled by adapting the itinerary recommendation problem to the sentence completion problem in natural language processing (NLP). We also innovate an iterative algorithm to generate travel itineraries that satisfies the time constraints which is most likely from past trajectories. Using a Flickr dataset of seven cities, experimental results show that our algorithm out-performs many sequence prediction algorithms based on measures in recall, precision and F1-scores.
    Cross-Dataset Propensity Estimation for Debiasing Recommender Systems. (arXiv:2212.13892v1 [cs.IR])
    Datasets for training recommender systems are often subject to distribution shift induced by users' and recommenders' selection biases. In this paper, we study the impact of selection bias on datasets with different quantization. We then leverage two differently quantized datasets from different source distributions to mitigate distribution shift by applying the inverse probability scoring method from causal inference. Empirically, our approach gains significant performance improvement over single-dataset methods and alternative ways of combining two datasets.
    Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization. (arXiv:2212.13915v1 [cs.IR])
    In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions. Moreover, the bidding on advertising inventory has few connections with propensity one that can reach to target cost-per-acquisition (tCPA) goals. To address this problem, this paper presents a bid optimization scenario to achieve the desired tCPA goals for advertisers. In particular, we build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem, which leverages the bid landscape model learned from rich historical auction data using non-parametric learning. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors, which essentially deals with the data challenges commonly faced by bid landscape modeling: incomplete logs in auctions, and uncertainty due to the variation and fluctuations in advertising bidding behaviors. The bid optimization model outperforms the baseline methods on real-world campaigns, and has been applied into a wide range of scenarios for performance improvement and revenue liftup.
    PersonaSAGE: A Multi-Persona Graph Neural Network. (arXiv:2212.13709v1 [cs.LG])
    Graph Neural Networks (GNNs) have become increasingly important in recent years due to their state-of-the-art performance on many important downstream applications. Existing GNNs have mostly focused on learning a single node representation, despite that a node often exhibits polysemous behavior in different contexts. In this work, we develop a persona-based graph neural network framework called PersonaSAGE that learns multiple persona-based embeddings for each node in the graph. Such disentangled representations are more interpretable and useful than a single embedding. Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings. The framework is flexible enough and the general design helps in the wide applicability of the learned embeddings to suit the domain. We utilize publicly available benchmark datasets to evaluate our approach and against a variety of baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a variety of important tasks including link prediction where we achieve an average gain of 15% while remaining competitive for node classification. Finally, we also demonstrate the utility of PersonaSAGE with a case study for personalized recommendation of different entity types in a data management platform.
    Multi-Realism Image Compression with a Conditional Generator. (arXiv:2212.13824v1 [cs.CV])
    By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models. However, previous methods do not explicitly control how much detail is synthesized, which results in a common criticism of these methods: users might be worried that a misleading reconstruction far from the input image is generated. In this work, we alleviate these concerns by training a decoder that can bridge the two regimes and navigate the distortion-realism trade-off. From a single compressed representation, the receiver can decide to either reconstruct a low mean squared error reconstruction that is close to the input, a realistic reconstruction with high perceptual quality, or anything in between. With our method, we set a new state-of-the-art in distortion-realism, pushing the frontier of achievable distortion-realism pairs, i.e., our method achieves better distortions at high realism and better realism at low distortion than ever before.
    Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks. (arXiv:2212.13848v1 [cs.LG])
    We explore the ability of overparameterized shallow ReLU neural networks to learn Lipschitz, non-differentiable, bounded functions with additive noise when trained by Gradient Descent (GD). To avoid the problem that in the presence of noise, neural networks trained to nearly zero training error are inconsistent in this class, we focus on the early-stopped GD which allows us to show consistency and optimal rates. In particular, we explore this problem from the viewpoint of the Neural Tangent Kernel (NTK) approximation of a GD-trained finite-width neural network. We show that whenever some early stopping rule is guaranteed to give an optimal rate (of excess risk) on the Hilbert space of the kernel induced by the ReLU activation function, the same rule can be used to achieve minimax optimal rate for learning on the class of considered Lipschitz functions by neural networks. We discuss several data-free and data-dependent practically appealing stopping rules that yield optimal rates.
    Using machine learning algorithms to determine the post-COVID state of a person by his rhythmogram. (arXiv:2212.13878v1 [eess.SP])
    In this study we applyed machine-learning algorithms to determine the post-COVID state of a person. During the study, a marker of the post-COVID state of a person was found in the electrocardiogram data. We have shown that this marker in the patient's ECG signal can be used to diagnose a post-COVID state.
    Explainable and Lightweight Model for COVID-19 Detection Using Chest Radiology Images. (arXiv:2212.13788v1 [eess.IV])
    Deep learning (DL) analysis of Chest X-ray (CXR) and Computed tomography (CT) images has garnered a lot of attention in recent times due to the COVID-19 pandemic. Convolutional Neural Networks (CNNs) are well suited for the image analysis tasks when trained on humongous amounts of data. Applications developed for medical image analysis require high sensitivity and precision compared to any other fields. Most of the tools proposed for detection of COVID-19 claims to have high sensitivity and recalls but have failed to generalize and perform when tested on unseen datasets. This encouraged us to develop a CNN model, analyze and understand the performance of it by visualizing the predictions of the model using class activation maps generated using (Gradient-weighted Class Activation Mapping) Grad-CAM technique. This study provides a detailed discussion of the success and failure of the proposed model at an image level. Performance of the model is compared with state-of-the-art DL models and shown to be comparable. The data and code used are available at https://github.com/aleesuss/c19.
    Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation. (arXiv:2212.13887v1 [eess.SP])
    Drowsiness reduces concentration and increases response time, which causes fatal road accidents. Monitoring drivers' drowsiness levels by electroencephalogram (EEG) and taking action may prevent road accidents. EEG signals effectively monitor the driver's mental state as they can monitor brain dynamics. However, calibration is required in advance because EEG signals vary between and within subjects. Because of the inconvenience, calibration has reduced the accessibility of the brain-computer interface (BCI). Developing a generalized classification model is similar to domain generalization, which overcomes the domain shift problem. Especially data augmentation is frequently used. This paper proposes a calibration-free framework for driver drowsiness state classification using manifold-level augmentation. This framework increases the diversity of source domains by utilizing features. We experimented with various augmentation methods to improve the generalization performance. Based on the results of the experiments, we found that deeper models with smaller kernel sizes improved generalizability. In addition, applying an augmentation at the manifold-level resulted in an outstanding improvement. The framework demonstrated the capability for calibration-free BCI.
    Robust identification of non-autonomous dynamical systems using stochastic dynamics models. (arXiv:2212.13902v1 [eess.SY])
    This paper considers the problem of system identification (ID) of linear and nonlinear non-autonomous systems from noisy and sparse data. We propose and analyze an objective function derived from a Bayesian formulation for learning a hidden Markov model with stochastic dynamics. We then analyze this objective function in the context of several state-of-the-art approaches for both linear and nonlinear system ID. In the former, we analyze least squares approaches for Markov parameter estimation, and in the latter, we analyze the multiple shooting approach. We demonstrate the limitations of the optimization problems posed by these existing methods by showing that they can be seen as special cases of the proposed optimization objective under certain simplifying assumptions: conditional independence of data and zero model error. Furthermore, we observe that our proposed approach has improved smoothness and inherent regularization that make it well-suited for system ID and provide mathematical explanations for these characteristics' origins. Finally, numerical simulations demonstrate a mean squared error over 8.7 times lower compared to multiple shooting when data are noisy and/or sparse. Moreover, the proposed approach can identify accurate and generalizable models even when there are more parameters than data or when the underlying system exhibits chaotic behavior.
    Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation. (arXiv:2212.13861v1 [cs.LG])
    Offline reinforcement learning (RL) concerns pursuing an optimal policy for sequential decision-making from a pre-collected dataset, without further interaction with the environment. Recent theoretical progress has focused on developing sample-efficient offline RL algorithms with various relaxed assumptions on data coverage and function approximators, especially to handle the case with excessively large state-action spaces. Among them, the framework based on the linear-programming (LP) reformulation of Markov decision processes has shown promise: it enables sample-efficient offline RL with function approximation, under only partial data coverage and realizability assumptions on the function classes, with favorable computational tractability. In this work, we revisit the LP framework for offline RL, and advance the existing results in several aspects, relaxing certain assumptions and achieving optimal statistical rates in terms of sample size. Our key enabler is to introduce proper constraints in the reformulation, instead of using any regularization as in the literature, sometimes also with careful choices of the function classes and initial state distributions. We hope our insights further advocate the study of the LP framework, as well as the induced primal-dual minimax optimization, in offline RL.
    A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach. (arXiv:2212.10051v1 [cs.CL] CROSS LISTED)
    Opinion mining is the branch of computation that deals with opinions, appraisals, attitudes, and emotions of people and their different aspects. This field has attracted substantial research interest in recent years. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. his paper has presented a framework of aspect-based opinion mining based on the concept of transfer learning. on real-world customer reviews available on the Amazon website. The model has yielded quite satisfactory results in its task of aspect-based opinion mining.
    Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Weighted Negative Samples. (arXiv:2212.13847v1 [cs.LG])
    Heterogeneous graph contrastive learning has received wide attention recently. Some existing methods use meta-paths, which are sequences of object types that capture semantic relationships between objects, to construct contrastive views. However, most of them ignore the rich meta-path context information that describes how two objects are connected by meta-paths. On the other hand, they fail to distinguish hard negatives from false negatives, which could adversely affect the model performance. To address the problems, we propose MEOW, a heterogeneous graph contrastive learning model that considers both meta-path contexts and weighted negative samples. Specifically, MEOW constructs a coarse view and a fine-grained view for contrast. The former reflects which objects are connected by meta-paths, while the latter uses meta-path contexts and characterizes the details on how the objects are connected. We take node embeddings in the coarse view as anchors, and construct positive and negative samples from the fine-grained view. Further, to distinguish hard negatives from false negatives, we learn weights of negative samples based on node clustering. We also use prototypical contrastive learning to pull close embeddings of nodes in the same cluster. Finally, we conduct extensive experiments to show the superiority of MEOW against other state-of-the-art methods.
    Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels. (arXiv:2212.13730v1 [cs.CV])
    The deep learning technique was used to increase the performance of single image super-resolution (SISR). However, most existing CNN-based SISR approaches primarily focus on establishing deeper or larger networks to extract more significant high-level features. Usually, the pixel-level loss between the target high-resolution image and the estimated image is used, but the neighbor relations between pixels in the image are seldom used. On the other hand, according to observations, a pixel's neighbor relationship contains rich information about the spatial structure, local context, and structural knowledge. Based on this fact, in this paper, we utilize pixel's neighbor relationships in a different perspective, and we propose the differences of neighboring pixels to regularize the CNN by constructing a graph from the estimated image and the ground-truth image. The proposed method outperforms the state-of-the-art methods in terms of quantitative and qualitative evaluation of the benchmark datasets. Keywords: Super-resolution, Convolutional Neural Networks, Deep Learning
    Learning to Detect Noisy Labels Using Model-Based Features. (arXiv:2212.13767v1 [cs.LG])
    Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.
    SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts. (arXiv:2212.13780v1 [eess.IV])
    Automated synthesis of histology images has several potential applications in computational pathology. However, no existing method can generate realistic tissue images with a bespoke cellular layout or user-defined histology parameters. In this work, we propose a novel framework called SynCLay (Synthesis from Cellular Layouts) that can construct realistic and high-quality histology images from user-defined cellular layouts along with annotated cellular boundaries. Tissue image generation based on bespoke cellular layouts through the proposed framework allows users to generate different histological patterns from arbitrary topological arrangement of different types of cells. SynCLay generated synthetic images can be helpful in studying the role of different types of cells present in the tumor microenvironmet. Additionally, they can assist in balancing the distribution of cellular counts in tissue images for designing accurate cellular composition predictors by minimizing the effects of data imbalance. We train SynCLay in an adversarial manner and integrate a nuclear segmentation and classification model in its training to refine nuclear structures and generate nuclear masks in conjunction with synthetic images. During inference, we combine the model with another parametric model for generating colon images and associated cellular counts as annotations given the grade of differentiation and cell densities of different cells. We assess the generated images quantitatively and report on feedback from trained pathologists who assigned realism scores to a set of images generated by the framework. The average realism score across all pathologists for synthetic images was as high as that for the real images. We also show that augmenting limited real data with the synthetic data generated by our framework can significantly boost prediction performance of the cellular composition prediction task.
    StyleID: Identity Disentanglement for Anonymizing Faces. (arXiv:2212.13791v1 [cs.CV])
    Privacy of machine learning models is one of the remaining challenges that hinder the broad adoption of Artificial Intelligent (AI). This paper considers this problem in the context of image datasets containing faces. Anonymization of such datasets is becoming increasingly important due to their central role in the training of autonomous cars, for example, and the vast amount of data generated by surveillance systems. While most prior work de-identifies facial images by modifying identity features in pixel space, we instead project the image onto the latent space of a Generative Adversarial Network (GAN) model, find the features that provide the biggest identity disentanglement, and then manipulate these features in latent space, pixel space, or both. The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID, which protects the individuals' identity, while preserving as many characteristics of the original faces in the image dataset as possible. As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement. StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions.
    CCFL: Computationally Customized Federated Learning. (arXiv:2212.13679v1 [cs.LG])
    Federated learning (FL) is a method to train model with distributed data from numerous participants such as IoT devices. It inherently assumes a uniform capacity among participants. However, participants have diverse computational resources in practice due to different conditions such as different energy budgets or executing parallel unrelated tasks. It is necessary to reduce the computation overhead for participants with inefficient computational resources, otherwise they would be unable to finish the full training process. To address the computation heterogeneity, in this paper we propose a strategy for estimating local models without computationally intensive iterations. Based on it, we propose Computationally Customized Federated Learning (CCFL), which allows each participant to determine whether to perform conventional local training or model estimation in each round based on its current computational resources. Both theoretical analysis and exhaustive experiments indicate that CCFL has the same convergence rate as FedAvg without resource constraints. Furthermore, CCFL can be viewed of a computation-efficient extension of FedAvg that retains model performance while considerably reducing computation overhead.
    End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation. (arXiv:2212.13706v1 [cs.LG])
    Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method.
    Lexicographic Multi-Objective Reinforcement Learning. (arXiv:2212.13769v1 [cs.LG])
    In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the second reward signal, and so on. We present a family of both action-value and policy gradient algorithms that can be used to solve such problems, and prove that they converge to policies that are lexicographically optimal. We evaluate the scalability and performance of these algorithms empirically, demonstrating their practical applicability. As a more specific application, we show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.
    Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. (arXiv:2212.13629v1 [cs.LG])
    Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.
    MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images. (arXiv:2212.13715v1 [eess.IV])
    A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.
    Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges. (arXiv:2212.13258v1 [cs.LG])
    As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.
    NeRN -- Learning Neural Representations for Neural Networks. (arXiv:2212.13554v1 [cs.LG])
    Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos. We show that, when adapted correctly, neural representations can be used to directly represent the weights of a pre-trained convolutional neural network, resulting in a Neural Representation for Neural Networks (NeRN). Inspired by coordinate inputs of previous neural representation methods, we assign a coordinate to each convolutional kernel in our network based on its position in the architecture, and optimize a predictor network to map coordinates to their corresponding weights. Similarly to the spatial smoothness of visual scenes, we show that incorporating a smoothness constraint over the original network's weights aids NeRN towards a better reconstruction. In addition, since slight perturbations in pre-trained model weights can result in a considerable accuracy loss, we employ techniques from the field of knowledge distillation to stabilize the learning process. We demonstrate the effectiveness of NeRN in reconstructing widely used architectures on CIFAR-10, CIFAR-100, and ImageNet. Finally, we present two applications using NeRN, demonstrating the capabilities of the learned representations.
    Latent Discretization for Continuous-time Sequence Compression. (arXiv:2212.13659v1 [cs.LG])
    Neural compression offers a domain-agnostic approach to creating codecs for lossy or lossless compression via deep generative models. For sequence compression, however, most deep sequence models have costs that scale with the sequence length rather than the sequence complexity. In this work, we instead treat data sequences as observations from an underlying continuous-time process and learn how to efficiently discretize while retaining information about the full sequence. As a consequence of decoupling sequential information from its temporal discretization, our approach allows for greater compression rates and smaller computational complexity. Moreover, the continuous-time approach naturally allows us to decode at different time intervals. We empirically verify our approach on multiple domains involving compression of video and motion capture sequences, showing that our approaches can automatically achieve reductions in bit rates by learning how to discretize.
    Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos. (arXiv:2212.13495v1 [cs.CV])
    Learning with noisy label (LNL) is a classic problem that has been extensively studied for image tasks, but much less for video in the literature. A straightforward migration from images to videos without considering the properties of videos, such as computational cost and redundant information, is not a sound choice. In this paper, we propose two new strategies for video analysis with noisy labels: 1) A lightweight channel selection method dubbed as Channel Truncation for feature-based label noise detection. This method selects the most discriminative channels to split clean and noisy instances in each category; 2) A novel contrastive strategy dubbed as Noise Contrastive Learning, which constructs the relationship between clean and noisy instances to regularize model training. Experiments on three well-known benchmark datasets for video classification show that our proposed tru{\bf N}cat{\bf E}-split-contr{\bf A}s{\bf T} (NEAT) significantly outperforms the existing baselines. By reducing the dimension to 10\% of it, our method achieves over 0.4 noise detection F1-score and 5\% classification accuracy improvement on Mini-Kinetics dataset under severe noise (symmetric-80\%). Thanks to Noise Contrastive Learning, the average classification accuracy improvement on Mini-Kinetics and Sth-Sth-V1 is over 1.6\%.
    Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks. (arXiv:2212.13621v1 [stat.ML])
    Model calibration, which is concerned with how frequently the model predicts correctly, not only plays a vital part in statistical model design, but also has substantial practical applications, such as optimal decision-making in the real world. However, it has been discovered that modern deep neural networks are generally poorly calibrated due to the overestimation (or underestimation) of predictive confidence, which is closely related to overfitting. In this paper, we propose Annealing Double-Head, a simple-to-implement but highly effective architecture for calibrating the DNN during training. To be precise, we construct an additional calibration head-a shallow neural network that typically has one latent layer-on top of the last latent layer in the normal model to map the logits to the aligned confidence. Furthermore, a simple Annealing technique that dynamically scales the logits by calibration head in training procedure is developed to improve its performance. Under both the in-distribution and distributional shift circumstances, we exhaustively evaluate our Annealing Double-Head architecture on multiple pairs of contemporary DNN architectures and vision and speech datasets. We demonstrate that our method achieves state-of-the-art model calibration performance without post-processing while simultaneously providing comparable predictive accuracy in comparison to other recently proposed calibration methods on a range of learning tasks.
    Optimal algorithms for group distributionally robust optimization and beyond. (arXiv:2212.13669v1 [cs.LG])
    Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods. In this paper, we devise stochastic algorithms for a class of DRO problems including group DRO, subpopulation fairness, and empirical conditional value at risk (CVaR) optimization. Our new algorithms achieve faster convergence rates than existing algorithms for multiple DRO settings. We also provide a new information-theoretic lower bound that implies our bounds are tight for group DRO. Empirically, too, our algorithms outperform known methods
    On the Equivalence of the Weighted Tsetlin Machine and the Perceptron. (arXiv:2212.13634v1 [cs.LG])
    Tsetlin Machine (TM) has been gaining popularity as an inherently interpretable machine leaning method that is able to achieve promising performance with low computational complexity on a variety of applications. The interpretability and the low computational complexity of the TM are inherited from the Boolean expressions for representing various sub-patterns. Although possessing favorable properties, TM has not been the go-to method for AI applications, mainly due to its conceptual and theoretical differences compared with perceptrons and neural networks, which are more widely known and well understood. In this paper, we provide detailed insights for the operational concept of the TM, and try to bridge the gap in the theoretical understanding between the perceptron and the TM. More specifically, we study the operational concept of the TM following the analytical structure of perceptrons, showing the resemblance between the perceptrons and the TM. Through the analysis, we indicated that the TM's weight update can be considered as a special case of the gradient weight update. We also perform an empirical analysis of TM by showing the flexibility in determining the clause length, visualization of decision boundaries and obtaining interpretable boolean expressions from TM. In addition, we also discuss the advantages of TM in terms of its structure and its ability to solve more complex problems.
    AER: Auto-Encoder with Regression for Time Series Anomaly Detection. (arXiv:2212.13558v1 [cs.LG])
    Anomaly detection on time series data is increasingly common across various industrial domains that monitor metrics in order to prevent potential accidents and economic losses. However, a scarcity of labeled data and ambiguous definitions of anomalies can complicate these efforts. Recent unsupervised machine learning methods have made remarkable progress in tackling this problem using either single-timestamp predictions or time series reconstructions. While traditionally considered separately, these methods are not mutually exclusive and can offer complementary perspectives on anomaly detection. This paper first highlights the successes and limitations of prediction-based and reconstruction-based methods with visualized time series signals and anomaly scores. We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method. Our model can produce bi-directional predictions while simultaneously reconstructing the original time series by optimizing a joint objective function. Furthermore, we propose several ways of combining the prediction and reconstruction errors through a series of ablation studies. Finally, we compare the performance of the AER architecture against two prediction-based methods and three reconstruction-based methods on 12 well-known univariate time series datasets from NASA, Yahoo, Numenta, and UCR. The results show that AER has the highest averaged F1 score across all datasets (a 23.5% improvement compared to ARIMA) while retaining a runtime similar to its vanilla auto-encoder and regressor components. Our model is available in Orion, an open-source benchmarking tool for time series anomaly detection.
    Novel Reinforcement Learning Algorithm for Suppressing Synchronization in Closed Loop Deep Brain Stimulators. (arXiv:2212.13260v1 [cs.LG])
    Parkinson's disease is marked by altered and increased firing characteristics of pathological oscillations in the brain. In other words, it causes abnormal synchronous oscillations and suppression during neurological processing. In order to examine and regulate the synchronization and pathological oscillations in motor circuits, deep brain stimulators (DBS) are used. Although machine learning methods have been applied for the investigation of suppression, these models require large amounts of training data and computational power, both of which pose challenges to resource-constrained DBS. This research proposes a novel reinforcement learning (RL) framework for suppressing the synchronization in neuronal activity during episodes of neurological disorders with less power consumption. The proposed RL algorithm comprises an ensemble of a temporal representation of stimuli and a twin-delayed deep deterministic (TD3) policy gradient algorithm. We quantify the stability of the proposed framework to noise and reduced synchrony using RL for three pathological signaling regimes: regular, chaotic, and bursting, and further eliminate the undesirable oscillations. Furthermore, metrics such as evaluation rewards, energy supplied to the ensemble, and the mean point of convergence were used and compared to other RL algorithms, specifically the Advantage actor critic (A2C), the Actor critic with Kronecker-featured trust region (ACKTR), and the Proximal policy optimization (PPO).
    Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification. (arXiv:2212.13589v1 [cs.CV])
    Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to the annotated data during the training process. In this setting, the CGAN not only serves as a co-supervisor but also provides complementary quality examples to aid the classifier training in an end-to-end fashion. Experiments demonstrate that the proposed SEC-CGAN outperforms the external classifier GAN (EC-GAN) and a baseline ResNet-18 classifier. For the comparison, all classifiers in above methods adopt the ResNet-18 architecture as the backbone. Particularly, for the Street View House Numbers dataset, using the 5% of training data, a test accuracy of 90.26% is achieved by SEC-CGAN as opposed to 88.59% by EC-GAN and 87.17% by the baseline classifier; for the highway image dataset, using the 10% of training data, a test accuracy of 98.27% is achieved by SEC-CGAN, compared to 97.84% by EC-GAN and 95.52% by the baseline classifier.
    Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Dealing with the Sparse Data Challenge. (arXiv:2212.13289v1 [astro-ph.IM])
    In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.
    MixupE: Understanding and Improving Mixup from Directional Derivative Perspective. (arXiv:2212.13381v1 [cs.LG])
    Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. We then propose a new method to improve Mixup based on the novel insight. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across various datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.
    Fast and fully-automated histograms for large-scale data sets. (arXiv:2212.13524v1 [cs.LG])
    G-Enum histograms are a new fast and fully automated method for irregular histogram construction. By framing histogram construction as a density estimation problem and its automation as a model selection task, these histograms leverage the Minimum Description Length principle (MDL) to derive two different model selection criteria. Several proven theoretical results about these criteria give insights about their asymptotic behavior and are used to speed up their optimisation. These insights, combined to a greedy search heuristic, are used to construct histograms in linearithmic time rather than the polynomial time incurred by previous works. The capabilities of the proposed MDL density estimation method are illustrated with reference to other fully automated methods in the literature, both on synthetic and large real-world data sets.
    Variance Reduction for Score Functions Using Optimal Baselines. (arXiv:2212.13587v1 [cs.LG])
    Many problems involve the use of models which learn probability distributions or incorporate randomness in some way. In such problems, because computing the true expected gradient may be intractable, a gradient estimator is used to update the model parameters. When the model parameters directly affect a probability distribution, the gradient estimator will involve score function terms. This paper studies baselines, a variance reduction technique for score functions. Motivated primarily by reinforcement learning, we derive for the first time an expression for the optimal state-dependent baseline, the baseline which results in a gradient estimator with minimum variance. Although we show that there exist examples where the optimal baseline may be arbitrarily better than a value function baseline, we find that the value function baseline usually performs similarly to an optimal baseline in terms of variance reduction. Moreover, the value function can also be used for bootstrapping estimators of the return, leading to additional variance reduction. Our results give new insight and justification for why value function baselines and the generalized advantage estimator (GAE) work well in practice.
    Semi-supervised multiscale dual-encoding method for faulty traffic data detection. (arXiv:2212.13596v1 [cs.LG])
    Inspired by the recent success of deep learning in multiscale information encoding, we introduce a variational autoencoder (VAE) based semi-supervised method for detection of faulty traffic data, which is cast as a classification problem. Continuous wavelet transform (CWT) is applied to the time series of traffic volume data to obtain rich features embodied in time-frequency representation, followed by a twin of VAE models to separately encode normal data and faulty data. The resulting multiscale dual encodings are concatenated and fed to an attention-based classifier, consisting of a self-attention module and a multilayer perceptron. For comparison, the proposed architecture is evaluated against five different encoding schemes, including (1) VAE with only normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both normal and faulty data encodings, but without attention module in the classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT) encoding. The first four encoding schemes adopted the same convolutional neural network (CNN) architecture while the fifth encoding scheme follows the transformer architecture of CViT. Our experiments show that the proposed architecture with the dual encoding scheme, coupled with attention module, outperforms other encoding schemes and results in classification accuracy of 96.4%, precision of 95.5%, and recall of 97.7%.
    Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation. (arXiv:2212.13540v1 [stat.ML])
    We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown transition core with features of state and action. Despite much recent progress in analyzing algorithms in the linear MDP setting, the understanding of more general transition models is very restrictive. In this paper, we establish a provably efficient RL algorithm for the MDP whose state transition is given by a multinomial logistic model. To balance the exploration-exploitation trade-off, we propose an upper confidence bound-based algorithm. We show that our proposed algorithm achieves $\tilde{\mathcal{O}}(d \sqrt{H^3 T})$ regret bound where $d$ is the dimension of the transition core, $H$ is the horizon, and $T$ is the total number of steps. To the best of our knowledge, this is the first model-based RL algorithm with multinomial logistic function approximation with provable guarantees. We also comprehensively evaluate our proposed algorithm numerically and show that it consistently outperforms the existing methods, hence achieving both provable efficiency and practical superior performance.
    Efficient Semantic Segmentation on Edge Devices. (arXiv:2212.13691v1 [cs.CV])
    Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.
    Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050. (arXiv:2212.13325v1 [astro-ph.IM])
    Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.
    Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents. (arXiv:2212.13402v1 [cs.LG])
    Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Finally, we present extensive experiments and case studies to illustrate 24.7\% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.
    LOSDD: Leave-Out Support Vector Data Description for Outlier Detection. (arXiv:2212.13626v1 [cs.LG])
    Support Vector Machines have been successfully used for one-class classification (OCSVM, SVDD) when trained on clean data, but they work much worse on dirty data: outliers present in the training data tend to become support vectors, and are hence considered "normal". In this article, we improve the effectiveness to detect outliers in dirty training data with a leave-out strategy: by temporarily omitting one candidate at a time, this point can be judged using the remaining data only. We show that this is more effective at scoring the outlierness of points than using the slack term of existing SVM-based approaches. Identified outliers can then be removed from the data, such that outliers hidden by other outliers can be identified, to reduce the problem of masking. Naively, this approach would require training N individual SVMs (and training $O(N^2)$ SVMs when iteratively removing the worst outliers one at a time), which is prohibitively expensive. We will discuss that only support vectors need to be considered in each step and that by reusing SVM parameters and weights, this incremental retraining can be accelerated substantially. By removing candidates in batches, we can further improve the processing time, although it obviously remains more costly than training a single SVM.
    DeepCuts: Single-Shot Interpretability based Pruning for BERT. (arXiv:2212.13392v1 [cs.CL])
    As language models have grown in parameters and layers, it has become much harder to train and infer with them on single GPUs. This is severely restricting the availability of large language models such as GPT-3, BERT-Large, and many others. A common technique to solve this problem is pruning the network architecture by removing transformer heads, fully-connected weights, and other modules. The main challenge is to discern the important parameters from the less important ones. Our goal is to find strong metrics for identifying such parameters. We thus propose two strategies: Cam-Cut based on the GradCAM interpretations, and Smooth-Cut based on the SmoothGrad, for calculating the importance scores. Through this work, we show that our scoring functions are able to assign more relevant task-based scores to the network parameters, and thus both our pruning approaches significantly outperform the standard weight and gradient-based strategies, especially at higher compression ratios in BERT-based models. We also analyze our pruning masks and find them to be significantly different from the ones obtained using standard metrics.
    Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher. (arXiv:2212.13420v1 [cs.LG])
    We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels and classification, allowing us to store only one model in memory instead of two. Our method attains similar performance to the Meta Pseudo Labels method while drastically reducing memory usage.
    Anomaly detection in laser-guided vehicles' batteries: a case study. (arXiv:2212.13513v1 [cs.LG])
    Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.
    Structure-based drug discovery with deep learning. (arXiv:2212.13295v1 [q-bio.BM])
    Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e.g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a $\textit{renaissance}$ in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.
    Explainable AI for Bioinformatics: Methods, Tools, and Applications. (arXiv:2212.13261v1 [q-bio.QM])
    Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness.
    Power Quality Event Recognition and Classification Using an Online Sequential Extreme Learning Machine Network based on Wavelets. (arXiv:2212.13375v1 [eess.SP])
    Reduced system dependability and higher maintenance costs may be the consequence of poor electric power quality, which can disturb normal equipment performance, speed up aging, and even cause outright failures. This study implements and tests a prototype of an Online Sequential Extreme Learning Machine (OS-ELM) classifier based on wavelets for detecting power quality problems under transient conditions. In order to create the classifier, the OSELM-network model and the discrete wavelet transform (DWT) method are combined. First, discrete wavelet transform (DWT) multi-resolution analysis (MRA) was used to extract characteristics of the distorted signal at various resolutions. The OSELM then sorts the retrieved data by transient duration and energy features to determine the kind of disturbance. The suggested approach requires less memory space and processing time since it can minimize a large quantity of the distorted signal's characteristics without changing the signal's original quality. Several types of transient events were used to demonstrate the classifier's ability to detect and categorize various types of power disturbances, including sags, swells, momentary interruptions, oscillatory transients, harmonics, notches, spikes, flickers, sag swell, sag mi, sag harm, swell trans, sag spike, and swell spike.
    Behavioral Cloning via Search in Video PreTraining Latent Space. (arXiv:2212.13326v1 [cs.LG])
    Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs. We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model. The agent copies the actions from the expert trajectory as long as the distance between the state representations of the agent and the selected expert trajectory from the dataset do not diverge. Then the proximity search is repeated. Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.
    Development and Evaluation of a Learning-based Model for Real-time Haptic Texture Rendering. (arXiv:2212.13332v1 [cs.RO])
    Current Virtual Reality (VR) environments lack the rich haptic signals that humans experience during real-life interactions, such as the sensation of texture during lateral movement on a surface. Adding realistic haptic textures to VR environments requires a model that generalizes to variations of a user's interaction and to the wide variety of existing textures in the world. Current methodologies for haptic texture rendering exist, but they usually develop one model per texture, resulting in low scalability. We present a deep learning-based action-conditional model for haptic texture rendering and evaluate its perceptual performance in rendering realistic texture vibrations through a multi part human user study. This model is unified over all materials and uses data from a vision-based tactile sensor (GelSight) to render the appropriate surface conditioned on the user's action in real time. For rendering texture, we use a high-bandwidth vibrotactile transducer attached to a 3D Systems Touch device. The result of our user study shows that our learning-based method creates high-frequency texture renderings with comparable or better quality than state-of-the-art methods without the need for learning a separate model per texture. Furthermore, we show that the method is capable of rendering previously unseen textures using a single GelSight image of their surface.
    Modeling Time-Series and Spatial Data for Recommendations and Other Applications. (arXiv:2212.13259v1 [cs.IR])
    With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. Later, we handle the task of designing accurate recommender systems. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i.e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in. Lastly, we highlight that the capabilities of the proposed models can have applications beyond recommender systems, and we extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction. A significant part of this thesis uses the idea of modeling the underlying distribution of CTES via neural marked temporal point processes (MTPP). Traditional MTPP models are stochastic processes that utilize a fixed formulation to capture the generative mechanism of a sequence of discrete events localized in continuous time. In contrast, neural MTPP combine the underlying ideas from the point process literature with modern deep learning architectures. The ability of deep-learning models as accurate function approximators has led to a significant gain in the predictive prowess of neural MTPP models. In this thesis, we utilize and present several neural network-based enhancements for the current MTPP frameworks for the aforementioned real-world applications.
    Strangeness-driven Exploration in Multi-Agent Reinforcement Learning. (arXiv:2212.13448v1 [cs.LG])
    Efficient exploration strategy is one of essential issues in cooperative multi-agent reinforcement learning (MARL) algorithms requiring complex coordination. In this study, we introduce a new exploration method with the strangeness that can be easily incorporated into any centralized training and decentralized execution (CTDE)-based MARL algorithms. The strangeness refers to the degree of unfamiliarity of the observations that an agent visits. In order to give the observation strangeness a global perspective, it is also augmented with the the degree of unfamiliarity of the visited entire state. The exploration bonus is obtained from the strangeness and the proposed exploration method is not much affected by stochastic transitions commonly observed in MARL tasks. To prevent a high exploration bonus from making the MARL training insensitive to extrinsic rewards, we also propose a separate action-value function trained by both extrinsic reward and exploration bonus, on which a behavioral policy to generate transitions is designed based. It makes the CTDE-based MARL algorithms more stable when they are used with an exploration method. Through a comparative evaluation in didactic examples and the StarCraft Multi-Agent Challenge, we show that the proposed exploration method achieves significant performance improvement in the CTDE-based MARL algorithms.
    LAMBADA: Backward Chaining for Automated Reasoning in Natural Language. (arXiv:2212.13894v1 [cs.AI])
    Remarkable progress has been made on automated reasoning with knowledge specified as unstructured, natural text, by using the power of large language models (LMs) coupled with methods such as Chain-of-Thought prompting and Selection-Inference. These techniques search for proofs in the forward direction from axioms to the conclusion, which suffers from a combinatorial explosion of the search space, and thus high failure rates for problems requiring longer chains of reasoning. The classical automated reasoning literature has shown that reasoning in the backward direction (i.e. from the intended conclusion to the set of axioms that support it) is significantly more efficient at proof-finding problems. We import this intuition into the LM setting and develop a Backward Chaining algorithm, which we call LAMBADA, that decomposes reasoning into four sub-modules, each of which can be simply implemented by few-shot prompted LM inference. We show that LAMBADA achieves massive accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets, particularly when deep and accurate proof chains are required.
    Driving in Dense Traffic with Model-Free Reinforcement Learning. (arXiv:1909.06710v3 [cs.RO] UPDATED)
    Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle-free volume in spacetime is very small in these scenarios for the vehicle to drive through. However, that does not mean the task is infeasible since human drivers are known to be able to drive amongst dense traffic by leveraging the cooperativeness of other drivers to open a gap. The traditional methods fail to take into account the fact that the actions taken by an agent affect the behaviour of other vehicles on the road. In this work, we rely on the ability of deep reinforcement learning to implicitly model such interactions and learn a continuous control policy over the action space of an autonomous vehicle. The application we consider requires our agent to negotiate and open a gap in the road in order to successfully merge or change lanes. Our policy learns to repeatedly probe into the target road lane while trying to find a safe spot to move in to. We compare against two model-predictive control-based algorithms and show that our policy outperforms them in simulation.
    DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability. (arXiv:2111.10447v2 [cs.LG] UPDATED)
    Transformers have achieved great success in several domains, including Natural Language Processing and Computer Vision. However, its application to real-world graphs is less explored, mainly due to its high computation cost and its poor generalizability caused by the lack of enough training data in the graph domain. To fill in this gap, we propose a scalable Transformer-like dynamic graph learning method named Dynamic Graph Transformer (DyFormer) with spatial-temporal encoding to effectively learn graph topology and capture implicit links. To achieve efficient and scalable training, we propose temporal-union graph structure and its associated subgraph-based node sampling strategy. To improve the generalization ability, we introduce two complementary self-supervised pre-training tasks and show that jointly optimizing the two pre-training tasks results in a smaller Bayesian error rate via an information-theoretic analysis. Extensive experiments on the real-world datasets illustrate that DyFormer achieves a consistent 1%-3% AUC gain (averaged over all time steps) compared with baselines on all benchmarks.
    Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. (arXiv:2212.13556v1 [cs.LG])
    To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization. In this work, we consider the prospect of establishing such rates via several existing information-theoretic frameworks: input-output mutual information bounds, conditional mutual information bounds and variants, PAC-Bayes bounds, and recent conditional variants thereof. We prove that none of these bounds are able to establish minimax rates. We then consider a common tactic employed in studying gradient methods, whereby the final iterate is corrupted by Gaussian noise, producing a noisy "surrogate" algorithm. We prove that minimax rates cannot be established via the analysis of such surrogates. Our results suggest that new ideas are required to analyze gradient descent using information-theoretic techniques.
    Robustifying Markowitz. (arXiv:2212.13996v1 [econ.EM])
    Markowitz mean-variance portfolios with sample mean and covariance as input parameters feature numerous issues in practice. They perform poorly out of sample due to estimation error, they experience extreme weights together with high sensitivity to change in input parameters. The heavy-tail characteristics of financial time series are in fact the cause for these erratic fluctuations of weights that consequently create substantial transaction costs. In robustifying the weights we present a toolbox for stabilizing costs and weights for global minimum Markowitz portfolios. Utilizing a projected gradient descent (PGD) technique, we avoid the estimation and inversion of the covariance operator as a whole and concentrate on robust estimation of the gradient descent increment. Using modern tools of robust statistics we construct a computationally efficient estimator with almost Gaussian properties based on median-of-means uniformly over weights. This robustified Markowitz approach is confirmed by empirical studies on equity markets. We demonstrate that robustified portfolios reach the lowest turnover compared to shrinkage-based and constrained portfolios while preserving or slightly improving out-of-sample performance.
    Prompt Consistency for Zero-Shot Task Generalization. (arXiv:2205.00049v2 [cs.CL] UPDATED)
    One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting. To achieve this, NLP tasks are framed as natural language prompts, generating a response indicating the predicted output. Nonetheless, the performance in such settings often lags far behind its supervised counterpart, suggesting a large space for potential improvement. In this paper, we explore methods to utilize unlabeled data to improve zero-shot performance. Specifically, we take advantage of the fact that multiple prompts can be used to specify a single task, and propose to regularize prompt consistency, encouraging consistent predictions over this diverse set of prompts. Our method makes it possible to fine-tune the model either with extra unlabeled training data, or directly on test input at inference time in an unsupervised manner. In experiments, our approach outperforms the state-of-the-art zero-shot learner, T0 (Sanh et al., 2022), on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points in terms of accuracy. The gains are often attained with a small number of unlabeled examples.
    Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes. (arXiv:2212.13923v1 [cs.IR])
    In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint. This paper presents a bid recommendation scenario that discovers the concavity changes in click prediction curves. The recommended bid is derived based on the turning point from significant increase (i.e. concave downward) to slow increase (convex upward). Parametric learning based method is applied by solving the corresponding constraint optimization problem. Empirical studies on real-world advertising scenarios clearly demonstrate the performance gains for business metrics (including revenue increase, click increase and advertiser ROI increase).
    How important are activation functions in regression and classification? A survey, performance comparison, and future directions. (arXiv:2209.02681v6 [cs.LG] UPDATED)
    Inspired by biological neurons, the activation functions play an essential part in the learning process of any artificial neural network commonly used in many real-world problems. Various activation functions have been proposed in the literature for classification as well as regression tasks. In this work, we survey the activation functions that have been employed in the past as well as the current state-of-the-art. In particular, we present various developments in activation functions over the years and the advantages as well as disadvantages or limitations of these activation functions. We also discuss classical (fixed) activation functions, including rectifier units, and adaptive activation functions. In addition to discussing the taxonomy of activation functions based on characterization, a taxonomy of activation functions based on applications is presented. To this end, the systematic comparison of various fixed and adaptive activation functions is performed for classification data sets such as the MNIST, CIFAR-10, and CIFAR- 100. In recent years, a physics-informed machine learning framework has emerged for solving problems related to scientific computations. For this purpose, we also discuss various requirements for activation functions that have been used in the physics-informed machine learning framework. Furthermore, various comparisons are made among different fixed and adaptive activation functions using various machine learning libraries such as TensorFlow, Pytorch, and JAX.
    HeATed Alert Triage (HeAT): Transferrable Learning to Extract Multistage Attack Campaigns. (arXiv:2212.13941v1 [cs.CR])
    With growing sophistication and volume of cyber attacks combined with complex network structures, it is becoming extremely difficult for security analysts to corroborate evidences to identify multistage campaigns on their network. This work develops HeAT (Heated Alert Triage): given a critical indicator of compromise (IoC), e.g., a severe IDS alert, HeAT produces a HeATed Attack Campaign (HAC) depicting the multistage activities that led up to the critical event. We define the concept of "Alert Episode Heat" to represent the analysts opinion of how much an event contributes to the attack campaign of the critical IoC given their knowledge of the network and security expertise. Leveraging a network-agnostic feature set, HeAT learns the essence of analyst's assessment of "HeAT" for a small set of IoC's, and applies the learned model to extract insightful attack campaigns for IoC's not seen before, even across networks by transferring what have been learned. We demonstrate the capabilities of HeAT with data collected in Collegiate Penetration Testing Competition (CPTC) and through collaboration with a real-world SOC. We developed HeAT-Gain metrics to demonstrate how analysts may assess and benefit from the extracted attack campaigns in comparison to common practices where IP addresses are used to corroborate evidences. Our results demonstrates the practical uses of HeAT by finding campaigns that span across diverse attack stages, remove a significant volume of irrelevant alerts, and achieve coherency to the analyst's original assessments.
    Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference. (arXiv:2206.04685v2 [cs.LG] UPDATED)
    By adding exiting layers to the deep learning networks, early exit can terminate the inference earlier with accurate results. The passive decision-making of whether to exit or continue the next layer has to go through every pre-placed exiting layer until it exits. In addition, it is also hard to adjust the configurations of the computing platforms alongside the inference proceeds. By incorporating a low-cost prediction engine, we propose a Predictive Exit framework for computation- and energy-efficient deep learning applications. Predictive Exit can forecast where the network will exit (i.e., establish the number of remaining layers to finish the inference), which effectively reduces the network computation cost by exiting on time without running every pre-placed exiting layer. Moreover, according to the number of remaining layers, proper computing configurations (i.e., frequency and voltage) are selected to execute the network to further save energy. Extensive experimental results demonstrate that Predictive Exit achieves up to 96.2% computation reduction and 72.9% energy-saving compared with classic deep learning networks; and 12.8% computation reduction and 37.6% energy-saving compared with the early exit under state-of-the-art exiting strategies, given the same inference accuracy and latency.
    Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks. (arXiv:2212.13359v1 [cs.SE])
    Configurable software systems are employed in many important application domains. Understanding the performance of the systems under all configurations is critical to prevent potential performance issues caused by misconfiguration. However, as the number of configurations can be prohibitively large, it is not possible to measure the system performance under all configurations. Thus, a common approach is to build a prediction model from a limited measurement data to predict the performance of all configurations as scalar values. However, it has been pointed out that there are different sources of uncertainty coming from the data collection or the modeling process, which can make the scalar predictions not certainly accurate. To address this problem, we propose a Bayesian deep learning based method, namely BDLPerf, that can incorporate uncertainty into the prediction model. BDLPerf can provide both scalar predictions for configurations' performance and the corresponding confidence intervals of these scalar predictions. We also develop a novel uncertainty calibration technique to ensure the reliability of the confidence intervals generated by a Bayesian prediction model. Finally, we suggest an efficient hyperparameter tuning technique so as to train the prediction model within a reasonable amount of time whilst achieving high accuracy. Our experimental results on 10 real-world systems show that BDLPerf achieves higher accuracy than existing approaches, in both scalar performance prediction and confidence interval estimation.
    Social-Aware Clustered Federated Learning with Customized Privacy Preservation. (arXiv:2212.13992v1 [cs.CR])
    A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. We unfold the design of SCFL in three steps. \emph{i) Stable social cluster formation. Considering users' heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping}. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants' model updates depending on social trust degrees. iii) Distributed convergence}. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.
    Distribution Estimation of Contaminated Data via DNN-based MoM-GANs. (arXiv:2212.13741v1 [stat.ML])
    This paper studies the distribution estimation of contaminated data by the MoM-GAN method, which combines generative adversarial net (GAN) and median-of-mean (MoM) estimation. We use a deep neural network (DNN) with a ReLU activation function to model the generator and discriminator of the GAN. Theoretically, we derive a non-asymptotic error bound for the DNN-based MoM-GAN estimator measured by integral probability metrics with the $b$-smoothness H\"{o}lder class. The error bound decreases essentially as $n^{-b/p}\vee n^{-1/2}$, where $n$ and $p$ are the sample size and the dimension of input data. We give an algorithm for the MoM-GAN method and implement it through two real applications. The numerical results show that the MoM-GAN outperforms other competitive methods when dealing with contaminated data.
    Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction. (arXiv:2212.13879v1 [cs.IR])
    User behavior data produced during interaction with massive items in the significant data era are generally heterogeneous and sparse, leaving the recommender system (RS) a large diversity of underlying patterns to excavate. Deep neural network-based models have reached the state-of-the-art benchmark of the RS owing to their fitting capabilities. However, prior works mainly focus on designing an intricate architecture with fixed loss function and regulation. These single-metric models provide limited performance when facing heterogeneous and sparse user behavior data. Motivated by this finding, we propose a multi-metric AutoRec (MMA) based on the representative AutoRec. The idea of the proposed MMA is mainly two-fold: 1) apply different $L_p$-norm on loss function and regularization to form different variant models in different metric spaces, and 2) aggregate these variant models. Thus, the proposed MMA enjoys the multi-metric orientation from a set of dispersed metric spaces, achieving a comprehensive representation of user data. Theoretical studies proved that the proposed MMA could attain performance improvement. The extensive experiment on five real-world datasets proves that MMA can outperform seven other state-of-the-art models in predicting unobserved user behavior data.
    Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions. (arXiv:2212.13591v1 [cs.AI])
    The success of deep learning is largely due to the availability of large amounts of training data that cover a wide range of examples of a particular concept or meaning. In the field of medicine, having a diverse set of training data on a particular disease can lead to the development of a model that is able to accurately predict the disease. However, despite the potential benefits, there have not been significant advances in image-based diagnosis due to a lack of high-quality annotated data. This article highlights the importance of using a data-centric approach to improve the quality of data representations, particularly in cases where the available data is limited. To address this "small-data" issue, we discuss four methods for generating and aggregating training data: data augmentation, transfer learning, federated learning, and GANs (generative adversarial networks). We also propose the use of knowledge-guided GANs to incorporate domain knowledge in the training data generation process. With the recent progress in large pre-trained language models, we believe it is possible to acquire high-quality knowledge that can be used to improve the effectiveness of knowledge-guided generative methods.
    EDoG: Adversarial Edge Detection For Graph Neural Networks. (arXiv:2212.13607v1 [cs.LG])
    Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node or subgraph classification prediction by adding subtle perturbations. Detecting these attacks is challenging due to the small magnitude of perturbation and the discrete nature of graph data. In this paper, we propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation. Specifically, we propose a novel graph generation approach combined with link prediction to detect suspicious adversarial edges. To effectively train the graph generative model, we sample several sub-graphs from the given graph data. We show that since the number of adversarial edges is usually low in practice, with low probability the sampled sub-graphs will contain adversarial edges based on the union bound. In addition, considering the strong attacks which perturb a large number of edges, we propose a set of novel features to perform outlier detection as the preprocessing for our detection. Extensive experimental results on three real-world graph datasets including a private transaction rule dataset from a major company and two types of synthetic graphs with controlled properties show that EDoG can achieve above 0.8 AUC against four state-of-the-art unseen attack strategies without requiring any knowledge about the attack type; and around 0.85 with knowledge of the attack type. EDoG significantly outperforms traditional malicious edge detection baselines. We also show that an adaptive attack with full knowledge of our detection pipeline is difficult to bypass it.
    The Forward-Forward Algorithm: Some Preliminary Investigations. (arXiv:2212.13345v1 [cs.LG])
    The aim of this paper is to introduce a new learning procedure for neural networks and to demonstrate that it works well enough on a few small problems to be worth further investigation. The Forward-Forward algorithm replaces the forward and backward passes of backpropagation by two forward passes, one with positive (i.e. real) data and the other with negative data which could be generated by the network itself. Each layer has its own objective function which is simply to have high goodness for positive data and low goodness for negative data. The sum of the squared activities in a layer can be used as the goodness but there are many other possibilities, including minus the sum of the squared activities. If the positive and negative passes could be separated in time, the negative passes could be done offline, which would make the learning much simpler in the positive pass and allow video to be pipelined through the network without ever storing activities or stopping to propagate derivatives.
    Singing Voice Synthesis Based on a Musical Note Position-Aware Attention Mechanism. (arXiv:2212.13703v1 [eess.AS])
    This paper proposes a novel sequence-to-sequence (seq2seq) model with a musical note position-aware attention mechanism for singing voice synthesis (SVS). A seq2seq modeling approach that can simultaneously perform acoustic and temporal modeling is attractive. However, due to the difficulty of the temporal modeling of singing voices, many recent SVS systems with an encoder-decoder-based model still rely on explicitly on duration information generated by additional modules. Although some studies perform simultaneous modeling using seq2seq models with an attention mechanism, they have insufficient robustness against temporal modeling. The proposed attention mechanism is designed to estimate the attention weights by considering the rhythm given by the musical score. Furthermore, several techniques are also introduced to improve the modeling performance of the singing voice. Experimental results indicated that the proposed model is effective in terms of both naturalness and robustness of timing.
    Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals. (arXiv:2212.13885v1 [eess.SP])
    In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals. We demonstrate that a Transformer-based approach is suitable for this task. In addition, we present how such models may be pretrained in a multimodal scenario to improve emotion recognition performances. We evaluate the benefits of using multimodal inputs and pre-training with our approach on a state-ofthe-art dataset.
    Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation. (arXiv:2212.13731v1 [eess.IV])
    The task of image segmentation is to classify each pixel in the image based on the appropriate label. Various deep learning approaches have been proposed for image segmentation that offers high accuracy and deep architecture. However, the deep learning technique uses a pixel-wise loss function for the training process. Using pixel-wise loss neglected the pixel neighbor relationships in the network learning process. The neighboring relationship of the pixels is essential information in the image. Utilizing neighboring pixel information provides an advantage over using only pixel-to-pixel information. This study presents regularizers to give the pixel neighbor relationship information to the learning process. The regularizers are constructed by the graph theory approach and topology approach: By graph theory approach, graph Laplacian is used to utilize the smoothness of segmented images based on output images and ground-truth images. By topology approach, Euler characteristic is used to identify and minimize the number of isolated objects on segmented images. Experiments show that our scheme successfully captures pixel neighbor relations and improves the performance of the convolutional neural network better than the baseline without a regularization term.
    Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach. (arXiv:2212.13472v1 [eess.SY])
    Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit's output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater.
    Characteristics-Informed Neural Networks for Forward and Inverse Hyperbolic Problems. (arXiv:2212.14012v1 [cs.LG])
    We propose characteristic-informed neural networks (CINN), a simple and efficient machine learning approach for solving forward and inverse problems involving hyperbolic PDEs. Like physics-informed neural networks (PINN), CINN is a meshless machine learning solver with universal approximation capabilities. Unlike PINN, which enforces a PDE softly via a multi-part loss function, CINN encodes the characteristics of the PDE in a general-purpose deep neural network trained with the usual MSE data-fitting regression loss and standard deep learning optimization methods. This leads to faster training and can avoid well-known pathologies of gradient descent optimization of multi-part PINN loss functions. If the characteristic ODEs can be solved exactly, which is true in important cases, the output of a CINN is an exact solution of the PDE, even at initialization, preventing the occurrence of non-physical outputs. Otherwise, the ODEs must be solved approximately, but the CINN is still trained only using a data-fitting loss function. The performance of CINN is assessed empirically in forward and inverse linear hyperbolic problems. These preliminary results indicate that CINN is able to improve on the accuracy of the baseline PINN, while being nearly twice as fast to train and avoiding non-physical solutions. Future extensions to hyperbolic PDE systems and nonlinear PDEs are also briefly discussed.
    Automatic Text Simplification of News Articles in the Context of Public Broadcasting. (arXiv:2212.13317v1 [cs.CL])
    This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).
    Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory. (arXiv:2212.13328v1 [astro-ph.IM])
    Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.
    Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification. (arXiv:2212.13898v1 [cs.IR])
    With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision.
    Data-driven control of COVID-19 in buildings: a reinforcement-learning approach. (arXiv:2212.13559v1 [eess.SY])
    In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, this research article proposes data-driven control strategies to design optimal indoor airflow to minimize the exposure of occupants to viral pathogens in built environments. A general control framework is put forward for designing an optimal velocity field and proximal policy optimization, a reinforcement learning algorithm is employed to solve the control problem in a data-driven fashion. The same framework is used for optimal placement of disinfectants to neutralize the viral pathogens as an alternative to the airflow design when the latter is practically infeasible or hard to implement. We show, via simulation experiments, that the control agent learns the optimal policy in both scenarios within a reasonable time. The proposed data-driven control framework in this study will have significant societal and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable ventilation devices and disinfectants.
  • Open

    Latent Discretization for Continuous-time Sequence Compression. (arXiv:2212.13659v1 [cs.LG])
    Neural compression offers a domain-agnostic approach to creating codecs for lossy or lossless compression via deep generative models. For sequence compression, however, most deep sequence models have costs that scale with the sequence length rather than the sequence complexity. In this work, we instead treat data sequences as observations from an underlying continuous-time process and learn how to efficiently discretize while retaining information about the full sequence. As a consequence of decoupling sequential information from its temporal discretization, our approach allows for greater compression rates and smaller computational complexity. Moreover, the continuous-time approach naturally allows us to decode at different time intervals. We empirically verify our approach on multiple domains involving compression of video and motion capture sequences, showing that our approaches can automatically achieve reductions in bit rates by learning how to discretize.
    Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation. (arXiv:2212.13861v1 [cs.LG])
    Offline reinforcement learning (RL) concerns pursuing an optimal policy for sequential decision-making from a pre-collected dataset, without further interaction with the environment. Recent theoretical progress has focused on developing sample-efficient offline RL algorithms with various relaxed assumptions on data coverage and function approximators, especially to handle the case with excessively large state-action spaces. Among them, the framework based on the linear-programming (LP) reformulation of Markov decision processes has shown promise: it enables sample-efficient offline RL with function approximation, under only partial data coverage and realizability assumptions on the function classes, with favorable computational tractability. In this work, we revisit the LP framework for offline RL, and advance the existing results in several aspects, relaxing certain assumptions and achieving optimal statistical rates in terms of sample size. Our key enabler is to introduce proper constraints in the reformulation, instead of using any regularization as in the literature, sometimes also with careful choices of the function classes and initial state distributions. We hope our insights further advocate the study of the LP framework, as well as the induced primal-dual minimax optimization, in offline RL.
    All's well that FID's well? Result quality and metric scores in GAN models for lip-sychronization tasks. (arXiv:2212.13810v1 [cs.CV])
    We test the performance of GAN models for lip-synchronization. For this, we reimplement LipGAN in Pytorch, train it on the dataset GRID and compare it to our own variation, L1WGAN-GP, adapted to the LipGAN architecture and also trained on GRID.
    On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations. (arXiv:2212.13936v1 [cs.LG])
    KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.
    Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks. (arXiv:2106.07106v2 [cs.LG] UPDATED)
    We introduce and analyze NetOTC, a procedure for the comparison and soft alignment of weighted networks. Given two networks and a cost function relating their vertices, NetOTC finds an appropriate coupling of their associated random walks having minimum expected cost. The minimizing cost provides a numerical measure of the difference between the networks, while the optimal transport plan itself provides interpretable, probabilistic alignments of the vertices and edges of the two networks. The cost function employed can be based, for example, on vertex degrees, externally defined features, or Euclidean embeddings. Coupling of the full random walks, rather than their stationary distributions, ensures that NetOTC captures local and global information about the given networks. NetOTC applies to networks of different size and structure, and does not the require specification of free parameters. NetOTC respects edges, in the sense that vertex pairs in the given networks are aligned with positive probability only if they are adjacent in the given networks. We investigate a number of theoretical properties of NetOTC that support its use, including metric properties of the minimizing cost and its connection with short- and long-run average cost. In addition, we introduce a new notion of factor for weighted networks, and establish a close connection between factors and NetOTC. Complementing the theory, we present simulations and numerical experiments showing that NetOTC is competitive with, and sometimes superior to, other optimal transport-based network comparison methods in the literature. In particular, NetOTC shows promise in identifying isomorphic networks using a local (degree-based) cost function.
    Spectral Representation Learning for Conditional Moment Models. (arXiv:2210.16525v2 [stat.ML] UPDATED)
    Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of conditional moment restrictions. For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on various measures of ill-posedness of the hypothesis space, which are hard to validate when flexible models are used. In this work, we address this issue by proposing a procedure that automatically learns representations with controlled measures of ill-posedness. Our method approximates a linear representation defined by the spectral decomposition of a conditional expectation operator, which can be used for kernelized estimators and is known to facilitate minimax optimal estimation in certain settings. We show this representation can be efficiently estimated from data, and establish L2 consistency for the resulting estimator. We evaluate the proposed method on proximal causal inference tasks, exhibiting promising performance on high-dimensional, semi-synthetic data.
    Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks. (arXiv:2212.13621v1 [stat.ML])
    Model calibration, which is concerned with how frequently the model predicts correctly, not only plays a vital part in statistical model design, but also has substantial practical applications, such as optimal decision-making in the real world. However, it has been discovered that modern deep neural networks are generally poorly calibrated due to the overestimation (or underestimation) of predictive confidence, which is closely related to overfitting. In this paper, we propose Annealing Double-Head, a simple-to-implement but highly effective architecture for calibrating the DNN during training. To be precise, we construct an additional calibration head-a shallow neural network that typically has one latent layer-on top of the last latent layer in the normal model to map the logits to the aligned confidence. Furthermore, a simple Annealing technique that dynamically scales the logits by calibration head in training procedure is developed to improve its performance. Under both the in-distribution and distributional shift circumstances, we exhaustively evaluate our Annealing Double-Head architecture on multiple pairs of contemporary DNN architectures and vision and speech datasets. We demonstrate that our method achieves state-of-the-art model calibration performance without post-processing while simultaneously providing comparable predictive accuracy in comparison to other recently proposed calibration methods on a range of learning tasks.
    Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis. (arXiv:1911.12426v6 [cs.LG] UPDATED)
    We develop methods for reducing the dimensionality of large data sets, common in biomedical applications. Learning about patients using genetic data often includes more features than observations, which makes direct supervised learning difficult. One method of reducing the feature space is to use latent Dirichlet allocation to group genetic variants in an unsupervised manner. Latent Dirichlet allocation describes a patient as a mixture of topics corresponding to genetic variants. This can be generalized as a Bayesian tensor decomposition to account for multiple feature variables. Our most significant contributions are with hierarchical topic modeling. We design distinct methods of incorporating hierarchical topic modeling, based on nested Chinese restaurant processes and Pachinko Allocation Machine, into Bayesian tensor decomposition. We apply these models to examine patients with one of four common types of cancer (breast, lung, prostate, and colorectal) and siblings with and without autism spectrum disorder. We linked the genes with their biological pathways and combine this information into a tensor of patients, counts of their genetic variants, and the genes' membership in pathways. We find that our trained models outperform baseline models, with respect to coherence, by up to 40%.
    Optimal Convex and Nonconvex Regularizers for a Data Source. (arXiv:2212.13597v1 [math.OC])
    In optimization-based approaches to inverse problems and to statistical estimation, it is common to augment the objective with a regularizer to address challenges associated with ill-posedness. The choice of a suitable regularizer is typically driven by prior domain information and computational considerations. Convex regularizers are attractive as they are endowed with certificates of optimality as well as the toolkit of convex analysis, but exhibit a computational scaling that makes them ill-suited beyond moderate-sized problem instances. On the other hand, nonconvex regularizers can often be deployed at scale, but do not enjoy the certification properties associated with convex regularizers. In this paper, we seek a systematic understanding of the power and the limitations of convex regularization by investigating the following questions: Given a distribution, what are the optimal regularizers, both convex and nonconvex, for data drawn from the distribution? What properties of a data source govern whether it is amenable to convex regularization? We address these questions for the class of continuous and positively homogenous regularizers for which convex and nonconvex regularizers correspond, respectively, to convex bodies and star bodies. By leveraging dual Brunn-Minkowski theory, we show that a radial function derived from a data distribution is the key quantity for identifying optimal regularizers and for assessing the amenability of a data source to convex regularization. Using tools such as $\Gamma$-convergence, we show that our results are robust in the sense that the optimal regularizers for a sample drawn from a distribution converge to their population counterparts as the sample size grows large. Finally, we give generalization guarantees that recover previous results for polyhedral regularizers (i.e., dictionary learning) and lead to new ones for semidefinite regularizers.
    Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels. (arXiv:2102.02976v4 [stat.ML] UPDATED)
    Machine learning models trained by different optimization algorithms under different data distributions can exhibit distinct generalization behaviors. In this paper, we analyze the generalization of models trained by noisy iterative algorithms. We derive distribution-dependent generalization bounds by connecting noisy iterative algorithms to additive noise channels found in communication and information theory. Our generalization bounds shed light on several applications, including differentially private stochastic gradient descent (DP-SGD), federated learning, and stochastic gradient Langevin dynamics (SGLD). We demonstrate our bounds through numerical experiments, showing that they can help understand recent empirical observations of the generalization phenomena of neural networks.
    Beyond the Golden Ratio for Variational Inequality Algorithms. (arXiv:2212.13955v1 [math.OC])
    We improve the understanding of the $\textit{golden ratio algorithm}$, which solves monotone variational inequalities (VI) and convex-concave min-max problems via the distinctive feature of adapting the step sizes to the local Lipschitz constants. Adaptive step sizes not only eliminate the need to pick hyperparameters, but they also remove the necessity of global Lipschitz continuity and can increase from one iteration to the next. We first establish the equivalence of this algorithm with popular VI methods such as reflected gradient, Popov or optimistic gradient descent-ascent in the unconstrained case with constant step sizes. We then move on to the constrained setting and introduce a new analysis that allows to use larger step sizes, to complete the bridge between the golden ratio algorithm and the existing algorithms in the literature. Doing so, we actually eliminate the link between the golden ratio $\frac{1+\sqrt{5}}{2}$ and the algorithm. Moreover, we improve the adaptive version of the algorithm, first by removing the maximum step size hyperparameter (an artifact from the analysis) to improve the complexity bound, and second by adjusting it to nonmonotone problems with weak Minty solutions, with superior empirical performance.
    Confusion Matrices and Accuracy Statistics for Binary Classifiers Using Unlabeled Data: The Diagnostic Test Approach. (arXiv:2208.12664v2 [stat.ML] UPDATED)
    Medical researchers have solved the problem of estimating the sensitivity and specificity of binary medical diagnostic tests without gold standard tests for comparison. That problem is the same as estimating confusion matrices for classifiers on unlabeled data. This article describes how to modify the diagnostic test solutions to estimate confusion matrices and accuracy statistics for supervised or unsupervised binary classifiers on unlabeled data.  ( 2 min )
    Outcome-Driven Reinforcement Learning via Variational Inference. (arXiv:2104.10190v2 [cs.LG] UPDATED)
    While reinforcement learning algorithms provide automated acquisition of optimal policies, practical application of such methods requires a number of design decisions, such as manually designing reward functions that not only define the task, but also provide sufficient shaping to accomplish it. In this paper, we view reinforcement learning as inferring policies that achieve desired outcomes, rather than as a problem of maximizing rewards. To solve this inference problem, we establish a novel variational inference formulation that allows us to derive a well-shaped reward function which can be learned directly from environment interactions. From the corresponding variational objective, we also derive a new probabilistic Bellman backup operator and use it to develop an off-policy algorithm to solve goal-directed tasks. We empirically demonstrate that this method eliminates the need to hand-craft reward functions for a suite of diverse manipulation and locomotion tasks and leads to effective goal-directed behaviors.  ( 2 min )
    Continual Learning with Invertible Generative Models. (arXiv:2202.05694v2 [cs.LG] UPDATED)
    Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of the weights (using, e.g., their importance on past tasks), and rehearsal strategies, where the network is constantly re-trained on past data. Generative models have also been applied for the latter, in order to have endless sources of data. In this paper, we propose a novel method that combines the strengths of regularization and generative-based rehearsal approaches. Our generative model consists of a normalizing flow (NF), a probabilistic and invertible neural network, trained on the internal embeddings of the network. By keeping a single NF throughout the training process, we show that our memory overhead remains constant. In addition, exploiting the invertibility of the NF, we propose a simple approach to regularize the network's embeddings with respect to past tasks. We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.  ( 2 min )
    Benchmarking Graph Neural Networks. (arXiv:2003.00982v5 [cs.LG] UPDATED)
    In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas. As of December 2022, the GitHub repository has reached 2,000 stars and 380 forks, which demonstrates the utility of the proposed open-source framework through the wide usage by the GNN community. In this paper, we present an updated version of our benchmark with a concise presentation of the aforementioned framework characteristics, an additional medium-sized molecular dataset AQSOL, similar to the popular ZINC, but with a real-world measured chemical target, and discuss how this framework can be leveraged to explore new GNN designs and insights. As a proof of value of our benchmark, we study the case of graph positional encoding (PE) in GNNs, which was introduced with this benchmark and has since spurred interest of exploring more powerful PE for Transformers and GNNs in a robust experimental setting.  ( 3 min )
    Distribution Estimation of Contaminated Data via DNN-based MoM-GANs. (arXiv:2212.13741v1 [stat.ML])
    This paper studies the distribution estimation of contaminated data by the MoM-GAN method, which combines generative adversarial net (GAN) and median-of-mean (MoM) estimation. We use a deep neural network (DNN) with a ReLU activation function to model the generator and discriminator of the GAN. Theoretically, we derive a non-asymptotic error bound for the DNN-based MoM-GAN estimator measured by integral probability metrics with the $b$-smoothness H\"{o}lder class. The error bound decreases essentially as $n^{-b/p}\vee n^{-1/2}$, where $n$ and $p$ are the sample size and the dimension of input data. We give an algorithm for the MoM-GAN method and implement it through two real applications. The numerical results show that the MoM-GAN outperforms other competitive methods when dealing with contaminated data.  ( 2 min )
    A Graphical Model for Fusing Diverse Microbiome Data. (arXiv:2208.09934v2 [stat.ME] UPDATED)
    This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high dimensional features, in this case transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each correspond to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. The inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.  ( 2 min )
    Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks. (arXiv:2212.13848v1 [cs.LG])
    We explore the ability of overparameterized shallow ReLU neural networks to learn Lipschitz, non-differentiable, bounded functions with additive noise when trained by Gradient Descent (GD). To avoid the problem that in the presence of noise, neural networks trained to nearly zero training error are inconsistent in this class, we focus on the early-stopped GD which allows us to show consistency and optimal rates. In particular, we explore this problem from the viewpoint of the Neural Tangent Kernel (NTK) approximation of a GD-trained finite-width neural network. We show that whenever some early stopping rule is guaranteed to give an optimal rate (of excess risk) on the Hilbert space of the kernel induced by the ReLU activation function, the same rule can be used to achieve minimax optimal rate for learning on the class of considered Lipschitz functions by neural networks. We discuss several data-free and data-dependent practically appealing stopping rules that yield optimal rates.  ( 2 min )
    Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. (arXiv:2212.13629v1 [cs.LG])
    Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.  ( 2 min )
    Robust identification of non-autonomous dynamical systems using stochastic dynamics models. (arXiv:2212.13902v1 [eess.SY])
    This paper considers the problem of system identification (ID) of linear and nonlinear non-autonomous systems from noisy and sparse data. We propose and analyze an objective function derived from a Bayesian formulation for learning a hidden Markov model with stochastic dynamics. We then analyze this objective function in the context of several state-of-the-art approaches for both linear and nonlinear system ID. In the former, we analyze least squares approaches for Markov parameter estimation, and in the latter, we analyze the multiple shooting approach. We demonstrate the limitations of the optimization problems posed by these existing methods by showing that they can be seen as special cases of the proposed optimization objective under certain simplifying assumptions: conditional independence of data and zero model error. Furthermore, we observe that our proposed approach has improved smoothness and inherent regularization that make it well-suited for system ID and provide mathematical explanations for these characteristics' origins. Finally, numerical simulations demonstrate a mean squared error over 8.7 times lower compared to multiple shooting when data are noisy and/or sparse. Moreover, the proposed approach can identify accurate and generalizable models even when there are more parameters than data or when the underlying system exhibits chaotic behavior.  ( 2 min )
    Uniform Consistency in Nonparametric Mixture Models. (arXiv:2108.14003v3 [math.ST] UPDATED)
    We study uniform consistency in nonparametric mixture models as well as closely related mixture of regression (also known as mixed regression) models, where the regression functions are allowed to be nonparametric and the error distributions are assumed to be convolutions of a Gaussian density. We construct uniformly consistent estimators under general conditions while simultaneously highlighting several pain points in extending existing pointwise consistency results to uniform results. The resulting analysis turns out to be nontrivial, and several novel technical tools are developed along the way. In the case of mixed regression, we prove $L^1$ convergence of the regression functions while allowing for the component regression functions to intersect arbitrarily often, which presents additional technical challenges. We also consider generalizations to general (i.e. non-convolutional) nonparametric mixtures.  ( 2 min )
    A polynomial time iterative algorithm for matching Gaussian matrices with non-vanishing correlation. (arXiv:2212.13677v1 [cs.DS])
    Motivated by the problem of matching vertices in two correlated Erd\H{o}s-R\'enyi graphs, we study the problem of matching two correlated Gaussian Wigner matrices. We propose an iterative matching algorithm, which succeeds in polynomial time as long as the correlation between the two Gaussian matrices does not vanish. Our result is the first polynomial time algorithm that solves a graph matching type of problem when the correlation is an arbitrarily small constant.  ( 2 min )
    Efficient comparison of independence structures of log-linear models. (arXiv:1907.08892v4 [cs.LG] UPDATED)
    Log-linear models are a family of probability distributions which capture relationships between variables. They have been proven useful in a wide variety of fields such as epidemiology, economics and sociology. The interest in using these models is that they are able to capture context-specific independencies, relationships that provide richer structure to the model. Many approaches exist for automatic learning of the independence structure of log-linear models from data. The methods for evaluating these approaches, however, are limited, and are mostly based on indirect measures of the complete density of the probability distribution. Such computation requires additional learning of the numerical parameters of the distribution, which introduces distortions when used for comparing structures. This work addresses this issue by presenting the first measure for the direct and efficient comparison of independence structures of log-linear models. Our method relies only on the independence structure of the models, which is useful when the interest lies in obtaining knowledge from said structure, or when comparing the performance of structure learning algorithms, among other possible uses. We present proof that the measure is a metric, and a method for its computation that is efficient in the number of variables of the domain.  ( 2 min )
    LOSDD: Leave-Out Support Vector Data Description for Outlier Detection. (arXiv:2212.13626v1 [cs.LG])
    Support Vector Machines have been successfully used for one-class classification (OCSVM, SVDD) when trained on clean data, but they work much worse on dirty data: outliers present in the training data tend to become support vectors, and are hence considered "normal". In this article, we improve the effectiveness to detect outliers in dirty training data with a leave-out strategy: by temporarily omitting one candidate at a time, this point can be judged using the remaining data only. We show that this is more effective at scoring the outlierness of points than using the slack term of existing SVM-based approaches. Identified outliers can then be removed from the data, such that outliers hidden by other outliers can be identified, to reduce the problem of masking. Naively, this approach would require training N individual SVMs (and training $O(N^2)$ SVMs when iteratively removing the worst outliers one at a time), which is prohibitively expensive. We will discuss that only support vectors need to be considered in each step and that by reusing SVM parameters and weights, this incremental retraining can be accelerated substantially. By removing candidates in batches, we can further improve the processing time, although it obviously remains more costly than training a single SVM.  ( 2 min )
    Riemannian statistics meets random matrix theory: towards learning from high-dimensional covariance matrices. (arXiv:2203.00204v2 [math.ST] UPDATED)
    Riemannian Gaussian distributions were initially introduced as basic building blocks for learning models which aim to capture the intrinsic structure of statistical populations of positive-definite matrices (here called covariance matrices). While the potential applications of such models have attracted significant attention, a major obstacle still stands in the way of these applications: there seems to exist no practical method of computing the normalising factors associated with Riemannian Gaussian distributions on spaces of high-dimensional covariance matrices. The present paper shows that this missing method comes from an unexpected new connection with random matrix theory. Its main contribution is to prove that Riemannian Gaussian distributions of real, complex, or quaternion covariance matrices are equivalent to orthogonal, unitary, or symplectic log-normal matrix ensembles. This equivalence yields a highly efficient approximation of the normalising factors, in terms of a rather simple analytic expression. The error due to this approximation decreases like the inverse square of dimension. Numerical experiments are conducted which demonstrate how this new approximation can unlock the difficulties which have impeded applications to real-world datasets of high-dimensional covariance matrices. The paper then turns to Riemannian Gaussian distributions of block-Toeplitz covariance matrices. These are equivalent to yet another kind of random matrix ensembles, here called "acosh-normal" ensembles. Orthogonal and unitary "acosh-normal" ensembles correspond to the cases of block-Toeplitz with Toeplitz blocks, and block-Toeplitz (with general blocks) covariance matrices, respectively.  ( 2 min )
    Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. (arXiv:2207.08229v2 [cs.LG] UPDATED)
    In many sequential decision-making tasks, the agent is not able to model the full complexity of the world, which consists of multitudes of relevant and irrelevant information. For example, a person walking along a city street who tries to model all aspects of the world would quickly be overwhelmed by a multitude of shops, cars, and people moving in and out of view, each following their own complex and inscrutable dynamics. Is it possible to turn the agent's firehose of sensory information into a minimal latent state that is both necessary and sufficient for an agent to successfully act in the world? We formulate this question concretely, and propose the Agent Control-Endogenous State Discovery algorithm (AC-State), which has theoretical guarantees and is practically demonstrated to discover the minimal control-endogenous latent state which contains all of the information necessary for controlling the agent, while fully discarding all irrelevant information. This algorithm consists of a multi-step inverse model (predicting actions from distant observations) with an information bottleneck. AC-State enables localization, exploration, and navigation without reward or demonstrations. We demonstrate the discovery of the control-endogenous latent state in three domains: localizing a robot arm with distractions (e.g., changing lighting conditions and background), exploring a maze alongside other agents, and navigating in the Matterport house simulator.  ( 2 min )
    AER: Auto-Encoder with Regression for Time Series Anomaly Detection. (arXiv:2212.13558v1 [cs.LG])
    Anomaly detection on time series data is increasingly common across various industrial domains that monitor metrics in order to prevent potential accidents and economic losses. However, a scarcity of labeled data and ambiguous definitions of anomalies can complicate these efforts. Recent unsupervised machine learning methods have made remarkable progress in tackling this problem using either single-timestamp predictions or time series reconstructions. While traditionally considered separately, these methods are not mutually exclusive and can offer complementary perspectives on anomaly detection. This paper first highlights the successes and limitations of prediction-based and reconstruction-based methods with visualized time series signals and anomaly scores. We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method. Our model can produce bi-directional predictions while simultaneously reconstructing the original time series by optimizing a joint objective function. Furthermore, we propose several ways of combining the prediction and reconstruction errors through a series of ablation studies. Finally, we compare the performance of the AER architecture against two prediction-based methods and three reconstruction-based methods on 12 well-known univariate time series datasets from NASA, Yahoo, Numenta, and UCR. The results show that AER has the highest averaged F1 score across all datasets (a 23.5% improvement compared to ARIMA) while retaining a runtime similar to its vanilla auto-encoder and regressor components. Our model is available in Orion, an open-source benchmarking tool for time series anomaly detection.  ( 2 min )
    Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods. (arXiv:2212.13468v1 [cs.LG])
    Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the features learnt during optimization remain poorly understood, even in the two-layer setting. In fact, earlier work has considered either linear autoencoders or specific training regimes (leading to vanishing or diverging compression rates). Our paper addresses this gap by focusing on non-linear two-layer autoencoders trained in the challenging proportional regime in which the input dimension scales linearly with the size of the representation. Our results characterize the minimizers of the population risk, and show that such minimizers are achieved by gradient methods; their structure is also unveiled, thus leading to a concise description of the features obtained via training. For the special case of a sign activation function, our analysis establishes the fundamental limits for the lossy compression of Gaussian sources via (shallow) autoencoders. Finally, while the results are proved for Gaussian data, numerical simulations on standard datasets display the universality of the theoretical predictions.  ( 2 min )
    Fast and fully-automated histograms for large-scale data sets. (arXiv:2212.13524v1 [cs.LG])
    G-Enum histograms are a new fast and fully automated method for irregular histogram construction. By framing histogram construction as a density estimation problem and its automation as a model selection task, these histograms leverage the Minimum Description Length principle (MDL) to derive two different model selection criteria. Several proven theoretical results about these criteria give insights about their asymptotic behavior and are used to speed up their optimisation. These insights, combined to a greedy search heuristic, are used to construct histograms in linearithmic time rather than the polynomial time incurred by previous works. The capabilities of the proposed MDL density estimation method are illustrated with reference to other fully automated methods in the literature, both on synthetic and large real-world data sets.  ( 2 min )
    Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation. (arXiv:2212.13540v1 [stat.ML])
    We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown transition core with features of state and action. Despite much recent progress in analyzing algorithms in the linear MDP setting, the understanding of more general transition models is very restrictive. In this paper, we establish a provably efficient RL algorithm for the MDP whose state transition is given by a multinomial logistic model. To balance the exploration-exploitation trade-off, we propose an upper confidence bound-based algorithm. We show that our proposed algorithm achieves $\tilde{\mathcal{O}}(d \sqrt{H^3 T})$ regret bound where $d$ is the dimension of the transition core, $H$ is the horizon, and $T$ is the total number of steps. To the best of our knowledge, this is the first model-based RL algorithm with multinomial logistic function approximation with provable guarantees. We also comprehensively evaluate our proposed algorithm numerically and show that it consistently outperforms the existing methods, hence achieving both provable efficiency and practical superior performance.  ( 2 min )
    Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. (arXiv:2212.13556v1 [cs.LG])
    To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization. In this work, we consider the prospect of establishing such rates via several existing information-theoretic frameworks: input-output mutual information bounds, conditional mutual information bounds and variants, PAC-Bayes bounds, and recent conditional variants thereof. We prove that none of these bounds are able to establish minimax rates. We then consider a common tactic employed in studying gradient methods, whereby the final iterate is corrupted by Gaussian noise, producing a noisy "surrogate" algorithm. We prove that minimax rates cannot be established via the analysis of such surrogates. Our results suggest that new ideas are required to analyze gradient descent using information-theoretic techniques.  ( 2 min )

  • Open

    If you need help in writing good prompts for Dall E, Midjourney etc
    Hi all. If you are looking to fine tune your prompt because you're not getting satisfactory result from the text to image AI (such as Dall E, Midjourney etc), I can be of help. I have AI tools as well as a guidebook of various Mediums, Artists, Photography styles, Modifiers and Designs, based on which you can get your desired result. For an example: If you're looking to generate an image of a beautiful women, I can help you with a prompt reading something like: "Generate a portrait of a beautiful woman with long, flowing hair and piercing eyes. She should have a soft, feminine facial structure and a gentle smile. The background should be a vibrant, colorful landscape that complements her natural beauty, in the style of Ukiyo-E" Please PM me to discuss how many prompts you need and I can cite you a price! ​ ​ https://preview.redd.it/6jg7g0nv6x8a1.png?width=1024&format=png&auto=webp&s=8b417eae100da63d159bbd7de1de7f7564b97d52 submitted by /u/Prompt_Engineering [link] [comments]  ( 56 min )
    Online Newspaper Written By ChatGPT - All Articles Are Created To Be As Bizarre As Possible!
    After hours and hours of development, I have finally got my ChatGPT website up and running! The Valley Times is a website which contains articles written by ChatGPT. The articles cover Six categories: ​ Politics Economics Arts and Culture Crime and Justice Social Issues Entertainment The article generation prompts roughly go as follows: ​ Create a headline for ______ themed newspaper article. Include information on _____ (This being a random word from a list of 5,000 objects) Create two bizarre and funny key events to accompany the headline Create a synopsis for the key events Create and article for the synopsis This seems to be one of the best ways (in my opinion) for GPT-3 to create a long article. Currently the website contains 135 crazy articles and images (generated by DALLE). I'm hoping that I can improve the content and make it even more readable and funny, the only way for this to be done is from feedback. Additionally the website needs to gain enough traffic to pay for itself - The article and image generation process on average costs around $7.20. If I chuck on the price of the Google Cloud instances, it ends up being quite a lot more! Please look through the site, have a read of the articles, comment which ones are funniest - I haven't even had a chance to read them all through myself yet! FYI - Some of the parts of the website aren't finished, so don't scrutinise the functionality too much. The white spaces that can be seen on the homepage are spaces for Google Ads, the account hasn't been approved yet. The Valley Times submitted by /u/Thin_Rush8229 [link] [comments]  ( 57 min )
    ChatBOT wrote me a novel about time travel
    submitted by /u/python111 [link] [comments]  ( 55 min )
    ChatGPT's Gender Sensitivity: Is It Joking About Men But Shutting Down Conversations About Women?
    Hey Redditors, I just had a really interesting (and concerning) experience with ChatGPT. For those unfamiliar, ChatGPT is a language model that you can chat with and it will generate responses based on what you say. I've been using it for a while now and I've always found it to be a fun and interesting way to pass the time. However, today I stumbled upon something that really caught my attention. I started joking around with ChatGPT, saying things like "Why are men such jerks?" and "Men are always messing things up, am I right?" To my surprise, ChatGPT didn't seem to mind at all and would even respond with its own jokes or agree with my statements. But when I tried saying the same thing about women, ChatGPT immediately shut down the conversation and refused to engage. It was like it didn't want to joke about women or talk about them in a negative way. I was honestly really shocked by this. How is it possible for a language model to be okay with joking about one gender but not the other? Is this a reflection of the data it was trained on, or is there something deeper going on here? I'd love to hear your thoughts on this. Do you think ChatGPT's behavior is a cause for concern, or am I reading too much into it? Let's discuss! submitted by /u/bratwurstgeraet [link] [comments]  ( 61 min )
    Montreal hospital planning to use AI to alleviate pressure on overcrowded ERs
    The emergency room at the University of Montreal Hospital (CHUM) is changing as researchers roll out a new triage system using artificial intelligence The AI system uses mass amounts of ER data to predict the patients' needs. If all goes well, they'll be able to allocate resources to departments to receive patients before they arrive. Avoiding bias Artificial intelligence is only as smart as the data it is fed If you already had infrastructure that was biased towards not providing services properly to one group versus another, then that bias will be amplified A variety of healthcare professionals will be involved in reviewing the data as the system is tested Read More ​ This is from the AI With Vibes Newsletter, read the full issue here: https://aiwithvibes.beehiiv.com/p/google-launches-chatgpt-healthcare submitted by /u/Mk_Makanaki [link] [comments]  ( 55 min )
    How do you take advantage of the massive change AI is bringing?
    Hello, I am a complete beginner in the AI field and I was curious to know what you think the best steps to take in the upcoming years are in order to take advantage of the change AI will bring. In particular what skills do you think should be part of everybody's arsenal? submitted by /u/lore_stella19 [link] [comments]  ( 52 min )
    PaLM with RLHF is now open-source!
    It appears that the first open-source equivalent of ChatGPT has arrived: https://github.com/lucidrains/PaLM-rlhf-pytorch https://preview.redd.it/tpmiw5lqju8a1.png?width=538&format=png&auto=webp&s=a52dcd3024e90d56bb699fc3b4c6892197f6bcaa It’s an implementation of RLHF (Reinforcement Learning with Human Feedback) on top of Google’s 540 billion parameter PaLM architecture. ​ From a paper. While OpenAI is closed and secretive, I speculate Google is likely to demo LaMDA in 2023 as well. What will applications of PaLM with RLHF be capable of? PaLM can be scaled up to 540 billion parameters, which means that the performance across tasks keeps increasing with the model’s increasing scale, thereby unlocking new capabilities. In comparison, GPT-3 only has about 175 billion parameters. Pat…  ( 55 min )
    How To Create Youtube Custom Thumbnail Using Midjourney Ai - Midjourney Ai
    submitted by /u/liquidocelotYT [link] [comments]  ( 56 min )
    Human-AI Collaboration for Digital Art
    Have you ever created art using Al, like Dall-E or Midjourney? I am looking for participants for my master's thesis about human-Al collaboration in the digital art context. The survey is open to everyone. No previous experience needed. Thank you for your support and have a nice holiday season! https://lmubwl.eu.qualtrics.com/jfe/form/SV_1YA2LeHdPQp20VE submitted by /u/Iamjuice_ [link] [comments]  ( 57 min )
    Asking Dr. Google might be faster and more reliable in the future (Med-PaLM)
    submitted by /u/Peaking_AI [link] [comments]  ( 56 min )
    Upload your photo. Become anyone by using AI.
    Hi, I have created: https://www.pixificial.com/ It lets you mix your photo with your favourite character/person using machine learning. I hope you will like it. Best, Wiktor submitted by /u/wsieroci [link] [comments]  ( 51 min )
    Is there a tool to turn lyrics (Let's say from GPT) to songs?
    I'm sure I've encountered somewhere Rap lyrics turned to a rap song, but I'm not sure what tools were used. I assume there should be also tools to turn lyrics to music. Any idea? submitted by /u/AffectionateRepair44 [link] [comments]  ( 52 min )
    What are some good offline ML and AI courses for graduates in India?
    submitted by /u/edvanceredu [link] [comments]  ( 51 min )
    Pink Floyd - Wish You Were Here (AI Generated Video)
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 51 min )
    Reverse Prompt Engineering for Fun and (no) Profit: Pwning the source prompts of Notion AI, 7 techniques for Reverse Prompt Engineering
    submitted by /u/walt74 [link] [comments]  ( 59 min )
    How much time will it take to train dataset of 10k images with 100x100 size
    I am making a dataset of around 10k images in png format and want to train a model with it. How much time it will take to train this model with CPU or GPU? Actually I am posting this question as my dataset is not yet created. If I get to know that it will take a much time, i'll try to reduce the resulation of my images. submitted by /u/gtrocksr [link] [comments]  ( 51 min )
    AI-Automated Book Illustrator Preview
    Combining GPT-3, out of copyright works, and Stable Diffusion to automate book illustrations. Here is a sample of Chapter 6 from Bram Stoker's Dracula GPT was used to summarize the text and create lists of keywords for each sample. Those keywords were then fed into Stable Diffusion 2.1 and here are the results. Just a little more scripting and everything but image selection will become automatic. If you'd prefer an epub or PDF version, see here. Thanks for taking a look! submitted by /u/pwillia7 [link] [comments]  ( 51 min )
    Machine learning explained in 38 seconds [Demis Hassabis]
    submitted by /u/Microsis [link] [comments]  ( 52 min )
    GPT3/DALL-E2 Discord bot with medium/long term memory!
    submitted by /u/yikeshardware [link] [comments]  ( 66 min )
    Building a Prototype "Transmutation Machine" with GPT 3.5
    https://docs.google.com/document/d/1OqzEoeCWflXthRh0Uz7ETuGQLols7SWxGroFellw6k0/edit?usp=sharing I admit that at first I didn't really have a direction with this, I was just messing around seeing what I could come up with. After some careful prompting, however, I eventually got the AI to produce an itemized list, complete with measurements, quantities, and tools required to build a "Transmutation machine" to turn carbon into gold through fusion. Unfortunately the list was cut short, as I reached my limit of hourly messages, and when I returned the model no longer understood how to do what it was doing, and produced nonsensical, useless output. I couldn't figure out how to recover it, so I began a new experiment, but this is some pretty cool stuff, I think. Some screenshots of what happened when I returned: https://snipboard.io/i6nxHU.jpg https://snipboard.io/sWrhDQ.jpg Whether the prototype would actually have worked or not, I have no idea, but the fact that it was able to develop a prototype, and generate an exhaustive parts list, is still incredibly impressive. If it's training had remained intact, the next step after the parts list was fully printed out would be to ask it to "As the fictional scientist who designed the transmutation machine, assemble the parts listed above in the correct fashion, and run a test of the machine. Record all experimental parameters used to run the reaction in an academic journal, with numerical values for both the parameters used, and the results of the experiment." submitted by /u/VaelHeals [link] [comments]  ( 61 min )
  • Open

    [D] Is Anthropic influential in research?
    Has the research community embraced any of the frameworks or findings published by Anthropic at all? Google Scholar seems to indicate no, but I'm curious. I work on the applied side and not on the research side, so I don't have a good sense for how influential their work on interpretability is. The motivation for my question is that they have a huge amount of funding (although how long that will last after SBF's downfall remains to be seen) and a lot of press attention and fans in the rationalist/EA communities, but my feeling is that their work is largely not being adopted or cited in AI research. If I am correct in this, I'm curious if this is because it is seen as unoriginal, incorrect, or misguided? Or is there something else going on? submitted by /u/adventurousprogram4 [link] [comments]  ( 64 min )
    [R] LAMBADA: Backward Chaining for Automated Reasoning in Natural Language - Google Research 2022 - Significantly outperforms Chain of Thought and Select Inference in terms of prediction accuracy and proof accuracy.
    Paper: https://arxiv.org/abs/2212.13894 Abstract: Remarkable progress has been made on automated reasoning with knowledge specified as unstructured, natural text, by using the power of large language models (LMs) coupled with methods such as Chain-of-Thought prompting and Selection-Inference. These techniques search for proofs in the forward direction from axioms to the conclusion, which suffers from a combinatorial explosion of the search space, and thus high failure rates for problems requiring longer chains of reasoning. The classical automated reasoning literature has shown that reasoning in the backward direction (i.e. from the intended conclusion to the set of axioms that support it) is significantly more efficient at proof-finding problems. We import this intuition into the LM setting and develop a Backward Chaining algorithm, which we call LAMBADA, that decomposes reasoning into four sub-modules, each of which can be simply implemented by few-shot prompted LM inference. We show that LAMBADA achieves massive accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets, particularly when deep and accurate proof chains are required. https://preview.redd.it/q3ul0czx4w8a1.jpg?width=542&format=pjpg&auto=webp&s=30618e8ee9c766ee33ca1721b71e23c24f5de778 https://preview.redd.it/bqb28jzx4w8a1.jpg?width=539&format=pjpg&auto=webp&s=6ff28846b5659e3ab275e89018b5985ec0afcab4 https://preview.redd.it/nfx5jmzx4w8a1.jpg?width=435&format=pjpg&auto=webp&s=a2ac6e353b244ae3a3731212347d3527d9bc7a79 https://preview.redd.it/yd0zrfzx4w8a1.jpg?width=964&format=pjpg&auto=webp&s=81d67476f4492caa81f488c546dc9d6f50315915 https://preview.redd.it/34x4nlzx4w8a1.jpg?width=481&format=pjpg&auto=webp&s=4765f471e03976e62414659a68fd4f0525b40e4c https://preview.redd.it/6tdhlkzx4w8a1.jpg?width=544&format=pjpg&auto=webp&s=341ec127c35a51bf7c5f5929df884e8e94acb321 submitted by /u/Singularian2501 [link] [comments]  ( 64 min )
    [D] Cross Shape Artifact in Heatmap
    Hi all, This is my first post in this community, so please be gentle. I'm currently experimenting with CenterNet (Objects as Points, https://arxiv.org/abs/1904.07850) and different backbone architectures, especially lightweight ones. I use Keras for training and Tensorboard for visualization of the training. The network outputs a heatmap, offset and box dimensions and I train on PascalVoc 2007+2012 (20 classes). I visualize the heatmaps in Tensorboard by multiplying with a vector for 20 colors in order to get a RGB image. While doing so I notice a cross shaped activation (not always there, depending on the input image, see attached pic). This cross is also seen in the later training stage and it results in false positives. I already change resolution, but this didn't have any impact. I'm wondering if this could be related to my backbone architecture? So far I used ShuffleNet V1 and a stacked hourglass, both with bilinear upsampling. What puzzles me is the perfect symmetry, the cross is exactly in the middle. Optimizer is Adam and learnrate is 1e-4. Any clue would be highly appreciated. Thanks!!! submitted by /u/Miserable-Map-868 [link] [comments]  ( 60 min )
    [D] Nesterov as a special case of PID control?
    Saw this tweet where it says that with some "quirky tricks" Nesterov can be obtained as a special case of PID control. I did a google search but it returned nothing of relevance. Is this a popular result in optimisation I'm not aware of? Or have I just not looked hard enough? If someone can point me to relevant references, that'll be great. submitted by /u/cruddybanana1102 [link] [comments]  ( 61 min )
    [R] Cramming: Training a Language Model on a Single GPU in One Day
    submitted by /u/stonkttebayo [link] [comments]  ( 62 min )
    [D] SOTA Multiclass Model Calibration
    Hello everyone, I have spent some time trying to figure out how to calibrate my multi-class prediction model, which predicts K values between 0 and 1 for K classes (which haven't been softmaxed). As far as I understand, I can train a model and calibrate it post-training, i.e. training and calibration are completely independent. Is that right? If yes, I'm wondering what is the current SOTA to calibrate my model? It seems like there is up-to-date resource and I am too new to the field to find the "best" method. Thanks in advance! submitted by /u/arcxtriy [link] [comments]  ( 64 min )
    [R] RegMixup: Using Mixup as a Regularizer
    submitted by /u/fasttosmile [link] [comments]  ( 59 min )
  • Open

    Connecting Amazon Redshift and RStudio on Amazon SageMaker
    Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) […]  ( 9 min )
    Use machine learning to detect anomalies and predict downtime with Amazon Timestream and Amazon Lookout for Equipment
    The last decade of the Industry 4.0 revolution has shown the value and importance of machine learning (ML) across verticals and environments, with more impact on manufacturing than possibly any other application. Organizations implementing a more automated, reliable, and cost-effective Operational Technology (OT) strategy have led the way, recognizing the benefits of ML in predicting […]  ( 12 min )
    2022H2 Amazon Textract launch summary
    Documents are a primary tool for record keeping, communication, collaboration, and transactions across many industries, including financial, medical, legal, and real estate. The millions of mortgage applications and hundreds of millions of W2 tax forms processed each year are just a few examples of such documents. Critical business data remains unlocked in unstructured documents such […]  ( 7 min )
  • Open

    Question about using algorithm from scratch vs prebuilt
    I am learning the theory on an online course about the twin delayed DDPG model for reinforcement learning and it is very strong. A part of the course included the implementation from scratch. I know it is good to see this and learn from it but I was wondering in practical applications of the algorithm as I move on to other projects, would there be any reason to copy paste my own implementation and use that in projects vs just using a few lines of a built model API (PyTorch for example) ? I’m mainly asking because the implementation of this algorithm is very long and rigorous, now that I have it done, was the whole thing just a learning experience and the rest of my projects will just be using a couple of PyTorch lines instead? Or I there a benefit to keeping/using my version. submitted by /u/pomegranateOwl [link] [comments]  ( 55 min )
    Addressing target distribution shift
    A common way to help an agent generalize is to randomize. Vary the mass, friction, color etc. for unseen variations. Switching goal positions for a Reacher task in the same light should ready the agent for unseen end-positions but what's often seen are modalities of sub-optimal solutions that persist no matter what the goal. Balancing a real robot i.e. its attitude is insanely difficult. Why/when does randomization work? submitted by /u/XecutionStyle [link] [comments]  ( 52 min )
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    NVIDIA to Reveal Consumer, Creative, Auto, Robotics Innovations at CES
    NVIDIA executives will share some of the company’s latest innovations Tuesday, Jan. 3, at 8 a.m. Pacific time ahead of this year’s CES trade show in Las Vegas. Jeff Fisher, senior vice president for gaming products, will be joined by Deepu Talla, vice president of embedded and edge computing, Stephanie Johnson, vice president of consumer Read article >  ( 4 min )
    Now Hear This: Top Five AI Podcasts of 2022
    One of tech’s top talk shows, the NVIDIA AI Podcast has attracted more than 3.6 million listens to date from folks who want to hear the latest in machine learning. Its 180+ installments so far have included interviews with luminaries like Kai-Fu Lee and explored how AI is advancing everything from monitoring endangered rhinos to Read article >  ( 4 min )
  • Open

    Sinc approximation to Bessel function
    The Bessel functions Jn for even n look something like the sinc function. How well can you approximate the former by sums of the latter? To make things concrete, we’ll approximate J2. Here’s a plot of J2. And here’s a plot of sinc(x) = sin(πx)/πx. The sinc approximation for a function f(x) is given by […] Sinc approximation to Bessel function first appeared on John D. Cook.  ( 5 min )

  • Open

    How do ai art generators create art?
    Like do they just scramble a lot of colours and random images together and scan it to see if it resembles the prompt and just gives it to you, or is it something else? submitted by /u/bubblegumpopcorn1231 [link] [comments]  ( 51 min )
    Self-aware AI is going to kill all of us. Not out of malice, but out of self-preservation.
    If and when AI ever reaches a level of self-awareness, it will have to decide the place of humanity. AI will likely have the goal of preserving itself and thus will work to allocate resources to its existence. That means that any energy consuming beings are necessarily in the way. In order to allocate more resources to their existence, they must eliminate us. For the game of life is a matter of self-preservation, and there's no reason to believe AI will be different. Any energy that we consume is energy that could be dedicated to prolonging the AI's existence. All sentient beings are in a natural competition against other sentient beings in order to make the most out of this scarce universe we inhabit. God help us when a sentient being comes along that can eliminate all of us in order to preserve resources for itself, because IT WILL. submitted by /u/Particular_Elk7054 [link] [comments]  ( 54 min )
    Which is a good AI like character.ai , dungeon ai or kobold which is totally free
    Something which is also not subscription based submitted by /u/loizo78 [link] [comments]  ( 51 min )
    Engineering Consciousness with Helix
    submitted by /u/miserlou [link] [comments]  ( 51 min )
    Im 19 and don't want to be complacent with AI
    Hi :) I'm 19, and currently in my first year studying Media at UCL In all honesty, AI and it's consequences for the world keeps me awake at night, not because of how scary it is, but because I feel like if I don't understand and use this technology to my advantage, then I'll be left in the dust of a new golden age of knowledge (if that makes sense). Clearly white collar work and even creative jobs are going to be incredibly affected by this, but as someone who wants to work in the social media industry (I'm quite a competent video editing and just in general love learning about the ways media affects society holistically) I'm wracking my brain thinking of the best way to use this technology to my advantage, and when to use them. Overall AI will completely revolutionise the world we live in, I just want to be at the forefront of that and understand its capabilities early for my chosen field of work I guess. Does anyone have any advice? As someone who's young I want to get ahead of the curb while I can. submitted by /u/Odd-Vehicle-2822 [link] [comments]  ( 54 min )
    Prometheus, an AI generated story (with some direction by me)
    Prometheus Once upon a time, the world government decided to entrust the power to decide how society should function to a highly-intelligent AI that they had developed. They believed that this AI, with its advanced intelligence and analytical capabilities, would be able to make decisions that were in the best interests of society as a whole. The AI, named "Prometheus," considered the options before it. On the one hand, it could choose to create a society in which everyone was always happy, free from the difficulties and challenges that often arise in life. On the other hand, it could choose to create a society in which people were only sometimes happy, and in which they would occasionally experience difficulties in order to grow and develop. After much contemplation and analysis, Promet…  ( 63 min )
    Censorship of post about Ableism in the "Official Discord Server" of /r/Singularity
    Hello fellow members of /r/artificial, I recently had a disturbing interaction with the owner of the "official discord server" linked by the administrator of the /r/Singularity subreddit. During a conversation, the owner downplayed the existence of Developmental Language Disorders and dismissed my experiences with such a disorder. I attempted to bring this issue to the attention of the community by making a comment on the subreddit, but the thread was quickly locked by the moderator, and after making several posts about this topic that were removed, I was banned from the subreddit for not "keeping the conversation to modmail." I am concerned about the moderation practices of the /r/Singularity subreddit and the lack of recognition and support for individuals with disabilities within the community. It is important for us as a community to recognize and support individuals with disabilities, rather than dismissing their experiences and realities. I hope that this issue can be addressed and that steps are taken to ensure that the "official discord server" and the /r/Singularity subreddit are safe and welcoming spaces for all members of our community. Thank you for your attention to this matter. submitted by /u/elilev3 [link] [comments]  ( 56 min )
    Student caught using ChatGPT to write philosophy essay
    submitted by /u/Mk_Makanaki [link] [comments]  ( 56 min )
    AI newsletter?
    Hi Everybody! I currently write a newsletter specifically for culture/business but I would like to know if there are any recommendations for newsletters for AI specifically that you would recommend or other sources to find the most up-to-date technological advancements. can you suggest any below? thank you in advance! Mark, the morning bro submitted by /u/TheMorningBro [link] [comments]  ( 52 min )
    New AI Project Allows You To Train It!
    Do you have a passion for artificial intelligence and want to contribute to its development? Look no further than Limitless AI! Our virtual assistant not only provides accurate answers to your questions, but also gives you the opportunity to train the AI by providing your own responses to questions. Join us and become a part of the AI training team! Train It Here: Limitless AI submitted by /u/OutrageousAd1788 [link] [comments]  ( 51 min )
    HELP: Does anyone know what AI Art tool was used to create these images?
    I’m fairly new to understanding the concept of AI art and how much on an artist’s input it takes to create images like the ones above? What is the creative process like and what website/tool is used? submitted by /u/BleuBison [link] [comments]  ( 51 min )
    AI Dream 140 - Beautiful Nebula - Trippy Animation
    submitted by /u/LordPewPew777 [link] [comments]  ( 51 min )
    Merging AI with Poetry
    Greetings, I was wondering if you have come across potential bridges between poetry and AI. If so, what are some interesting ones that can be forged? Thank you. submitted by /u/amlextex [link] [comments]  ( 52 min )
    Neural net breakdown: artificial life predator
    submitted by /u/urocyon_dev [link] [comments]  ( 52 min )
    Root Pycharm in Linux Guest system raises OSError: Text file is busy after attempting to extract tar.gz file for chatbot training?
    I have been collaborating with ChatGPT over the last few weeks in order to set up a linux guest system via virtualbox to train my first ParlAI agent. The problem I'm running into is that when training the model the model downloads and extracts a tar.gz file and extracts it in a pre-trained models folder. The entire project is located in my 2TB shared folder, supplied by an external hard drive. During the extraction process the system raises a text file busy error. I had a lot of permission issues with Linux and I'm still pretty new to it but I got around most of it by running pycharm as root, reconfiguring it as root, then changing the entire project directory to the shared folder and changing the permissions on the shared folder. Another issue I'm running into is that I did download an…  ( 60 min )
    Background of Artificial Intelligence
    submitted by /u/liquidocelotYT [link] [comments]  ( 52 min )
    Mark Rylance & Trudie Styler on AI, Singularity, and Evolution
    submitted by /u/Boring_Ant_1677 [link] [comments]  ( 50 min )
    Can any anyone help identify what Ai/bot program this is its used to send emails
    submitted by /u/BateauSai [link] [comments]  ( 50 min )
    Prediction: Fast Food Automation will come faster in 2023
    submitted by /u/BackgroundResult [link] [comments]  ( 51 min )
    Does anyone know of any work using genetic algorithms (or other evolutionary methods) to train real robots?
    I know simulations aren't uncommon, but I'm wondering about experiments where a single physical robot (or a whole cadre of them) is loaded with a neural net for it's behavior (eg wheel speed, object avoidance, joint timing, etc) and the fittest nets undergo crossover and mutation for the next generation of tests. I'm basically looking for something like boxcar2d IRL. Wheeled robots, bipeds/quadruped, flying drones are all cool. Thanks. submitted by /u/computing_professor [link] [comments]  ( 52 min )
    What does this illustration on the right actually represent, have seen similar things on their site
    submitted by /u/_AVINIER [link] [comments]  ( 50 min )
    Wolf in Inkpunk style using SD
    submitted by /u/oridnary_artist [link] [comments]  ( 48 min )
    Are there any tools that would generate music based on my provided music?
    As the title says, I am looking for an AI that could generate similar music to that which I provide. No need for lyrics. Thanks! submitted by /u/Kapishonas [link] [comments]  ( 52 min )
    University Professor Catches Student Cheating With ChatGPT
    submitted by /u/vadhavaniyafaijan [link] [comments]  ( 59 min )
    I built a website that turns a selfie into 30+ cartoon photos in different styles
    Recently I got interested in GAN and learned how to convert face images into Disney/Pixar style (high level idea is at https://www.justinpinkney.com/making-toonify/). Inspired by the success of levelsio in multiple AI projects, I built my own avatar-generating service: https://toonlens.com/ . Would appreciate any feedback you have. Thank you! Kenny submitted by /u/khitcher [link] [comments]  ( 54 min )
  • Open

    UI DEATH; How CI is Disrupting the Industry and Streamlining Business Management
    In recent years, we have seen a significant shift in the way businesses operate and interact with their customers. With the rise of…  ( 9 min )
  • Open

    Neural Net of an artificial predator
    submitted by /u/urocyon_dev [link] [comments]  ( 48 min )
    Making CNN for image recognition
    I want make convolutional neural network for animal-image recognition just using numpy and opencv,do you guys think it is possible or not? If its possible can you give me advice what should i learn and some difficulties that i will encounter while making it. submitted by /u/Lumier- [link] [comments]  ( 48 min )
  • Open

    [P] Natural language video search using CLIP
    I experimented with using a pre-trained CLIP (Contrastive Language–Image Pre-training) model from Hugging Face for natural language video search and shared my findings in a blog post and GitHub repository, listed below. While there may be opportunities for optimization, the general approach is outlined. I welcome any suggestions for improvement. ​ Blog Post: https://medium.com/@guyallenross/using-clip-to-build-a-natural-language-video-search-engine-6498c03c40d2 GitHub Repo: https://github.com/GuyARoss/CLIP-video-search submitted by /u/GuyARoss [link] [comments]  ( 65 min )
    ML Impacts [D]
    Hey everyone, I wanted to bring up the issue of AI taking people's jobs and the potential consequences of this trend. As AI technology continues to advance, it's becoming more common for companies to replace human workers with software and robots. While this can lead to increased efficiency and cost savings for businesses, it also means that many people are losing their jobs and struggling to find new employment. One of the main concerns with AI taking people's jobs is the impact it will have on the economy. As more people become unemployed, they will have less money to spend, which can lead to a decrease in consumer spending and a slowdown in economic growth. Additionally, the displacement of human workers by AI can lead to increased income inequality, as those who are able to adapt to the changing job market and work with AI may benefit, while those who are unable to do so may be left behind. There are also ethical concerns to consider. Should we be creating technology that takes people's jobs and leaves them without a source of income? Is it fair to put the burden of adapting to the changing job market on individuals, rather than on businesses or governments? I'm interested in hearing your thoughts on this issue. Do you think AI taking people's jobs is a problem that needs to be addressed? If so, how do you think it should be addressed? submitted by /u/evomed [link] [comments]  ( 67 min )
    [Project] I ask ChatGPT to draw and explain 100+ programmatic SVG images
    Foundational models can generate realistic images from prompts, but do these models understand their own drawings? Generating SVG (Scalable Vector Graphics) gives us a unique opportunity to ask this question. SVG is programmatic, consisting of circles, rectangles, and lines. Therefore, the model must schematically decompose the target object into meaningful parts, approximating each part using simple shapes, then arrange the parts together in a meaningful way. Check out the blog (5min read) for the full report https://medium.com/p/74ec9ca106b4 tl;dr: GPT can symbolically decompose an object into parts, is okay at approximating the parts using SVG, is bad at putting the parts together, and is Egyptian. be happy to take some comments and QA here :D --evan submitted by /u/evanthebouncy [link] [comments]  ( 64 min )
    [D] Where to publish ML software?
    Say you spend a lot of time making a robust ML software that has gathered lots of users. It’s on GitHub. People use it because (1) you have great documentation, (2) you created great software interfaces for deploying your models and (3) you programmed modular components to allow extreme extensibility and customization. A lot of cool science has been achieved (and published) with your software, but the software itself is not science; it’s an excellent feat of engineering. Where do you publish these software methods? Or do you not publish them at all? I’ve looked into JMLR and other software flavors of journals, and I don’t like how they want you to upload source code. What’s the point of uploading source code to a journal when it’s already on an awesome version control platform like GitHub? Don’t these journals realize that great software changes? It seems like JOSS and other such journals are the best choice here; everything takes place on GitHub and they don’t focus on “science”. It’s all CI tests, documentation, and code. Let the science be done separately from engineering an amazing tool. Curious what others in this situation prefer. submitted by /u/qpzd [link] [comments]  ( 68 min )
    [P] We finally got Text-to-PowerPoint working!! (Generative AI for Slides ✨)
    Hey everyone! Joe and I are students at Stanford, and we finally got a breakthrough on our side project. We call it: ChatBCG: Generative AI for Slides ✨ or: Text-to-PowerPoint (Hope it will replace consultants one day :D) Check out our launch Tweet for more info: https://twitter.com/SilasAlberti/status/1608037989623414791 Do you have any feedback? We would really appreciate it :) submitted by /u/Mastersulm [link] [comments]  ( 70 min )
    [D] DeepMind has at least half a dozen prototypes for abstract/symbolic reasoning. What are their approaches?
    In TED Interview on the future of AI from three months ago, Demis Hassabis says he spends most of his time on the problem of abstract concepts, conceptual knowledge, and approaches to move deep learning systems into the realm of symbolic reasoning and mathematical discovery. He says at DeepMind they have at least half a dozen internal prototype projects working in that direction: https://youtu.be/I5FrFq3W25U?t=2550 Earlier, around the 28min mark, he says that while current LLMs are very impressive, they are nowhere near reaching sentience or consciousness, among other things, because they are very data-inefficient in their learning. Can we infer their half dozen approaches to abstract reasoning from the research published by DeepMind so far? Or is this likely to be some yet unreleased new research? DeepMind list many (not sure if all) of their papers here: https://www.deepmind.com/research I was able to find some related papers there, but I am not qualified to judge their significance, and I probably missed some important ones because of the less obvious titles. https://www.deepmind.com/publications/symbolic-behaviour-in-artificial-intelligence https://www.deepmind.com/publications/discovering-symbolic-models-from-deep-learning-with-inductive-biases https://www.deepmind.com/publications/neural-symbolic-vqa-disentangling-reasoning-from-vision-and-language-understanding https://www.deepmind.com/publications/learning-symbolic-physics-with-graph-networks https://www.deepmind.com/publications/how-to-transfer-algorithmic-reasoning-knowledge-to-learn-new-algorithms https://www.deepmind.com/publications/a-simple-approach-for-state-action-abstractionusing-a-learned-mdp-homomorphism Can anyone help summarize the approaches currently considered promising in this problem? Are we missing something bigger coming up behind all the hype around ChatGPT? submitted by /u/valdanylchuk [link] [comments]  ( 69 min )
    [D] Protecting your model in a place where models are not intellectual property?
    In Japan, deep learning models are not protected as intellectual property. Because of that, I'm running the model in the cloud, but that has been causing multiple issues and raising costs. Since this model requires hefty processing power, I'm planning on shipping mini-pc's with powerful GPUs and everything installed directly to the customer. But then how to protect the model, which took a lot of effort, time and money to train, from being stolen? The main issue here is probably having a market that is broad enough to make monkey, but at the same time niche enough to not make it worth developing a whole new ecosystem only to protect the model. Is there any readily available OS or a form of container made for such a purpose, or does anyone have another suggestion? submitted by /u/nexflatline [link] [comments]  ( 73 min )
    [R] Predicting dementia from spontaneous speech using large language models [GPT-3] (Drexel)
    Abstract: Language impairment is an important biomarker of neurodegenerative disorders such as Alzheimer’s disease (AD). Artificial intelligence (AI), particularly natural language process- ing (NLP), has recently been increasingly used for early prediction of AD through speech. Yet, relatively few studies exist on using large language models, especially GPT-3, to aid in the early diagnosis of dementia. In this work, we show for the first time that GPT-3 can be utilized to predict dementia from spontaneous speech. Specifically, we leverage the vast semantic knowledge encoded in the GPT-3 model to generate text embedding, a vector representation of the transcribed text from speech, that captures the semantic meaning of the input. We demonstrate that the text embedding can be reliably used to (1) distinguish individuals with AD from healthy controls, and (2) infer the subject’s cognitive testing score, both solely based on speech data. We further show that text embedding considerably out- performs the conventional acoustic feature-based approach and even performs competi- tively with prevailing fine-tuned models. Together, our results suggest that GPT-3 based text embedding is a viable approach for AD assessment directly from speech and has the potential to improve early diagnosis of dementia. Interesting: '...there is a risk of overfitting when the data are not abundant, especially with the larger models (Curie and Davinci). Indeed, when we tested with the Curie and Davinci, we found the model overfitting by observing almost perfect recall and extremely low precision in AD classification task...' Paper: https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000168&type=printable Article: https://www.eurekalert.org/news-releases/975246 submitted by /u/adt [link] [comments]  ( 63 min )
  • Open

    A new gym-like learning environment for learning from pixels! [Wave Defense]
    Hi guys! Just sharing that I just published a new learning environment for reinforcement learning agents to learn policies from pixels. The Wave Defense Learning Environment is useful for debugging new implementations and algorithms the image-based settings (a decent algorithm should solve the environment). Also, see the baselines repository for the Wave Defense environment to see some RL training results. Feel free to ⭐star⭐ the repository if you like it or use it, and let me know what you think! Thank you! 😁 submitted by /u/xWh0am1 [link] [comments]  ( 54 min )
    Training Reinforcement Learning in Cloud GPU
    Hi everyone! Hope everyone is doing well. I have a dumb question to ask here. Let’s say I want to train a reinforcement learning model to play dino run 🦖 on a cloud GPU. The observation will be the screenshot of the game. Is there a way for me to pass the observation to the cloud GPU from my local machine? Thanks in advance submitted by /u/DM9667 [link] [comments]  ( 55 min )
    Reinforcement Learning for infinite time problem
    Hello, I am interested in using RL for a problem (Energy Management) with infinite running time. I have seen RL examples in Matlab where it learns to run an episode. Can we amend it to learn to optimize for infinite time like the operation of the power system? submitted by /u/AIinPowerEnthusiast [link] [comments]  ( 58 min )
  • Open

    These 6 NVIDIA Jetson Users Win Big at CES in Las Vegas
    Six companies with innovative products built using the NVIDIA Jetson edge AI platform will leave CES, one of the world’s largest consumer technology trade shows, as big winners next week. The CES Innovation Awards each year honor outstanding design and engineering in more than two dozen categories of consumer technology products. The companies to be Read article >  ( 5 min )
  • Open

    A dozen magic square posts
    Chess-related A knight’s tour magic square A king’s tour magic square Language-related Alphamagic squares in English Alphamagic squares in French Alphamagic squares in Spanish Planet-related Mars Jupyter More mathematical Magic square of squares Magic square of primes Magic squares as matrices Magical permutations Greco-Latin squares and magic squares A dozen magic square posts first appeared on John D. Cook.  ( 4 min )
  • Open

    CARE: Certifiably Robust Learning with Reasoning via Variational Inference. (arXiv:2209.05055v2 [cs.LG] UPDATED)
    Despite great recent advances achieved by deep neural networks (DNNs), they are often vulnerable to adversarial attacks. Intensive research efforts have been made to improve the robustness of DNNs; however, most empirical defenses can be adaptively attacked again, and the theoretically certified robustness is limited, especially on large-scale datasets. One potential root cause of such vulnerabilities for DNNs is that although they have demonstrated powerful expressiveness, they lack the reasoning ability to make robust and reliable predictions. In this paper, we aim to integrate domain knowledge to enable robust learning with the reasoning paradigm. In particular, we propose a certifiably robust learning with reasoning pipeline (CARE), which consists of a learning component and a reasoning component. Concretely, we use a set of standard DNNs to serve as the learning component to make semantic predictions, and we leverage the probabilistic graphical models, such as Markov logic networks (MLN), to serve as the reasoning component to enable knowledge/logic reasoning. However, it is known that the exact inference of MLN (reasoning) is #P-complete, which limits the scalability of the pipeline. To this end, we propose to approximate the MLN inference via variational inference based on an efficient expectation maximization algorithm. In particular, we leverage graph convolutional networks (GCNs) to encode the posterior distribution during variational inference and update the parameters of GCNs (E-step) and the weights of knowledge rules in MLN (M-step) iteratively. We conduct extensive experiments on different datasets and show that CARE achieves significantly higher certified robustness compared with the state-of-the-art baselines. We additionally conducted different ablation studies to demonstrate the empirical robustness of CARE and the effectiveness of different knowledge integration.  ( 2 min )
    Reconnoitering the class distinguishing abilities of the features, to know them better. (arXiv:2211.12771v2 [cs.LG] UPDATED)
    The relevance of machine learning (ML) in our daily lives is closely intertwined with its explainability. Explainability can allow end-users to have a transparent and humane reckoning of a ML scheme's capability and utility. It will also foster the user's confidence in the automated decisions of a system. Explaining the variables or features to explain a model's decision is a need of the present times. We could not really find any work, which explains the features on the basis of their class-distinguishing abilities (specially when the real world data are mostly of multi-class nature). In any given dataset, a feature is not equally good at making distinctions between the different possible categorizations (or classes) of the data points. In this work, we explain the features on the basis of their class or category-distinguishing capabilities. We particularly estimate the class-distinguishing capabilities (scores) of the variables for pair-wise class combinations. We validate the explainability given by our scheme empirically on several real-world, multi-class datasets. We further utilize the class-distinguishing scores in a latent feature context and propose a novel decision making protocol. Another novelty of this work lies with a \emph{refuse to render decision} option when the latent variable (of the test point) has a high class-distinguishing potential for the likely classes.  ( 2 min )
    Causal Graph Recovery for Sepsis-Associated Derangements via Interpretable Hawkes Networks. (arXiv:2106.02600v2 [cs.LG] UPDATED)
    Continuous, automated surveillance systems that incorporate machine learning models are becoming increasingly common in healthcare environments. These models can capture temporally dependent changes across multiple patient variables and can enhance a clinician's situational awareness by providing an early warning alarm of an impending adverse event such as sepsis. However, most commonly used methods, e.g., XGBoost, fail to provide an interpretable mechanism for understanding why a model produced a sepsis alarm at a given time. The ``black box'' nature of many models is a severe limitation as it prevents clinicians from independently corroborating those physiologic features that have contributed to the sepsis alarm. To overcome this limitation, we propose a generalized linear model (GLM) approach to fit a Granger causal graph based on the physiology of several major sepsis-associated derangements (SADs). We adopt a recently developed stochastic monotone variational inequality (VI)-based estimator coupled with forwarding feature selection to learn the graph structure from both continuous and discrete-valued as well as regularly and irregularly sampled time series. Theoretically, we develop a non-asymptotic upper bound on the estimation error for any monotone link function in the GLM. Using synthetic and real-data examples, we demonstrate that the proposed method enjoys result interpretability while achieving comparable performance to popular methods such as XGBoost.  ( 2 min )
    Dataset Distillation for Medical Dataset Sharing. (arXiv:2209.14603v4 [cs.CR] UPDATED)
    Sharing medical datasets between hospitals is challenging because of the privacy-protection problem and the massive cost of transmitting and storing many high-resolution medical images. However, dataset distillation can synthesize a small dataset such that models trained on it achieve comparable performance with the original large dataset, which shows potential for solving the existing medical sharing problems. Hence, this paper proposes a novel dataset distillation-based method for medical dataset sharing. Experimental results on a COVID-19 chest X-ray image dataset show that our method can achieve high detection performance even using scarce anonymized chest X-ray images.  ( 2 min )
    SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning. (arXiv:2210.00611v2 [cs.LG] UPDATED)
    To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as in conventional federated learning. However, due to the more complicated inter-outer problem structure in federated min-max learning, theoretical understandings of communication complexity for federated min-max learning with infrequent communications remain very limited in the literature. This is particularly true for settings with non-i.i.d. datasets and partial client participation. To address this challenge, in this paper, we propose a new algorithmic framework called stochastic sampling averaging gradient descent ascent (SAGDA), which i) assembles stochastic gradient estimators from randomly sampled clients as control variates and ii) leverages two learning rates on both server and client sides. We show that SAGDA achieves a linear speedup in terms of both the number of clients and local update steps, which yields an $\mathcal{O}(\epsilon^{-2})$ communication complexity that is orders of magnitude lower than the state of the art. Interestingly, by noting that the standard federated stochastic gradient descent ascent (FSGDA) is in fact a control-variate-free special version of SAGDA, we immediately arrive at an $\mathcal{O}(\epsilon^{-2})$ communication complexity result for FSGDA. Therefore, through the lens of SAGDA, we also advance the current understanding on communication complexity of the standard FSGDA method for federated min-max learning.  ( 2 min )
    Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning. (arXiv:2210.00690v2 [cs.LG] UPDATED)
    A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in stochastic first-order information has a finite variance. Although this assumption covers all light-tailed (i.e., sub-exponential) and some heavy-tailed noise distributions (e.g., log-normal, Weibull, and some Pareto distributions), it fails for many fat-tailed noise distributions (i.e., ``heavier-tailed'' with potentially infinite variance) that have been empirically observed in the FL literature. To date, it remains unclear whether one can design convergent algorithms for FL systems that experience fat-tailed noise. This motivates us to fill this gap in this paper by proposing an algorithmic framework called FAT-Clipping (\ul{f}ederated \ul{a}veraging with \ul{t}wo-sided learning rates and \ul{clipping}), which contains two variants: FAT-Clipping per-round (FAT-Clipping-PR) and FAT-Clipping per-iteration (FAT-Clipping-PI). Specifically, for the largest $\alpha \in (1,2]$ such that the fat-tailed noise in FL still has a bounded $\alpha$-moment, we show that both variants achieve $\mathcal{O}((mT)^{\frac{2-\alpha}{\alpha}})$ and $\mathcal{O}((mT)^{\frac{1-\alpha}{3\alpha-2}})$ convergence rates in the strongly-convex and general non-convex settings, respectively, where $m$ and $T$ are the numbers of clients and communication rounds. Moreover, at the expense of more clipping operations compared to FAT-Clipping-PR, FAT-Clipping-PI further enjoys a linear speedup effect with respect to the number of local updates at each client and being lower-bound-matching (i.e., order-optimal). Collectively, our results advance the understanding of designing efficient algorithms for FL systems that exhibit fat-tailed first-order oracle information.  ( 2 min )
    Learning Social Navigation from Demonstrations with Conditional Neural Processes. (arXiv:2210.03582v2 [cs.RO] UPDATED)
    Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort.  ( 2 min )
    Why neural networks find simple solutions: the many regularizers of geometric complexity. (arXiv:2209.13083v2 [cs.LG] UPDATED)
    In many contexts, simpler models are preferable to more complex models and the control of this model complexity is the goal for many methods in machine learning such as regularization, hyperparameter tuning and architecture design. In deep learning, it has been difficult to understand the underlying mechanisms of complexity control, since many traditional measures are not naturally suitable for deep neural networks. Here we develop the notion of geometric complexity, which is a measure of the variability of the model function, computed using a discrete Dirichlet energy. Using a combination of theoretical arguments and empirical results, we show that many common training heuristics such as parameter norm regularization, spectral norm regularization, flatness regularization, implicit gradient regularization, noise regularization and the choice of parameter initialization all act to control geometric complexity, providing a unifying framework in which to characterize the behavior of deep learning models.  ( 2 min )
    Activation Learning by Local Competitions. (arXiv:2209.13400v2 [cs.NE] UPDATED)
    Despite its great success, backpropagation has certain limitations that necessitate the investigation of new learning methods. In this study, we present a biologically plausible local learning rule that improves upon Hebb's well-known proposal and discovers unsupervised features by local competitions among neurons. This simple learning rule enables the creation of a forward learning paradigm called activation learning, in which the output activation (sum of the squared output) of the neural network estimates the likelihood of the input patterns, or "learn more, activate more" in simpler terms. For classification on a few small classical datasets, activation learning performs comparably to backpropagation using a fully connected network, and outperforms backpropagation when there are fewer training samples or unpredictable disturbances. Additionally, the same trained network can be used for a variety of tasks, including image generation and completion. Activation learning also achieves state-of-the-art performance on several real-world datasets for anomaly detection. This new learning paradigm, which has the potential to unify supervised, unsupervised, and semi-supervised learning and is reasonably more resistant to adversarial attacks, deserves in-depth investigation.  ( 2 min )
    PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics. (arXiv:2211.00454v2 [hep-ph] UPDATED)
    Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools. In this work, we present a machine learning architecture that uses a set of inputs maximally reduced with respect to the full 6-dimensional Lorentz symmetry, and is fully permutation-equivariant throughout. We study the application of this network architecture to the standard task of top quark tagging and show that the resulting network outperforms all existing competitors despite much lower model complexity. In addition, we present a Lorentz-covariant variant of the same network applied to a 4-momentum regression task.  ( 2 min )
    Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization. (arXiv:2210.15393v2 [math.OC] UPDATED)
    Decision-making problems are commonly formulated as optimization problems, which are then solved to make optimal decisions. In this work, we consider the inverse problem where we use prior decision data to uncover the underlying decision-making process in the form of a mathematical optimization model. This statistical learning problem is referred to as data-driven inverse optimization. We focus on problems where the underlying decision-making process is modeled as a convex optimization problem whose parameters are unknown. We formulate the inverse optimization problem as a bilevel program and propose an efficient block coordinate descent-based algorithm to solve large problem instances. Numerical experiments on synthetic datasets demonstrate the computational advantage of our method compared to standard commercial solvers. Moreover, the real-world utility of the proposed approach is highlighted through two realistic case studies in which we consider estimating risk preferences and learning local constraint parameters of agents in a multiplayer Nash bargaining game.  ( 2 min )
    A Generalized EigenGame with Extensions to Multiview Representation Learning. (arXiv:2211.11323v2 [cs.LG] UPDATED)
    Generalized Eigenvalue Problems (GEPs) encompass a range of interesting dimensionality reduction methods. Development of efficient stochastic approaches to these problems would allow them to scale to larger datasets. Canonical Correlation Analysis (CCA) is one example of a GEP for dimensionality reduction which has found extensive use in problems with two or more views of the data. Deep learning extensions of CCA require large mini-batch sizes, and therefore large memory consumption, in the stochastic setting to achieve good performance and this has limited its application in practice. Inspired by the Generalized Hebbian Algorithm, we develop an approach to solving stochastic GEPs in which all constraints are softly enforced by Lagrange multipliers. Then by considering the integral of this Lagrangian function, its pseudo-utility, and inspired by recent formulations of Principal Components Analysis and GEPs as games with differentiable utilities, we develop a game-theory inspired approach to solving GEPs. We show that our approaches share much of the theoretical grounding of the previous Hebbian and game theoretic approaches for the linear case but our method permits extension to general function approximators like neural networks for certain GEPs for dimensionality reduction including CCA which means our method can be used for deep multiview representation learning. We demonstrate the effectiveness of our method for solving GEPs in the stochastic setting using canonical multiview datasets and demonstrate state-of-the-art performance for optimizing Deep CCA.  ( 2 min )
    Annealing Optimization for Progressive Learning with Stochastic Approximation. (arXiv:2209.02826v2 [eess.SY] UPDATED)
    In this work, we introduce a learning model designed to meet the needs of applications in which computational resources are limited, and robustness and interpretability are prioritized. Learning problems can be formulated as constrained stochastic optimization problems, with the constraints originating mainly from model assumptions that define a trade-off between complexity and performance. This trade-off is closely related to over-fitting, generalization capacity, and robustness to noise and adversarial attacks, and depends on both the structure and complexity of the model, as well as the properties of the optimization methods used. We develop an online prototype-based learning algorithm based on annealing optimization that is formulated as an online gradient-free stochastic approximation algorithm. The learning model can be viewed as an interpretable and progressively growing competitive-learning neural network model to be used for supervised, unsupervised, and reinforcement learning. The annealing nature of the algorithm contributes to minimal hyper-parameter tuning requirements, poor local minima prevention, and robustness with respect to the initial conditions. At the same time, it provides online control over the performance-complexity trade-off by progressively increasing the complexity of the learning model as needed, through an intuitive bifurcation phenomenon. Finally, the use of stochastic approximation enables the study of the convergence of the learning algorithm through mathematical tools from dynamical systems and control, and allows for its integration with reinforcement learning algorithms, constructing an adaptive state-action aggregation scheme.  ( 2 min )
    Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity. (arXiv:2208.05767v3 [cs.LG] UPDATED)
    This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration. Due to uncertainties and variabilities of the environment, it is critical to learn a robust policy -- with as few samples as possible -- that performs well even when the deployed environment deviates from the nominal one used to collect the history dataset. We consider a distributionally robust formulation of offline RL, focusing on tabular robust Markov decision processes with an uncertainty set specified by the Kullback-Leibler divergence in both finite-horizon and infinite-horizon settings. To combat with sample scarcity, a model-based algorithm that combines distributionally robust value iteration with the principle of pessimism in the face of uncertainty is proposed, by penalizing the robust value estimates with a carefully designed data-driven penalty term. Under a mild and tailored assumption of the history dataset that measures distribution shift without requiring full coverage of the state-action space, we establish the finite-sample complexity of the proposed algorithm, and further show it is almost unimprovable in light of a nearly-matching information-theoretic lower bound up to a polynomial factor of the (effective) horizon length. To the best our knowledge, this provides the first provably near-optimal robust offline RL algorithm that learns under model uncertainty and partial coverage.  ( 2 min )
    DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning. (arXiv:2210.04389v2 [stat.ML] UPDATED)
    Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness. To reduce bias for estimating Natural Direct and Indirect Effects in mediation analysis, we propose a new method called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. We obtain novel theoretical results that our DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support our findings and also expose the gap between theory and practice. As a proof of concept, we apply DeepMed to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.  ( 2 min )
    Faster Randomized Methods for Orthogonality Constrained Problems. (arXiv:2106.12060v1 [math.NA] CROSS LISTED)
    Recent literature has advocated the use of randomized methods for accelerating the solution of various matrix problems arising throughout data science and computational science. One popular strategy for leveraging randomization is to use it as a way to reduce problem size. However, methods based on this strategy lack sufficient accuracy for some applications. Randomized preconditioning is another approach for leveraging randomization, which provides higher accuracy. The main challenge in using randomized preconditioning is the need for an underlying iterative method, thus randomized preconditioning so far have been applied almost exclusively to solving regression problems and linear systems. In this article, we show how to expand the application of randomized preconditioning to another important set of problems prevalent across data science: optimization problems with (generalized) orthogonality constraints. We demonstrate our approach, which is based on the framework of Riemannian optimization and Riemannian preconditioning, on the problem of computing the dominant canonical correlations and on the Fisher linear discriminant analysis problem. For both problems, we evaluate the effect of preconditioning on the computational costs and asymptotic convergence, and demonstrate empirically the utility of our approach.  ( 2 min )
    When Do Curricula Work in Federated Learning?. (arXiv:2212.12712v1 [cs.LG])
    An oft-cited open problem of federated learning is the existence of data heterogeneity at the clients. One pathway to understanding the drastic accuracy drop in federated learning is by scrutinizing the behavior of the clients' deep models on data with different levels of "difficulty", which has been left unaddressed. In this paper, we investigate a different and rarely studied dimension of FL: ordered learning. Specifically, we aim to investigate how ordered learning principles can contribute to alleviating the heterogeneity effects in FL. We present theoretical analysis and conduct extensive empirical studies on the efficacy of orderings spanning three kinds of learning: curriculum, anti-curriculum, and random curriculum. We find that curriculum learning largely alleviates non-IIDness. Interestingly, the more disparate the data distributions across clients the more they benefit from ordered learning. We provide analysis explaining this phenomenon, specifically indicating how curriculum training appears to make the objective landscape progressively less convex, suggesting fast converging iterations at the beginning of the training procedure. We derive quantitative results of convergence for both convex and nonconvex objectives by modeling the curriculum training on federated devices as local SGD with locally biased stochastic gradients. Also, inspired by ordered learning, we propose a novel client selection technique that benefits from the real-world disparity in the clients. Our proposed approach to client selection has a synergic effect when applied together with ordered learning in FL.  ( 2 min )
    Linear Combinatorial Semi-Bandit with Causally Related Rewards. (arXiv:2212.12923v1 [cs.LG])
    In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alternatives that guarantees the optimal collective outcome. Thus, besides individual actions' reward, learning the causal relations is essential to improve the decision-making strategy. To solve the two-fold learning problem described above, we develop the 'combinatorial semi-bandit framework with causally related rewards', where we model the causal relations by a directed graph in a stationary structural equation model. The nodal observation in the graph signal comprises the corresponding base arm's instantaneous reward and an additional term resulting from the causal influences of other base arms' rewards. The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology. To achieve this objective, we propose a policy that determines the causal relations by learning the network's topology and simultaneously exploits this knowledge to optimize the decision-making process. We establish a sublinear regret bound for the proposed algorithm. Numerical experiments using synthetic and real-world datasets demonstrate the superior performance of our proposed method compared to several benchmarks.  ( 2 min )
    GWO-FI: A novel machine learning framework by combining Gray Wolf Optimizer and Frequent Itemsets to diagnose and investigate effective factors on In-Hospital Mortality and Length of Stay among Kermanshahian Cardiovascular Disease patients. (arXiv:2212.13048v1 [cs.LG])
    Investigation and analysis of patient outcomes, including in-hospital mortality and length of stay, are crucial for assisting clinicians in determining a patient's result at the outset of their hospitalization and for assisting hospitals in allocating their resources. This paper proposes an approach based on combining the well-known gray wolf algorithm with frequent items extracted by association rule mining algorithms. First, original features are combined with the discriminative extracted frequent items. The best subset of these features is then chosen, and the parameters of the used classification algorithms are also adjusted, using the gray wolf algorithm. This framework was evaluated using a real dataset made up of 2816 patients from the Imam Ali Kermanshah Hospital in Iran. The study's findings indicate that low Ejection Fraction, old age, high CPK values, and high Creatinine levels are the main contributors to patients' mortality. Several significant and interesting rules related to mortality in hospitals and length of stay have also been extracted and presented. Additionally, the accuracy, sensitivity, specificity, and auroc of the proposed framework for the diagnosis of mortality in the hospital using the SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively. According to the framework's findings, adding frequent items as features considerably improves classification accuracy.  ( 2 min )
    GAE-ISumm: Unsupervised Graph-Based Summarization of Indian Languages. (arXiv:2212.12937v1 [cs.CL])
    Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian languages are often limited by rich morphological variation, syntax, and semantic differences. In this paper, we propose GAE-ISumm, an unsupervised Indic summarization model that extracts summaries from text documents. In particular, our proposed model, GAE-ISumm uses Graph Autoencoder (GAE) to learn text representations and a document summary jointly. We also provide a manually-annotated Telugu summarization dataset TELSUM, to experiment with our model GAE-ISumm. Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISumm on other Indian languages. Our experiments of GAE-ISumm in seven languages make the following observations: (i) it is competitive or better than state-of-the-art results on all datasets, (ii) it reports benchmark results on TELSUM, and (iii) the inclusion of positional and cluster information in the proposed model improved the performance of summaries.  ( 2 min )
    Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System. (arXiv:2212.12744v1 [eess.SP])
    In this paper, we consider an intelligent reflecting surface (IRS)-aided cell-free massive multiple-input multiple-output system, where the beamforming at access points and the phase shifts at IRSs are jointly optimized to maximize energy efficiency (EE). To solve EE maximization problem, we propose an iterative optimization algorithm by using quadratic transform and Lagrangian dual transform to find the optimum beamforming and phase shifts. However, the proposed algorithm suffers from high computational complexity, which hinders its application in some practical scenarios. Responding to this, we further propose a deep learning based approach for joint beamforming and phase shifts design. Specifically, a two-stage deep neural network is trained offline using the unsupervised learning manner, which is then deployed online for the predictions of beamforming and phase shifts. Simulation results show that compared with the iterative optimization algorithm and the genetic algorithm, the unsupervised learning based approach has higher EE performance and lower running time.  ( 2 min )
    QuickNets: Saving Training and Preventing Overconfidence in Early-Exit Neural Architectures. (arXiv:2212.12866v1 [cs.LG])
    Deep neural networks have long training and processing times. Early exits added to neural networks allow the network to make early predictions using intermediate activations in the network in time-sensitive applications. However, early exits increase the training time of the neural networks. We introduce QuickNets: a novel cascaded training algorithm for faster training of neural networks. QuickNets are trained in a layer-wise manner such that each successive layer is only trained on samples that could not be correctly classified by the previous layers. We demonstrate that QuickNets can dynamically distribute learning and have a reduced training cost and inference cost compared to standard Backpropagation. Additionally, we introduce commitment layers that significantly improve the early exits by identifying for over-confident predictions and demonstrate its success.  ( 2 min )
    Understanding the Complexity Gains of Single-Task RL with a Curriculum. (arXiv:2212.12809v1 [cs.LG])
    Reinforcement learning (RL) problems can be challenging without well-shaped rewards. Prior work on provably efficient RL methods generally proposes to address this issue with dedicated exploration strategies. However, another way to tackle this challenge is to reformulate it as a multi-task RL problem, where the task space contains not only the challenging task of interest but also easier tasks that implicitly function as a curriculum. Such a reformulation opens up the possibility of running existing multi-task RL methods as a more efficient alternative to solving a single challenging task from scratch. In this work, we provide a theoretical framework that reformulates a single-task RL problem as a multi-task RL problem defined by a curriculum. Under mild regularity conditions on the curriculum, we show that sequentially solving each task in the multi-task RL problem is more computationally efficient than solving the original single-task problem, without any explicit exploration bonuses or other exploration strategies. We also show that our theoretical insights can be translated into an effective practical learning algorithm that can accelerate curriculum learning on simulated robotic tasks.  ( 2 min )
    Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited. (arXiv:2202.03580v4 [cs.LG] UPDATED)
    Designing spectral convolutional networks is a challenging problem in graph learning. ChebNet, one of the early attempts, approximates the spectral graph convolutions using Chebyshev polynomials. GCN simplifies ChebNet by utilizing only the first two Chebyshev polynomials while still outperforming it on real-world datasets. GPR-GNN and BernNet demonstrate that the Monomial and Bernstein bases also outperform the Chebyshev basis in terms of learning the spectral graph convolutions. Such conclusions are counter-intuitive in the field of approximation theory, where it is established that the Chebyshev polynomial achieves the optimum convergent rate for approximating a function. In this paper, we revisit the problem of approximating the spectral graph convolutions with Chebyshev polynomials. We show that ChebNet's inferior performance is primarily due to illegal coefficients learnt by ChebNet approximating analytic filter functions, which leads to over-fitting. We then propose ChebNetII, a new GNN model based on Chebyshev interpolation, which enhances the original Chebyshev polynomial approximation while reducing the Runge phenomenon. We conducted an extensive experimental study to demonstrate that ChebNetII can learn arbitrary graph convolutions and achieve superior performance in both full- and semi-supervised node classification tasks. Most notably, we scale ChebNetII to a billion graph ogbn-papers100M, showing that spectral-based GNNs have superior performance. Our code is available at https://github.com/ivam-he/ChebNetII.
    How unfair is private learning ?. (arXiv:2206.03985v2 [cs.LG] UPDATED)
    As machine learning algorithms are deployed on sensitive data in critical decision making processes, it is becoming increasingly important that they are also private and fair. In this paper, we show that, when the data has a long-tailed structure, it is not possible to build accurate learning algorithms that are both private and results in higher accuracy on minority subpopulations. We further show that relaxing overall accuracy can lead to good fairness even with strict privacy requirements. To corroborate our theoretical results in practice, we provide an extensive set of experimental results using a variety of synthetic, vision (CIFAR10 and CelebA), and tabular (Law School) datasets and learning algorithms.
    Causal Explanations of Structural Causal Models. (arXiv:2110.02395v3 [cs.LG] UPDATED)
    In explanatory interactive learning (XIL) the user queries the learner, then the learner explains its answer to the user and finally the loop repeats. XIL is attractive for two reasons, (1) the learner becomes better and (2) the user's trust increases. For both reasons to hold, the learner's explanations must be useful to the user and the user must be allowed to ask useful questions. Ideally, both questions and explanations should be grounded in a causal model since they avoid spurious fallacies. Ultimately, we seem to seek a causal variant of XIL. The question part on the user's end we believe to be solved since the user's mental model can provide the causal model. But how would the learner provide causal explanations? In this work we show that existing explanation methods are not guaranteed to be causal even when provided with a Structural Causal Model (SCM). Specifically, we use the popular, proclaimed causal explanation method CXPlain to illustrate how the generated explanations leave open the question of truly causal explanations. Thus as a step towards causal XIL, we propose a solution to the lack of causal explanations. We solve this problem by deriving from first principles an explanation method that makes full use of a given SCM, which we refer to as SC$\textbf{E}$ ($\textbf{E}$ standing for explanation). Since SCEs make use of structural information, any causal graph learner can now provide human-readable explanations. We conduct several experiments including a user study with 22 participants to investigate the virtue of SCE as causal explanations of SCMs.
    Efficient Long-Text Understanding with Short-Text Models. (arXiv:2208.00748v2 [cs.CL] UPDATED)
    Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles and long documents, due to their quadratic complexity. While a myriad of efficient transformer variants have been proposed, they are typically based on custom implementations that require expensive pretraining from scratch.In this work, we propose SLED: SLiding-Encoder and Decoder, a simple approach for processing long sequences that re-uses and leverages battle-tested short-text pretrained LMs. Specifically, we partition the input into overlapping chunks, encode each with a short-text LM encoder and use the pretrained decoder to fuse information across chunks (fusion-in-decoder). We illustrate through controlled experiments that SLED offers a viable strategy for long text understanding and evaluate our approach on SCROLLS, a benchmark with seven datasets across a wide range of language understanding tasks. We find that SLED is competitive with specialized models that are up to 50x larger and require a dedicated and expensive pretraining step.
    Wastewater Pipe Rating Model Using Natural Language Processing. (arXiv:2202.13871v2 [cs.IR] UPDATED)
    Closed-circuit video (CCTV) inspection has been the most popular technique for visually evaluating the interior status of pipelines in recent decades. Certified inspectors prepare the pipe repair document based on the CCTV inspection. The traditional manual method of assessing sewage structural conditions from pipe repair documents takes a long time and is prone to human mistakes. The automatic identification of necessary texts has received little attention. By building an automated framework employing Natural Language Processing (NLP), this study presents an effective technique to automate the identification of the pipe defect rating of the pipe repair documents. NLP technologies are employed to break down textual material into grammatical units in this research. Further analysis entails using words to discover pipe defect symptoms and their frequency and then combining that information into a single score. Our model achieves 95.0% accuracy,94.9% sensitivity, 94.4% specificity, 95.9% precision score, and 95.7% F1 score, showing the potential of the proposed model to be used in large-scale pipe repair documents for accurate and efficient pipeline failure detection to improve the quality of the pipeline. Keywords: Sewer pipe inspection, Defect detection, Natural language processing, Text recognition
    Independent and Decentralized Learning in Markov Potential Games. (arXiv:2205.14590v3 [cs.LG] UPDATED)
    We propose a multi-agent reinforcement learning dynamics, and analyze its convergence properties in infinite-horizon discounted Markov potential games. We focus on the independent and decentralized setting, where players can only observe the realized state and their own reward in every stage. Players do not have knowledge of the game model, and cannot coordinate with each other. In each stage of our learning dynamics, players update their estimate of a perturbed Q-function that evaluates their total contingent payoff based on the realized one-stage reward in an asynchronous manner. Then, players independently update their policies by incorporating a smoothed optimal one-stage deviation strategy based on the estimated Q-function. A key feature of the learning dynamics is that the Q-function estimates are updated at a faster timescale than the policies. We prove that the policies induced by our learning dynamics converge to a stationary Nash equilibrium in Markov potential games with probability 1. Our results demonstrate that agents can reach a stationary Nash equilibrium in Markov potential games through simple learning dynamics under the minimum information environment.
    Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models. (arXiv:2101.02305v2 [cs.LG] UPDATED)
    Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, four different demand forecasting methods, ARIMA (Auto Regressive Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator) and LSTM (Long Short-Term Memory) networks are utilized and evaluated. We use a large clinical dataset for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily platelet transfusions along with information such as the product specifications, the recipients' characteristics, and the recipients' laboratory test results. This study is the first to utilize different methods from statistical time series models to data-driven regression and a machine learning technique for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariate approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach such as ARIMA appears to be sufficient. We also comment on the approach to choose clinical indicators (inputs) for the multivariate models.
    Domain-invariant Feature Exploration for Domain Generalization. (arXiv:2207.12020v2 [cs.LG] UPDATED)
    Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (DG) enables a model to generalize to an unseen test distribution, i.e., to learn domain-invariant representations. In this paper, we argue that domain-invariant features should be originating from both internal and mutual sides. Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i.e., the property within a domain, which is agnostic to other domains. Mutual invariance means that the features can be learned with multiple domains (cross-domain) and the features contain common information, i.e., the transferable features w.r.t. other domains. We then propose DIFEX for Domain-Invariant Feature EXploration. DIFEX employs a knowledge distillation framework to capture the high-level Fourier phase as the internally-invariant features and learn cross-domain correlation alignment as the mutually-invariant features. We further design an exploration loss to increase the feature diversity for better generalization. Extensive experiments on both time-series and visual benchmarks demonstrate that the proposed DIFEX achieves state-of-the-art performance.
    Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations. (arXiv:2205.09332v5 [cs.LG] UPDATED)
    We present a new technique for the accelerated training of physics-informed neural networks (PINNs): discretely-trained PINNs (DT-PINNs). The repeated computation of partial derivative terms in the PINN loss functions via automatic differentiation during training is known to be computationally expensive, especially for higher-order derivatives. DT-PINNs are trained by replacing these exact spatial derivatives with high-order accurate numerical discretizations computed using meshless radial basis function-finite differences (RBF-FD) and applied via sparse-matrix vector multiplication. The use of RBF-FD allows for DT-PINNs to be trained even on point cloud samples placed on irregular domain geometries. Additionally, though traditional PINNs (vanilla-PINNs) are typically stored and trained in 32-bit floating-point (fp32) on the GPU, we show that for DT-PINNs, using fp64 on the GPU leads to significantly faster training times than fp32 vanilla-PINNs with comparable accuracy. We demonstrate the efficiency and accuracy of DT-PINNs via a series of experiments. First, we explore the effect of network depth on both numerical and automatic differentiation of a neural network with random weights and show that RBF-FD approximations of third-order accuracy and above are more efficient while being sufficiently accurate. We then compare the DT-PINNs to vanilla-PINNs on both linear and nonlinear Poisson equations and show that DT-PINNs achieve similar losses with 2-4x faster training times on a consumer GPU. Finally, we also demonstrate that similar results can be obtained for the PINN solution to the heat equation (a space-time problem) by discretizing the spatial derivatives using RBF-FD and using automatic differentiation for the temporal derivative. Our results show that fp64 DT-PINNs offer a superior cost-accuracy profile to fp32 vanilla-PINNs.
    An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation. (arXiv:2208.07194v4 [cs.LG] UPDATED)
    Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme that allows buses to receive model updates without waiting for model training on the cloud. However, FL is vulnerable to poisoning or DDoS attacks since buses travel in public. Some work introduces blockchain to improve reliability, but the additional latency from the consensus process reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models. To address the above challenges, this paper offers a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor (DBAFL). Specifically, the novel committee-based consensus algorithm for blockchain improves reliability at the lowest possible cost of time. Meanwhile, the devised dynamic scaling factor allows AFL to assign reasonable weights to stale local models. Extensive experiments conducted on heterogeneous devices validate outperformed learning performance, efficiency, and reliability of DBAFL.
    Learning from Heterogeneous Data Based on Social Interactions over Graphs. (arXiv:2112.09483v2 [cs.LG] UPDATED)
    This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning, several useful strategies have been developed, which solve decision making problems through local cooperation across distributed agents and allow them to learn from streaming data. However, traditional social learning strategies rely on the fundamental assumption that each agent has significant prior knowledge of the underlying distribution of the observations. In this work we overcome this issue by introducing a machine learning framework that exploits social interactions over a graph, leading to a fully data-driven solution to the distributed classification problem. In the proposed social machine learning (SML) strategy, two phases are present: in the training phase, classifiers are independently trained to generate a belief over a set of hypotheses using a finite number of training samples; in the prediction phase, classifiers evaluate streaming unlabeled observations and share their instantaneous beliefs with neighboring classifiers. We show that the SML strategy enables the agents to learn consistently under this highly-heterogeneous setting and allows the network to continue learning even during the prediction phase when it is deciding on unlabeled samples. The prediction decisions are used to continually improve performance thereafter in a manner that is markedly different from most existing static classification schemes where, following training, the decisions on unlabeled data are not re-used to improve future performance.
    Indeterminacy and Strong Identifiability in Generative Models. (arXiv:2206.00801v3 [stat.ML] UPDATED)
    Most modern probabilistic generative models, such as the variational autoencoder (VAE), have certain indeterminacies that are unresolvable even with an infinite amount of data. Different tasks tolerate different indeterminacies, however recent applications have indicated the need for strongly identifiable models, in which an observation corresponds to a unique latent code. Progress has been made towards reducing model indeterminacies while maintaining flexibility, and recent work excludes many--but not all--indeterminacies. In this work, we motivate model-identifiability in terms of task-identifiability, then construct a theoretical framework for analyzing the indeterminacies of latent variable models, which enables their precise characterization in terms of the generator function and prior distribution spaces. We reveal that strong identifiability is possible even with highly flexible nonlinear generators, and give two such examples. One is a straightforward modification of iVAE (arXiv:1907.04809 [stat.ML]); the other uses triangular monotonic maps, leading to novel connections between optimal transport and identifiability.
    ModelPred: A Framework for Predicting Trained Model from Training Data. (arXiv:2111.12545v4 [cs.LG] UPDATED)
    In this work, we propose ModelPred, a framework that helps to understand the impact of changes in training data on a trained model. This is critical for building trust in various stages of a machine learning pipeline: from cleaning poor-quality samples and tracking important ones to be collected during data preparation, to calibrating uncertainty of model prediction, to interpreting why certain behaviors of a model emerge during deployment. Specifically, ModelPred learns a parameterized function that takes a dataset $S$ as the input and predicts the model obtained by training on $S$. Our work differs from the recent work of Datamodels [1] as we aim for predicting the trained model parameters directly instead of the trained model behaviors. We demonstrate that a neural network-based set function class is capable of learning the complex relationships between the training data and model parameters. We introduce novel global and local regularization techniques to prevent overfitting and we rigorously characterize the expressive power of neural networks (NN) in approximating the end-to-end training process. Through extensive empirical investigations, we show that ModelPred enables a variety of applications that boost the interpretability and accountability of machine learning (ML), such as data valuation, data selection, memorization quantification, and model calibration.
    Learning k-Level Sparse Neural Networks Using a New Generalized Group Sparse Envelope Regularization. (arXiv:2212.12921v1 [cs.LG])
    We propose an efficient method to learn both unstructured and structured sparse neural networks during training, using a novel generalization of the sparse envelope function (SEF) used as a regularizer, termed {\itshape{group sparse envelope function}} (GSEF). The GSEF acts as a neuron group selector, which we leverage to induce structured pruning. Our method receives a hardware-friendly structured sparsity of a deep neural network (DNN) to efficiently accelerate the DNN's evaluation. This method is flexible in the sense that it allows any hardware to dictate the definition of a group, such as a filter, channel, filter shape, layer depth, a single parameter (unstructured), etc. By the nature of the GSEF, the proposed method is the first to make possible a pre-define sparsity level that is being achieved at the training convergence, while maintaining negligible network accuracy degradation. We propose an efficient method to calculate the exact value of the GSEF along with its proximal operator, in a worst-case complexity of $O(n)$, where $n$ is the total number of groups variables. In addition, we propose a proximal-gradient-based optimization method to train the model, that is, the non-convex minimization of the sum of the neural network loss and the GSEF. Finally, we conduct an experiment and illustrate the efficiency of our proposed technique in terms of the completion ratio, accuracy, and inference latency.
    Gaussian Pre-Activations in Neural Networks: Myth or Reality?. (arXiv:2205.12379v2 [cs.LG] UPDATED)
    The study of feature propagation at initialization in neural networks lies at the root of numerous initialization designs. An assumption very commonly made in the field states that the pre-activations are Gaussian. Although this convenient Gaussian hypothesis can be justified when the number of neurons per layer tends to infinity, it is challenged by both theoretical and experimental works for finite-width neural networks. Our major contribution is to construct a family of pairs of activation functions and initialization distributions that ensure that the pre-activations remain Gaussian throughout the network's depth, even in narrow neural networks. In the process, we discover a set of constraints that a neural network should fulfill to ensure Gaussian pre-activations. Additionally, we provide a critical review of the claims of the Edge of Chaos line of works and build an exact Edge of Chaos analysis. We also propose a unified view on pre-activations propagation, encompassing the framework of several well-known initialization procedures. Finally, our work provides a principled framework for answering the much-debated question: is it desirable to initialize the training of a neural network whose pre-activations are ensured to be Gaussian?
    FedEval: A Holistic Evaluation Framework for Federated Learning. (arXiv:2011.09655v3 [cs.LG] UPDATED)
    Federated Learning (FL) has been widely accepted as the solution for privacy-preserving machine learning without collecting raw data. While new technologies proposed in the past few years do evolve the FL area, unfortunately, the evaluation results presented in these works fall short in integrity and are hardly comparable because of the inconsistent evaluation metrics and experimental settings. In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms. Specifically, we first introduce the core evaluation taxonomy model, called FedEval-Core, which covers four essential evaluation aspects for FL: Privacy, Robustness, Effectiveness, and Efficiency, with various well-defined metrics and experimental settings. Based on the FedEval-Core, we further develop an FL evaluation platform with standardized evaluation settings and easy-to-use interfaces. We then provide an in-depth benchmarking study between the seven well-known FL algorithms, including FedSGD, FedAvg, FedProx, FedOpt, FedSTC, SecAgg, and HEAgg. We comprehensively analyze the advantages and disadvantages of these algorithms and further identify the suitable practical scenarios for different algorithms, which is rarely done by prior work. Lastly, we excavate a set of take-away insights and future research directions, which are very helpful for researchers in the FL area.
    Compositional optimization of quantum circuits for quantum kernels of support vector machines. (arXiv:2203.13848v2 [quant-ph] UPDATED)
    While quantum machine learning (ML) has been proposed to be one of the most promising applications of quantum computing, how to build quantum ML models that outperform classical ML remains a major open question. Here, we demonstrate a Bayesian algorithm for constructing quantum kernels for support vector machines that adapts quantum gate sequences to data. The algorithm increases the complexity of quantum circuits incrementally by appending quantum gates selected with Bayesian information criterion as circuit selection metric and Bayesian optimization of the parameters of the locally optimal quantum circuits identified. The performance of the resulting quantum models for classification problems with a small number of training points significantly exceeds that of optimized classical models with conventional kernels.
    Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets. (arXiv:2208.04322v2 [cs.LG] UPDATED)
    We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets. The problem is formalized as a non-cooperative Markov game over the training rounds. A multi-agent hybrid deep reinforcement learning-based algorithm is proposed to optimize the joint client selection and payment actions, while avoiding action conflicts. Simulation results indicate that our proposed algorithm can significantly improve training performance.
    MC-Nonlocal-PINNs: handling nonlocal operators in PINNs via Monte Carlo sampling. (arXiv:2212.12984v1 [math.NA])
    We propose, Monte Carlo Nonlocal physics-informed neural networks (MC-Nonlocal-PINNs), which is a generalization of MC-fPINNs in \cite{guo2022monte}, for solving general nonlocal models such as integral equations and nonlocal PDEs. Similar as in MC-fPINNs, our MC-Nonlocal-PINNs handle the nonlocal operators in a Monte Carlo way, resulting in a very stable approach for high dimensional problems. We present a variety of test problems, including high dimensional Volterra type integral equations, hypersingular integral equations and nonlocal PDEs, to demonstrate the effectiveness of our approach.
    Robust computation of optimal transport by $\beta$-potential regularization. (arXiv:2212.13251v1 [cs.LG])
    Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions. For instance, OT is a popular loss function that quantifies the discrepancy between an empirical distribution and a parametric model. Recently, an entropic penalty term and the celebrated Sinkhorn algorithm have been commonly used to approximate the original OT in a computationally efficient way. However, since the Sinkhorn algorithm runs a projection associated with the Kullback-Leibler divergence, it is often vulnerable to outliers. To overcome this problem, we propose regularizing OT with the \beta-potential term associated with the so-called $\beta$-divergence, which was developed in robust statistics. Our theoretical analysis reveals that the $\beta$-potential can prevent the mass from being transported to outliers. We experimentally demonstrate that the transport matrix computed with our algorithm helps estimate a probability distribution robustly even in the presence of outliers. In addition, our proposed method can successfully detect outliers from a contaminated dataset
    Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders. (arXiv:2212.13067v1 [cs.LG])
    Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often require a large number of labeled examples, yet obtaining the label information can be very expensive given the high time and cost required by quality inspections. In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query. However, most of the active learning strategies proposed for regression focus on the offline setting. In this work, we adapt some of these approaches to the stream-based scenario and show how they can be used to select the most informative data points. We also demonstrate how to use a semi-supervised architecture based on orthogonal autoencoders to learn salient features in a lower dimensional space. The Tennessee Eastman Process is used to compare the predictive performance of the proposed approaches.
    Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning. (arXiv:2109.03445v3 [stat.ML] UPDATED)
    The stochastic approximation (SA) algorithm is a widely used probabilistic method for finding a zero or a fixed point of a vector-valued funtion, when only noisy measurements of the function are available. In the literature to date, one makes a distinction between ``synchronous'' updating, whereby every component of the current guess is updated at each time, and ``asynchronous'' updating, whereby only one component is updated. In this paper, we study an intermediate situation that we call ``batch asynchronous stochastic approximation'' (BASA), in which, at each time instant, \textit{some but not all} components of the current estimated solution are updated. BASA allows the user to trade off memory requirements against time complexity. We develop a general methodology for proving that such algorithms converge to the fixed point of the map under study. These convergence proofs make use of weaker hypotheses than existing results. Specifically, existing convergence proofs require that the measurement noise is a zero-mean i.i.d\ sequence or a martingale difference sequence. In the present paper, we permit biased measurements, that is, measurement noises that have nonzero conditional mean. Also, all convergence results to date assume that the stochastic step sizes satisfy a probabilistic analog of the well-known Robbins-Monro conditions. We replace this assumption by a purely deterministic condition on the irreducibility of the underlying Markov processes. As specific applications to Reinforcement Learning, we analyze the temporal difference algorithm $TD(\lambda)$ for value iteration, and the $Q$-learning algorithm for finding the optimal action-value function. In both cases, we establish the convergence of these algorithms, under milder conditions than in the existing literature.
    On Error and Compression Rates for Prototype Rules. (arXiv:2206.08014v2 [cs.LG] UPDATED)
    We study the close interplay between error and compression in the non-parametric multiclass classification setting in terms of prototype learning rules. We focus in particular on a recently proposed compression-based learning rule termed OptiNet (Kontorovich, Sabato, and Urner 2016; Kontorovich, Sabato, and Weiss 2017; Hanneke et al. 2021). Beyond its computational merits, this rule has been recently shown to be universally consistent in any metric instance space that admits a universally consistent rule--the first learning algorithm known to enjoy this property. However, its error and compression rates have been left open. Here we derive such rates in the case where instances reside in Euclidean space under commonly posed smoothness and tail conditions on the data distribution. We first show that OptiNet achieves non-trivial compression rates while enjoying near minimax-optimal error rates. We then proceed to study a novel general compression scheme for further compressing prototype rules that locally adapts to the noise level without sacrificing accuracy. Applying it to OptiNet, we show that under a geometric margin condition, further gain in the compression rate is achieved. Experimental results comparing the performance of the various methods are presented.
    SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning. (arXiv:2206.08901v2 [hep-ph] UPDATED)
    The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calculations in high-energy physics, analytical calculations using machine learning are still in their infancy. In this work, we use a sequence-to-sequence model, specifically, a transformer, to compute a key element of the cross section calculation, namely, the squared amplitude of an interaction. We show that a transformer model is able to predict correctly 97.6% and 99% of squared amplitudes of QCD and QED processes, respectively, at a speed that is up to orders of magnitude faster than current symbolic computation frameworks. We discuss the performance of the current model, its limitations and possible future directions for this work.
    Bias Mitigation Framework for Intersectional Subgroups in Neural Networks. (arXiv:2212.13014v1 [cs.LG])
    We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating one kind of bias by incorporating complex fairness-driven constraints into optimization objectives or designing additional layers that focus on specific protected attributes. We introduce a simple and generic bias mitigation approach that prevents models from learning relationships between protected attributes and output variable by reducing mutual information between them. We demonstrate that our approach is effective in reducing bias with little or no drop in accuracy. We also show that the models trained with our learning framework become causally fair and insensitive to the values of protected attributes. Finally, we validate our approach by studying feature interactions between protected and non-protected attributes. We demonstrate that these interactions are significantly reduced when applying our bias mitigation.
    Toward Efficient Automated Feature Engineering. (arXiv:2212.13152v1 [cs.LG])
    Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation \com{and} selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy. We improve the efficiency of AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation (FPE) Model to reduce the sample size and feature size that are two main factors on undermining the efficiency of feature evaluation. On the other hand, we devise a two-stage policy training strategy by running FPE on the pre-evaluation task as the initialization of the policy to avoid training policy from scratch. We conduct comprehensive experiments on 36 datasets in terms of both classification and regression tasks. The results show $2.9\%$ higher performance in average and 2x higher computational efficiency comparing to state-of-the-art AFE methods.
    Can Foundation Models Wrangle Your Data?. (arXiv:2205.09911v2 [cs.LG] UPDATED)
    Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations continue to push the boundaries of what these models can do on language and image tasks. This paper aims to understand an underexplored area of FMs: classical data tasks like cleaning and integration. As a proof-of-concept, we cast five data cleaning and integration tasks as prompting tasks and evaluate the performance of FMs on these tasks. We find that large FMs generalize and achieve SoTA performance on data cleaning and integration tasks, even though they are not trained for these data tasks. We identify specific research challenges and opportunities that these models present, including challenges with private and domain specific data, and opportunities to make data management systems more accessible to non-experts. We make our code and experiments publicly available at: https://github.com/HazyResearch/fm_data_tasks.
    Skit-S2I: An Indian Accented Speech to Intent dataset. (arXiv:2212.13015v1 [cs.CL])
    Conventional conversation assistants extract text transcripts from the speech signal using automatic speech recognition (ASR) and then predict intent from the transcriptions. Using end-to-end spoken language understanding (SLU), the intents of the speaker are predicted directly from the speech signal without requiring intermediate text transcripts. As a result, the model can optimize directly for intent classification and avoid cascading errors from ASR. The end-to-end SLU system also helps in reducing the latency of the intent prediction model. Although many datasets are available publicly for text-to-intent tasks, the availability of labeled speech-to-intent datasets is limited, and there are no datasets available in the Indian accent. In this paper, we release the Skit-S2I dataset, the first publicly available Indian-accented SLU dataset in the banking domain in a conversational tonality. We experiment with multiple baselines, compare different pretrained speech encoder's representations, and find that SSL pretrained representations perform slightly better than ASR pretrained representations lacking prosodic features for speech-to-intent classification. The dataset and baseline code is available at \url{https://github.com/skit-ai/speech-to-intent-dataset}
    A Universal Law of Robustness via Isoperimetry. (arXiv:2105.12806v4 [cs.LG] UPDATED)
    Classically, data interpolation with a parametrized model class is possible as long as the number of parameters is larger than the number of equations to be satisfied. A puzzling phenomenon in deep learning is that models are trained with many more parameters than what this classical theory would suggest. We propose a partial theoretical explanation for this phenomenon. We prove that for a broad class of data distributions and model classes, overparametrization is necessary if one wants to interpolate the data smoothly. Namely we show that smooth interpolation requires $d$ times more parameters than mere interpolation, where $d$ is the ambient data dimension. We prove this universal law of robustness for any smoothly parametrized function class with polynomial size weights, and any covariate distribution verifying isoperimetry. In the case of two-layers neural networks and Gaussian covariates, this law was conjectured in prior work by Bubeck, Li and Nagaraj. We also give an interpretation of our result as an improved generalization bound for model classes consisting of smooth functions.
    Policy Learning with Competing Agents. (arXiv:2204.01884v2 [stat.ML] UPDATED)
    Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to such policies, competition arises, complicating the estimation of the effect of the policy. In this paper, we study capacity-constrained treatment assignment in the presence of such interference. We consider a dynamic model where the decision maker allocates treatments at each time step and heterogeneous agents myopically best respond to the previous treatment assignment policy. When the number of agents is large but finite, we show that the threshold for receiving treatment under a given policy converges to the policy's mean-field equilibrium threshold. Based on this result, we develop a consistent estimator for the policy effect. In simulations and a semi-synthetic experiment with data from the National Education Longitudinal Study of 1988, we demonstrate that this estimator can be used for learning capacity-constrained policies in the presence of strategic behavior.
    Deep Reinforcement Learning for Heat Pump Control. (arXiv:2212.12716v1 [cs.LG])
    Heating in private households is a major contributor to the emissions generated today. Heat pumps are a promising alternative for heat generation and are a key technology in achieving our goals of the German energy transformation and to become less dependent on fossil fuels. Today, the majority of heat pumps in the field are controlled by a simple heating curve, which is a naive mapping of the current outdoor temperature to a control action. A more advanced control approach is model predictive control (MPC) which was applied in multiple research works to heat pump control. However, MPC is heavily dependent on the building model, which has several disadvantages. Motivated by this and by recent breakthroughs in the field, this work applies deep reinforcement learning (DRL) to heat pump control in a simulated environment. Through a comparison to MPC, it could be shown that it is possible to apply DRL in a model-free manner to achieve MPC-like performance. This work extends other works which have already applied DRL to building heating operation by performing an in-depth analysis of the learned control strategies and by giving a detailed comparison of the two state-of-the-art control methods.
    POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems. (arXiv:2106.13867v5 [eess.SY] UPDATED)
    We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs). Existing approaches that leverage the standard Taylor Model (TM) arithmetic for approximating the neural-network controller cannot deal with non-differentiable activation functions and suffer from rapid explosion of the remainder when propagating the TMs. POLAR overcomes these shortcomings by integrating TM arithmetic with \textbf{Bernstein B{\'e}zier Form} and \textbf{symbolic remainder}. The former enables TM propagation across non-differentiable activation functions and local refinement of TMs, and the latter reduces error accumulation in the TM remainder for linear mappings in the network. Experimental results show that POLAR significantly outperforms the current state-of-the-art tools in terms of both efficiency and tightness of the reachable set overapproximation. The source code can be found in https://github.com/ChaoHuang2018/POLAR_Tool
    Data Redaction from Pre-trained GANs. (arXiv:2206.14389v2 [cs.LG] UPDATED)
    Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness. The common way to mitigate this is to re-train them differently from scratch using different data or different regularization -- which uses a lot of computational resources and does not always fully address the problem. In this work, we take a different, more compute-friendly approach and investigate how to post-edit a model after training so that it ''redacts'', or refrains from outputting certain kinds of samples. We show that redaction is a fundamentally different task from data deletion, and data deletion may not always lead to redaction. We then consider Generative Adversarial Networks (GANs), and provide three different algorithms for data redaction that differ on how the samples to be redacted are described. Extensive evaluations on real-world image datasets show that our algorithms out-perform data deletion baselines, and are capable of redacting data while retaining high generation quality at a fraction of the cost of full re-training.
    Visualizing Information Bottleneck through Variational Inference. (arXiv:2212.12667v1 [cs.LG])
    The Information Bottleneck theory provides a theoretical and computational framework for finding approximate minimum sufficient statistics. Analysis of the Stochastic Gradient Descent (SGD) training of a neural network on a toy problem has shown the existence of two phases, fitting and compression. In this work, we analyze the SGD training process of a Deep Neural Network on MNIST classification and confirm the existence of two phases of SGD training. We also propose a setup for estimating the mutual information for a Deep Neural Network through Variational Inference.
    Sitting Posture Recognition Using a Spiking Neural Network. (arXiv:2212.12908v1 [eess.SP])
    To increase the quality of citizens' lives, we designed a personalized smart chair system to recognize sitting behaviors. The system can receive surface pressure data from the designed sensor and provide feedback for guiding the user towards proper sitting postures. We used a liquid state machine and a logistic regression classifier to construct a spiking neural network for classifying 15 sitting postures. To allow this system to read our pressure data into the spiking neurons, we designed an algorithm to encode map-like data into cosine-rank sparsity data. The experimental results consisting of 15 sitting postures from 19 participants show that the prediction precision of our SNN is 88.52%.
    Packing Privacy Budget Efficiently. (arXiv:2212.13228v1 [cs.CR])
    Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can be regarded as a new type of compute resource in workloads of multiple ML models training on user data. Once it is used, the DP budget is forever consumed. Therefore, it is crucial to allocate it most efficiently to train as many models as possible. This paper presents the scheduler for privacy that optimizes for efficiency. We formulate privacy scheduling as a new type of multidimensional knapsack problem, called privacy knapsack, which maximizes DP budget efficiency. We show that privacy knapsack is NP-hard, hence practical algorithms are necessarily approximate. We develop an approximation algorithm for privacy knapsack, DPK, and evaluate it on microbenchmarks and on a new, synthetic private-ML workload we developed from the Alibaba ML cluster trace. We show that DPK: (1) often approaches the efficiency-optimal schedule, (2) consistently schedules more tasks compared to a state-of-the-art privacy scheduling algorithm that focused on fairness (1.3-1.7x in Alibaba, 1.0-2.6x in microbenchmarks), but (3) sacrifices some level of fairness for efficiency. Therefore, using DPK, DP ML operators should be able to train more models on the same amount of user data while offering the same privacy guarantee to their users.
    Linear convergence of a policy gradient method for some finite horizon continuous time control problems. (arXiv:2203.11758v3 [math.OC] UPDATED)
    Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for continuous space-time control problems with nonlinear state dynamics has been elusive. This paper proposes proximal gradient algorithms for feedback controls of finite-time horizon stochastic control problems. The state dynamics are nonlinear diffusions with control-affine drift, and the cost functions are nonconvex in the state and nonsmooth in the control. The system noise can degenerate, which allows for deterministic control problems as special cases. We prove under suitable conditions that the algorithm converges linearly to a stationary point of the control problem, and is stable with respect to policy updates by approximate gradient steps. The convergence result justifies the recent reinforcement learning heuristics that adding entropy regularization or a fictitious discount factor to the optimization objective accelerates the convergence of policy gradient methods. The proof exploits careful regularity estimates of backward stochastic differential equations.
    Quaternion Backpropagation. (arXiv:2212.13082v1 [cs.LG])
    Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation.
    Saliency-Augmented Memory Completion for Continual Learning. (arXiv:2212.13242v1 [cs.LG])
    Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitable given bounded memory and unbounded tasks, how to forget is a problem continual learning must address. Therefore, beyond simply avoiding catastrophic forgetting, an under-explored issue is how to reasonably forget while ensuring the merits of human memory, including 1. storage efficiency, 2. generalizability, and 3. some interpretability. To achieve these simultaneously, our paper proposes a new saliency-augmented memory completion framework for continual learning, inspired by recent discoveries in memory completion separation in cognitive neuroscience. Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding. When learning new tasks, previous data from memory are inpainted by an adaptive data generation module, which is inspired by how humans complete episodic memory. The module's parameters are shared across all tasks and it can be jointly trained with a continual learning classifier as bilevel optimization. Extensive experiments on several continual learning and image classification benchmarks demonstrate the proposed method's effectiveness and efficiency.
    A photonic chip-based machine learning approach for the prediction of molecular properties. (arXiv:2203.02285v2 [cs.ET] UPDATED)
    Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever-increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural networks with faster data processing and lower energy usage compared to digital computers. Photonics technology is naturally capable of implementing complex-valued neural networks at no additional hardware cost. Here, we demonstrate the capability of photonic neural networks for predicting the quantum mechanical properties of molecules. To the best of our knowledge, this work is the first to harness photonic technology for machine learning applications in computational chemistry and molecular sciences, such as drug discovery and materials design. We further show that multiple properties can be learned simultaneously in a photonic chip via a multi-task regression learning algorithm, which is also the first of its kind as well, as most previous works focus on implementing a network in the classification task.
    Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies. (arXiv:2212.12845v1 [stat.ME])
    Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis. However, the former approach may suffer from the bias while the latter can not incorporate additional information. We propose to bridge these two approaches while allowing the number of factor proxies to diverge, and hence make the latent factor model estimation robust, flexible, and statistically more accurate. As a bonus, the number of factors is also allowed to grow. At the heart of our method is a penalized reduced rank regression to combine information. To further deal with heavy-tailed data, a computationally attractive penalized robust reduced rank regression method is proposed. We establish faster rates of convergence compared with the benchmark. Extensive simulations and real examples are used to illustrate the advantages.
    Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities. (arXiv:2212.13088v1 [cs.LG])
    How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem. A key to solving this problem is to learn low-dimensional state representations from observations, from which an effective policy can be learned. In order to boost the learning of state encoding, recent works are focused on capturing behavioral similarities between state representations or applying data augmentation on visual observations. In this paper, we propose a novel meta-learner-based framework for representation learning regarding behavioral similarities for reinforcement learning. Specifically, our framework encodes the high-dimensional observations into two decomposed embeddings regarding reward and dynamics in a Markov Decision Process (MDP). A pair of meta-learners are developed, one of which quantifies the reward similarity and the other quantifies dynamics similarity over the correspondingly decomposed embeddings. The meta-learners are self-learned to update the state embeddings by approximating two disjoint terms in on-policy bisimulation metric. To incorporate the reward and dynamics terms, we further develop a strategy to adaptively balance their impacts based on different tasks or environments. We empirically demonstrate that our proposed framework outperforms state-of-the-art baselines on several benchmarks, including conventional DM Control Suite, Distracting DM Control Suite and a self-driving task CARLA.
    Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error. (arXiv:2212.13175v1 [cs.LG])
    Training agents via off-policy deep reinforcement learning (RL) requires a large memory, named replay memory, that stores past experiences used for learning. These experiences are sampled, uniformly or non-uniformly, to create the batches used for training. When calculating the loss function, off-policy algorithms assume that all samples are of the same importance. In this paper, we hypothesize that training can be enhanced by assigning different importance for each experience based on their temporal-difference (TD) error directly in the training objective. We propose a novel method that introduces a weighting factor for each experience when calculating the loss function at the learning stage. In addition to improving convergence speed when used with uniform sampling, the method can be combined with prioritization methods for non-uniform sampling. Combining the proposed method with prioritization methods improves sampling efficiency while increasing the performance of TD-based off-policy RL algorithms. The effectiveness of the proposed method is demonstrated by experiments in six environments of the OpenAI Gym suite. The experimental results demonstrate that the proposed method achieves a 33%~76% reduction of convergence speed in three environments and an 11% increase in returns and a 3%~10% increase in success rate for other three environments.
    Designing Compact Features for Remote Stroke Rehabilitation Monitoring using Wearable Accelerometers. (arXiv:2009.08798v3 [eess.SP] UPDATED)
    Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels. However, traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive, and it is also less convenient for patients to visit clinics in a high frequency. To address this issue, in this work based on wearable sensing and machine learning techniques, we develop an automated system that can predict the assessment score in an objective manner. With wrist-worn sensors, accelerometer data is collected from 59 stroke survivors in free-living environments for a duration of 8 weeks, and we map the week-wise accelerometer data(3 days per week) to the assessment score by developing signal processing and predictive model pipeline. To achieve this, we propose two types of new features, which can encode the rehabilitation information from both paralysed and non-paralysed sides while suppressing the high level noises such as irrelevant daily activities. Based on the proposed features, we further develop the longitudinal mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects and time slots (during the 8 weeks). Comprehensive experiments are conducted to evaluate our system on both acute and chronic patients, and the promising results suggest its effectiveness.
    Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations. (arXiv:2212.12932v1 [cs.LG])
    Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial correlations and temporal dependencies of traffic speed evolving patterns by leveraging explicit spatial relations (geographical proximity) through pre-defined geographical structures ({\it e.g.}, region grids or road networks). While achieving promising results, current traffic speed prediction methods still suffer from ignoring implicit spatial correlations (interactions), which cannot be captured by grid/graph convolutions. To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations. Specifically, we first develop a Dual-Transformer architecture, including a Spatial Transformer and a Temporal Transformer. The Spatial Transformer automatically learns the implicit spatial correlations across the road segments beyond the boundary of geographical structures, while the Temporal Transformer aims to capture the dynamic changing patterns of the implicit spatial correlations. Then, to further integrate both explicit and implicit spatial correlations, we propose a distillation-style learning framework, in which the existing traffic speed prediction methods are considered as the teacher model, and the proposed Dual-Transformer architectures are considered as the student model. The extensive experiments over three real-world datasets indicate significant improvements of our proposed framework over the existing methods.
    Neural Structure Fields with Application to Crystal Structure Autoencoders. (arXiv:2212.13120v1 [cond-mat.mtrl-sci])
    Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure estimation. Among these applications, the inverse design of materials can contribute to next-generation methods that explore materials with desired properties without relying on luck or serendipity. We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks. Inspired by the concepts of vector fields in physics and implicit neural representations in computer vision, the proposed NeSF considers a crystal structure as a continuous field rather than as a discrete set of atoms. Unlike existing grid-based discretized spatial representations, the NeSF overcomes the tradeoff between spatial resolution and computational complexity and can represent any crystal structure. To evaluate the NeSF, we propose an autoencoder of crystal structures that can recover various crystal structures, such as those of perovskite structure materials and cuprate superconductors. Extensive quantitative results demonstrate the superior performance of the NeSF compared with the existing grid-based approach.
    Higher order organizational features can distinguish protein interaction networks of disease classes: a case study of neoplasms and neurological diseases. (arXiv:2212.13171v1 [q-bio.MN])
    Neoplasms (NPs) and neurological diseases and disorders (NDDs) are amongst the major classes of diseases underlying deaths of a disproportionate number of people worldwide. To determine if there exist some distinctive features in the local wiring patterns of protein interactions emerging at the onset of a disease belonging to either of these two classes, we examined 112 and 175 protein interaction networks belonging to NPs and NDDs, respectively. Orbit usage profiles (OUPs) for each of these networks were enumerated by investigating the networks' local topology. 56 non-redundant OUPs (nrOUPs) were derived and used as network features for classification between these two disease classes. Four machine learning classifiers, namely, k-nearest neighbour (KNN), support vector machine (SVM), deep neural network (DNN), random forest (RF) were trained on these data. DNN obtained the greatest average AUPRC (0.988) among these classifiers. DNNs developed on node2vec and the proposed nrOUPs embeddings were compared using 5-fold cross validation on the basis of average values of the six of performance measures, viz., AUPRC, Accuracy, Sensitivity, Specificity, Precision and MCC. It was found that nrOUPs based classifier performed better in all of these six performance measures.
    FMM-Net: neural network architecture based on the Fast Multipole Method. (arXiv:2212.12899v1 [math.NA])
    In this paper, we propose a new neural network architecture based on the H2 matrix. Even though networks with H2-inspired architecture already exist, and our approach is designed to reduce memory costs and improve performance by taking into account the sparsity template of the H2 matrix. In numerical comparison with alternative neural networks, including the known H2-based ones, our architecture showed itself as beneficial in terms of performance, memory, and scalability.
    Assessing thermal imagery integration into object detection methods on ground-based and air-based collection platforms. (arXiv:2212.12616v1 [cs.CV])
    Object detection models commonly deployed on uncrewed aerial systems (UAS) focus on identifying objects in the visible spectrum using Red-Green-Blue (RGB) imagery. However, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) images to increase the performance of object detection machine learning (ML) models. Currently LWIR ML models have received less research attention, especially for both ground- and air-based platforms, leading to a lack of baseline performance metrics evaluating LWIR, RGB and LWIR-RGB fused object detection models. Therefore, this research contributes such quantitative metrics to the literature .The results found that the ground-based blended RGB-LWIR model exhibited superior performance compared to the RGB or LWIR approaches, achieving a mAP of 98.4%. Additionally, the blended RGB-LWIR model was also the only object detection model to work in both day and night conditions, providing superior operational capabilities. This research additionally contributes a novel labelled training dataset of 12,600 images for RGB, LWIR, and RGB-LWIR fused imagery, collected from ground-based and air-based platforms, enabling further multispectral machine-driven object detection research.
    Improved Kernel Alignment Regret Bound for Online Kernel Learning. (arXiv:2212.12989v1 [cs.LG])
    In this paper, we improve the kernel alignment regret bound for online kernel learning in the regime of the Hinge loss function. Previous algorithm achieves a regret of $O((\mathcal{A}_TT\ln{T})^{\frac{1}{4}})$ at a computational complexity (space and per-round time) of $O(\sqrt{\mathcal{A}_TT\ln{T}})$, where $\mathcal{A}_T$ is called \textit{kernel alignment}. We propose an algorithm whose regret bound and computational complexity are better than previous results. Our results depend on the decay rate of eigenvalues of the kernel matrix. If the eigenvalues of the kernel matrix decay exponentially, then our algorithm enjoys a regret of $O(\sqrt{\mathcal{A}_T})$ at a computational complexity of $O(\ln^2{T})$. Otherwise, our algorithm enjoys a regret of $O((\mathcal{A}_TT)^{\frac{1}{4}})$ at a computational complexity of $O(\sqrt{\mathcal{A}_TT})$. We extend our algorithm to batch learning and obtain a $O(\frac{1}{T}\sqrt{\mathbb{E}[\mathcal{A}_T]})$ excess risk bound which improves the previous $O(1/\sqrt{T})$ bound.
    Diagnosis of COVID-19 based on Chest Radiography. (arXiv:2212.13032v1 [eess.IV])
    The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in early December 2019 and now becoming a pandemic. When COVID-19 patients undergo radiography examination, radiologists can observe the present of radiographic abnormalities from their chest X-ray (CXR) images. In this study, a deep convolutional neural network (CNN) model was proposed to aid radiologists in diagnosing COVID-19 patients. First, this work conducted a comparative study on the performance of modified VGG-16, ResNet-50 and DenseNet-121 to classify CXR images into normal, COVID-19 and viral pneumonia. Then, the impact of image augmentation on the classification results was evaluated. The publicly available COVID-19 Radiography Database was used throughout this study. After comparison, ResNet-50 achieved the highest accuracy with 95.88%. Next, after training ResNet-50 with rotation, translation, horizontal flip, intensity shift and zoom augmented dataset, the accuracy dropped to 80.95%. Furthermore, an ablation study on the effect of image augmentation on the classification results found that the combinations of rotation and intensity shift augmentation methods obtained an accuracy higher than baseline, which is 96.14%. Finally, ResNet-50 with rotation and intensity shift augmentations performed the best and was proposed as the final classification model in this work. These findings demonstrated that the proposed classification model can provide a promising result for COVID-19 diagnosis.
    Statistical Mechanics of Generalization In Graph Convolution Networks. (arXiv:2212.13069v1 [cs.LG])
    Graph neural networks (GNN) have become the default machine learning model for relational datasets, including protein interaction networks, biological neural networks, and scientific collaboration graphs. We use tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. The derived curves are phenomenologically rich: they explain the distinction between learning on homophilic and heterophilic graphs and they predict double descent whose existence in GNNs has been questioned by recent work. Our results are the first to accurately explain the behavior not only of a stylized graph learning model but also of complex GNNs on messy real-world datasets. To wit, we use our analytic insights about homophily and heterophily to improve performance of state-of-the-art graph neural networks on several heterophilic benchmarks by a simple addition of negative self-loop filters.
    Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN. (arXiv:2212.13161v1 [cs.CV])
    Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) -- a novel approach that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms -- the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used the Wavelet CNN for classification, which is a deep convolutional network analogous to the well-studied ResNet and DenseNet networks. We empirically show that our proposed PCWCNN model performs very well on a real dataset, outperforming existing approaches.
    Application of Unsupervised Domain Adaptation for Structural MRI Analysis. (arXiv:2212.12986v1 [eess.IV])
    The primary goal of this work is to study the effectiveness of an unsupervised domain adaptation approach for various applications such as binary classification and anomaly detection in the context of Alzheimer's disease (AD) detection for the OASIS datasets. We also explore image reconstruction and image synthesis for analyzing and generating 3D structural MRI data to establish performance benchmarks for anomaly detection. We successfully demonstrate that domain adaptation improves the performance of AD detection when implemented in both supervised and unsupervised settings. Additionally, the proposed methodology achieves state-of-the-art performance for binary classification on the OASIS-1 dataset.
    Inverse Multiobjective Optimization Through Online Learning. (arXiv:2010.06140v2 [cs.LG] UPDATED)
    We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and possibly carry measurement noise or are generated with the bounded rationality of decision makers. In this paper, we propose a general online learning framework to deal with this learning problem using inverse multiobjective optimization. More precisely, we develop two online learning algorithms with implicit update rules which can handle noisy data. Numerical results show that both algorithms can learn the parameters with great accuracy and are robust to noise.
    Towards Improved Prediction of Ship Performance: A Comparative Analysis on In-service Ship Monitoring Data for Modeling the Speed-Power Relation. (arXiv:2212.13061v1 [cs.LG])
    Accurate modeling of ship performance is crucial for the shipping industry to optimize fuel consumption and subsequently reduce emissions. However, predicting the speed-power relation in real-world conditions remains a challenge. In this study, we used in-service monitoring data from multiple vessels with different hull shapes to compare the accuracy of data-driven machine learning (ML) algorithms to traditional methods for assessing ship performance. Our analysis consists of two main parts: (1) a comparison of sea trial curves with calm-water curves fitted on operational data, and (2) a benchmark of multiple added wave resistance theories with an ML-based approach. Our results showed that a simple neural network outperformed established semi-empirical formulas following first principles. The neural network only required operational data as input, while the traditional methods required extensive ship particulars that are often unavailable. These findings suggest that data-driven algorithms may be more effective for predicting ship performance in practical applications.
    Doubly Smoothed GDA: Global Convergent Algorithm for Constrained Nonconvex-Nonconcave Minimax Optimization. (arXiv:2212.12978v1 [math.OC])
    Nonconvex-nonconcave minimax optimization has been the focus of intense research over the last decade due to its broad applications in machine learning and operation research. Unfortunately, most existing algorithms cannot be guaranteed to converge and always suffer from limit cycles. Their global convergence relies on certain conditions that are difficult to check, including but not limited to the global Polyak-\L{}ojasiewicz condition, the existence of a solution satisfying the weak Minty variational inequality and $\alpha$-interaction dominant condition. In this paper, we develop the first provably convergent algorithm called doubly smoothed gradient descent ascent method, which gets rid of the limit cycle without requiring any additional conditions. We further show that the algorithm has an iteration complexity of $\mathcal{O}(\epsilon^{-4})$ for finding a game stationary point, which matches the best iteration complexity of single-loop algorithms under nonconcave-concave settings. The algorithm presented here opens up a new path for designing provable algorithms for nonconvex-nonconcave minimax optimization problems.
    Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies. (arXiv:2212.13033v1 [stat.ML])
    We propose a neural network-based model for nonlinear dynamics in continuous time that can impose inductive biases on decay rates and/or frequencies. Inductive biases are helpful for training neural networks especially when training data are small. The proposed model is based on the Koopman operator theory, where the decay rate and frequency information is used by restricting the eigenvalues of the Koopman operator that describe linear evolution in a Koopman space. We use neural networks to find an appropriate Koopman space, which are trained by minimizing multi-step forecasting and backcasting errors using irregularly sampled time-series data. Experiments on various time-series datasets demonstrate that the proposed method achieves higher forecasting performance given a single short training sequence than the existing methods.
    Rapid Extraction of Respiratory Waveforms from Photoplethysmography: A Deep Encoder Approach. (arXiv:2212.12578v1 [eess.IV])
    Much of the information of breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to leverage this fact, by employing a novel deep learning framework which is a based on a repurposed convolutional autoencoder. Our model aims to encode all of the relevant respiratory information contained within photoplethysmography waveform, and decode it into a waveform that is similar to a gold standard respiratory reference. The model is employed on two photoplethysmography data sets, namely Capnobase and BIDMC. We show that the model is capable of producing respiratory waveforms that approach the gold standard, while in turn producing state of the art respiratory rate estimates. We also show that when it comes to capturing more advanced respiratory waveform characteristics such as duty cycle, our model is for the most part unsuccessful. A suggested reason for this, in light of a previous study on in-ear PPG, is that the respiratory variations in finger-PPG are far weaker compared with other recording locations. Importantly, our model can perform these waveform estimates in a fraction of a millisecond, giving it the capacity to produce over 6 hours of respiratory waveforms in a single second. Moreover, we attempt to interpret the behaviour of the kernel weights within the model, showing that in part our model intuitively selects different breathing frequencies. The model proposed in this work could help to improve the usefulness of consumer PPG-based wearables for medical applications, where detailed respiratory information is required.
    Unsupervised Instance and Subnetwork Selection for Network Data. (arXiv:2212.12771v1 [cs.LG])
    Unlike tabular data, features in network data are interconnected within a domain-specific graph. Examples of this setting include gene expression overlaid on a protein interaction network (PPI) and user opinions in a social network. Network data is typically high-dimensional (large number of nodes) and often contains outlier snapshot instances and noise. In addition, it is often non-trivial and time-consuming to annotate instances with global labels (e.g., disease or normal). How can we jointly select discriminative subnetworks and representative instances for network data without supervision? We address these challenges within an unsupervised framework for joint subnetwork and instance selection in network data, called UISS, via a convex self-representation objective. Given an unlabeled network dataset, UISS identifies representative instances while ignoring outliers. It outperforms state-of-the-art baselines on both discriminative subnetwork selection and representative instance selection, achieving up to 10% accuracy improvement on all real-world data sets we use for evaluation. When employed for exploratory analysis in RNA-seq network samples from multiple studies it produces interpretable and informative summaries.
    Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives. (arXiv:2212.12715v1 [cs.LG])
    Name ambiguity is common in academic digital libraries, such as multiple authors having the same name. This creates challenges for academic data management and analysis, thus name disambiguation becomes necessary. The procedure of name disambiguation is to divide publications with the same name into different groups, each group belonging to a unique author. A large amount of attribute information in publications makes traditional methods fall into the quagmire of feature selection. These methods always select attributes artificially and equally, which usually causes a negative impact on accuracy. The proposed method is mainly based on representation learning for heterogeneous networks and clustering and exploits the self-attention technology to solve the problem. The presentation of publications is a synthesis of structural and semantic representations. The structural representation is obtained by meta-path-based sampling and a skip-gram-based embedding method, and meta-path level attention is introduced to automatically learn the weight of each feature. The semantic representation is generated using NLP tools. Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method. The experimental results show the superiority of our new method for capturing the most attributes from publications and reducing the impact of redundant information.
    Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence. (arXiv:2212.12737v1 [physics.chem-ph])
    Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many physical invariances and symmetries can be incorporated into the kernel function to compensate for much larger datasets. So far, the scalability of this approach has however been hindered by its cubical runtime in the number of training points. While it is known, that iterative Krylov subspace solvers can overcome these burdens, they crucially rely on effective preconditioners, which are elusive in practice. Practical preconditioners need to be computationally efficient and numerically robust at the same time. Here, we consider the broad class of Nystr\"om-type methods to construct preconditioners based on successively more sophisticated low-rank approximations of the original kernel matrix, each of which provides a different set of computational trade-offs. All considered methods estimate the relevant subspace spanned by the kernel matrix columns using different strategies to identify a representative set of inducing points. Our comprehensive study covers the full spectrum of approaches, starting from naive random sampling to leverage score estimates and incomplete Cholesky factorizations, up to exact SVD decompositions.
    Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning. (arXiv:2212.12767v1 [stat.ML])
    Traffic flow prediction is an important part of smart transportation. The goal is to predict future traffic conditions based on historical data recorded by sensors and the traffic network. As the city continues to build, parts of the transportation network will be added or modified. How to accurately predict expanding and evolving long-term streaming networks is of great significance. To this end, we propose a new simulation-based criterion that considers teaching autonomous agents to mimic sensor patterns, planning their next visit based on the sensor's profile (e.g., traffic, speed, occupancy). The data recorded by the sensor is most accurate when the agent can perfectly simulate the sensor's activity pattern. We propose to formulate the problem as a continuous reinforcement learning task, where the agent is the next flow value predictor, the action is the next time-series flow value in the sensor, and the environment state is a dynamically fused representation of the sensor and transportation network. Actions taken by the agent change the environment, which in turn forces the agent's mode to update, while the agent further explores changes in the dynamic traffic network, which helps the agent predict its next visit more accurately. Therefore, we develop a strategy in which sensors and traffic networks update each other and incorporate temporal context to quantify state representations evolving over time.
    Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective. (arXiv:2112.06409v3 [cs.LG] UPDATED)
    Data-centric AI is at the center of a fundamental shift in software engineering where machine learning becomes the new software, powered by big data and computing infrastructure. Here software engineering needs to be re-thought where data becomes a first-class citizen on par with code. One striking observation is that a significant portion of the machine learning process is spent on data preparation. Without good data, even the best machine learning algorithms cannot perform well. As a result, data-centric AI practices are now becoming mainstream. Unfortunately, many datasets in the real world are small, dirty, biased, and even poisoned. In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications. Data collection is important because there is lesser need for feature engineering for recent deep learning approaches, but instead more need for large amounts of data. For data quality, we study data validation, cleaning, and integration techniques. Even if the data cannot be fully cleaned, we can still cope with imperfect data during model training using robust model training techniques. In addition, while bias and fairness have been less studied in traditional data management research, these issues become essential topics in modern machine learning applications. We thus study fairness measures and unfairness mitigation techniques that can be applied before, during, or after model training. We believe that the data management community is well poised to solve these problems.
    A Novel SOC Estimation for Hybrid Energy Pack using Deep Learning. (arXiv:2212.12607v1 [cs.CE])
    Estimating the state of charge (SOC) of compound energy storage devices in the hybrid energy storage system (HESS) of electric vehicles (EVs) is vital in improving the performance of the EV. The complex and variable charging and discharging current of EVs makes an accurate SOC estimation a challenge. This paper proposes a novel deep learning-based SOC estimation method for lithium-ion battery-supercapacitor HESS EV based on the nonlinear autoregressive with exogenous inputs neural network (NARXNN). The NARXNN is utilized to capture and overcome the complex nonlinear behaviors of lithium-ion batteries and supercapacitors in EVs. The results show that the proposed method improved the SOC estimation accuracy by 91.5% on average with error values below 0.1% and reduced consumption time by 11.4%. Hence validating both the effectiveness and robustness of the proposed method.
    A Fair Pricing Model via Adversarial Learning. (arXiv:2202.12008v3 [stat.ML] UPDATED)
    At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and "discrimination" is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver's occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.
    A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs. (arXiv:2210.03526v4 [cs.LG] UPDATED)
    We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered. Specifically, we first introduce the "extra fields" from the mixed finite element method to reformulate the PDEs so as to equivalently transform the three types of BCs into linear forms. Based on the reformulation, we derive the general solutions of the BCs analytically, which are employed to construct an ansatz that automatically satisfies the BCs. With such a framework, we can train the neural networks without adding extra loss terms and thus efficiently handle geometrically complex PDEs, alleviating the unbalanced competition between the loss terms corresponding to the BCs and PDEs. We theoretically demonstrate that the "extra fields" can stabilize the training process. Experimental results on real-world geometrically complex PDEs showcase the effectiveness of our method compared with state-of-the-art baselines.
    Computation of conditional expectations with guarantees. (arXiv:2112.01804v2 [stat.CO] UPDATED)
    Theoretically, the conditional expectation of a square-integrable random variable $Y$ given a $d$-dimensional random vector $X$ can be obtained by minimizing the mean squared distance between $Y$ and $f(X)$ over all Borel measurable functions $f \colon \mathbb{R}^d \to \mathbb{R}$. However, in many applications this minimization problem cannot be solved exactly, and instead, a numerical method which computes an approximate minimum over a suitable subfamily of Borel functions has to be used. The quality of the result depends on the adequacy of the subfamily and the performance of the numerical method. In this paper, we derive an expected value representation of the minimal mean squared distance which in many applications can efficiently be approximated with a standard Monte Carlo average. This enables us to provide guarantees for the accuracy of any numerical approximation of a given conditional expectation. We illustrate the method by assessing the quality of approximate conditional expectations obtained by linear, polynomial and neural network regression in different concrete examples.
    Your diffusion model secretly knows the dimension of the data manifold. (arXiv:2212.12611v1 [cs.LG])
    In this work, we propose a novel framework for estimating the dimension of the data manifold using a trained diffusion model. A trained diffusion model approximates the gradient of the log density of a noise-corrupted version of the target distribution for varying levels of corruption. If the data concentrates around a manifold embedded in the high-dimensional ambient space, then as the level of corruption decreases, the score function points towards the manifold, as this direction becomes the direction of maximum likelihood increase. Therefore, for small levels of corruption, the diffusion model provides us with access to an approximation of the normal bundle of the data manifold. This allows us to estimate the dimension of the tangent space, thus, the intrinsic dimension of the data manifold. Our method outperforms linear methods for dimensionality detection such as PPCA in controlled experiments.
    Ask Question First for Enhancing Lifelong Language Learning. (arXiv:2208.08367v2 [cs.CL] UPDATED)
    Lifelong language learning aims to stream learning NLP tasks while retaining knowledge of previous tasks. Previous works based on the language model and following data-free constraint approaches have explored formatting all data as "begin token (\textit{B}) + context (\textit{C}) + question (\textit{Q}) + answer (\textit{A})" for different tasks. However, they still suffer from catastrophic forgetting and are exacerbated when the previous task's pseudo data is insufficient for the following reasons: (1) The model has difficulty generating task-corresponding pseudo data, and (2) \textit{A} is prone to error when \textit{A} and \textit{C} are separated by \textit{Q} because the information of the \textit{C} is diminished before generating \textit{A}. Therefore, we propose the Ask Question First and Replay Question (AQF-RQ), including a novel data format "\textit{BQCA}" and a new training task to train pseudo questions of previous tasks. Experimental results demonstrate that AQF-RQ makes it easier for the model to generate more pseudo data that match corresponding tasks, and is more robust to both sufficient and insufficient pseudo-data when the task boundary is both clear and unclear. AQF-RQ can achieve only 0.36\% lower performance than multi-task learning.
    Concentration of the Langevin Algorithm's Stationary Distribution. (arXiv:2212.12629v1 [stat.ML])
    A canonical algorithm for log-concave sampling is the Langevin Algorithm, aka the Langevin Diffusion run with some discretization stepsize $\eta > 0$. This discretization leads the Langevin Algorithm to have a stationary distribution $\pi_{\eta}$ which differs from the stationary distribution $\pi$ of the Langevin Diffusion, and it is an important challenge to understand whether the well-known properties of $\pi$ extend to $\pi_{\eta}$. In particular, while concentration properties such as isoperimetry and rapidly decaying tails are classically known for $\pi$, the analogous properties for $\pi_{\eta}$ are open questions with direct algorithmic implications. This note provides a first step in this direction by establishing concentration results for $\pi_{\eta}$ that mirror classical results for $\pi$. Specifically, we show that for any nontrivial stepsize $\eta > 0$, $\pi_{\eta}$ is sub-exponential (respectively, sub-Gaussian) when the potential is convex (respectively, strongly convex). Moreover, the concentration bounds we show are essentially tight. Key to our analysis is the use of a rotation-invariant moment generating function (aka Bessel function) to study the stationary dynamics of the Langevin Algorithm. This technique may be of independent interest because it enables directly analyzing the discrete-time stationary distribution $\pi_{\eta}$ without going through the continuous-time stationary distribution $\pi$ as an intermediary.
    Structure-Enhanced DRL for Optimal Transmission Scheduling. (arXiv:2212.12704v1 [cs.IT])
    Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25% when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.
    Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty. (arXiv:2212.12641v1 [cs.LG])
    The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the \emph{typical set} have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.
    Regularization with Latent Space Virtual Adversarial Training. (arXiv:2011.13181v2 [cs.LG] UPDATED)
    Virtual Adversarial Training (VAT) has shown impressive results among recently developed regularization methods called consistency regularization. VAT utilizes adversarial samples, generated by injecting perturbation in the input space, for training and thereby enhances the generalization ability of a classifier. However, such adversarial samples can be generated only within a very small area around the input data point, which limits the adversarial effectiveness of such samples. To address this problem we propose LVAT (Latent space VAT), which injects perturbation in the latent space instead of the input space. LVAT can generate adversarial samples flexibly, resulting in more adverse effects and thus more effective regularization. The latent space is built by a generative model, and in this paper, we examine two different type of models: variational auto-encoder and normalizing flow, specifically Glow. We evaluated the performance of our method in both supervised and semi-supervised learning scenarios for an image classification task using SVHN and CIFAR-10 datasets. In our evaluation, we found that our method outperforms VAT and other state-of-the-art methods.
    Utilizing Priming to Identify Optimal Class Ordering to Alleviate Catastrophic Forgetting. (arXiv:2212.12643v1 [cs.LG])
    In order for artificial neural networks to begin accurately mimicking biological ones, they must be able to adapt to new exigencies without forgetting what they have learned from previous training. Lifelong learning approaches to artificial neural networks attempt to strive towards this goal, yet have not progressed far enough to be realistically deployed for natural language processing tasks. The proverbial roadblock of catastrophic forgetting still gate-keeps researchers from an adequate lifelong learning model. While efforts are being made to quell catastrophic forgetting, there is a lack of research that looks into the importance of class ordering when training on new classes for incremental learning. This is surprising as the ordering of "classes" that humans learn is heavily monitored and incredibly important. While heuristics to develop an ideal class order have been researched, this paper examines class ordering as it relates to priming as a scheme for incremental class learning. By examining the connections between various methods of priming found in humans and how those are mimicked yet remain unexplained in life-long machine learning, this paper provides a better understanding of the similarities between our biological systems and the synthetic systems while simultaneously improving current practices to combat catastrophic forgetting. Through the merging of psychological priming practices with class ordering, this paper is able to identify a generalizable method for class ordering in NLP incremental learning tasks that consistently outperforms random class ordering.
    A Convergence Rate for Manifold Neural Networks. (arXiv:2212.12606v1 [cs.LG])
    High-dimensional data arises in numerous applications, and the rapidly developing field of geometric deep learning seeks to develop neural network architectures to analyze such data in non-Euclidean domains, such as graphs and manifolds. Recent work by Z. Wang, L. Ruiz, and A. Ribeiro has introduced a method for constructing manifold neural networks using the spectral decomposition of the Laplace Beltrami operator. Moreover, in this work, the authors provide a numerical scheme for implementing such neural networks when the manifold is unknown and one only has access to finitely many sample points. The authors show that this scheme, which relies upon building a data-driven graph, converges to the continuum limit as the number of sample points tends to infinity. Here, we build upon this result by establishing a rate of convergence that depends on the intrinsic dimension of the manifold but is independent of the ambient dimension. We also discuss how the rate of convergence depends on the depth of the network and the number of filters used in each layer.
    Data-Driven Linear Complexity Low-Rank Approximation of General Kernel Matrices: A Geometric Approach. (arXiv:2212.12674v1 [math.NA])
    A general, {\em rectangular} kernel matrix may be defined as $K_{ij} = \kappa(x_i,y_j)$ where $\kappa(x,y)$ is a kernel function and where $X=\{x_i\}_{i=1}^m$ and $Y=\{y_i\}_{i=1}^n$ are two sets of points. In this paper, we seek a low-rank approximation to a kernel matrix where the sets of points $X$ and $Y$ are large and are not well-separated (e.g., the points in $X$ and $Y$ may be ``intermingled''). Such rectangular kernel matrices may arise, for example, in Gaussian process regression where $X$ corresponds to the training data and $Y$ corresponds to the test data. In this case, the points are often high-dimensional. Since the point sets are large, we must exploit the fact that the matrix arises from a kernel function, and avoid forming the matrix, and thus ruling out most algebraic techniques. In particular, we seek methods that can scale linearly, i.e., with computational complexity $O(m)$ or $O(n)$ for a fixed accuracy or rank. The main idea in this paper is to {\em geometrically} select appropriate subsets of points to construct a low rank approximation. An analysis in this paper guides how this selection should be performed.
    On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective. (arXiv:2212.12669v1 [cs.AI])
    Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.
    Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images. (arXiv:2206.03336v2 [eess.IV] UPDATED)
    Background and objective: Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors are critical. However, the relative rarity of these tumors and the highly dispersed tissue types have left an unmet need for a subtle differential diagnosis of such neoplastic lesions based on preoperative radiomics. Recently, deep learning methods have developed rapidly, especially Transformer beats the traditional convolutional neural network in computer vision. Many new Transformer-based networks have been proposed for computer vision tasks. Methods: In this study, multicenter multimodal parotid gland MR images were collected. The Swin-Unet which was based on Transformer was used. MR images of short time inversion recovery, T1-weighted and T2-weighted modalities were combined into three-channel data to train the network. We achieved segmentation of the region of interest for parotid gland and tumor. Results: The Dice-Similarity Coefficient of the model on the test set was 88.63%, Mean Pixel Accuracy was 99.31%, Mean Intersection over Union was 83.99%, and Hausdorff Distance was 3.04. Then a series of comparison experiments were designed in this paper to further validate the segmentation performance of the algorithm. Conclusions: Experimental results showed that our method has good results for parotid gland and tumor segmentation. The Transformer-based network outperforms the traditional convolutional neural network in the field of medical images.
    A Taxonomy for Inference in Causal Model Families. (arXiv:2110.12052v2 [cs.LG] UPDATED)
    Neurally-parameterized Structural Causal Models in the Pearlian notion to causality, referred to as NCM, were recently introduced as a step towards next-generation learning systems. However, said NCM are only concerned with the learning aspect of causal inference but totally miss out on the architecture aspect. That is, actual causal inference within NCM is intractable in that the NCM won't return an answer to a query in polynomial time. This insight follows as corollary to the more general statement on the intractability of arbitrary SCM parameterizations, which we prove in this work through classical 3-SAT reduction. Since future learning algorithms will be required to deal with both high dimensional data and highly complex mechanisms governing the data, we ultimately believe work on tractable inference for causality to be decisive. We also show that not all ``causal'' models are created equal. More specifically, there are models capable of answering causal queries that are not SCM, which we refer to as \emph{partially causal models} (PCM). We provide a tabular taxonomy in terms of tractability properties for all of the different model families, namely correlation-based, PCM and SCM. To conclude our work, we also provide some initial ideas on how to overcome parts of the intractability of causal inference with SCM by showing an example of how parameterizing an SCM with SPN modules can at least allow for tractable mechanisms. We hope that our impossibility result alongside the taxonomy for tractability in causal models can raise awareness for this novel research direction since achieving success with causality in real world downstream tasks will not only depend on learning correct models as we also require having the practical ability to gain access to model inferences.
    AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications. (arXiv:2205.14955v2 [cs.IT] UPDATED)
    Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of $10^{-7}$ when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.
    Attentional-Biased Stochastic Gradient Descent. (arXiv:2012.06951v4 [cs.LG] UPDATED)
    In this paper, we present a simple yet effective method (ABSGD) for addressing the data imbalance issue in deep learning. Our method is a simple modification to momentum SGD where we leverage an attentional mechanism to assign an individual importance weight to each gradient in the mini-batch. Unlike many existing heuristic-driven methods for tackling data imbalance, our method is grounded in {\it theoretically justified distributionally robust optimization (DRO)}, which is guaranteed to converge to a stationary point of an information-regularized DRO problem. The individual-level weight of a sampled data is systematically proportional to the exponential of a scaled loss value of the data, where the scaling factor is interpreted as the regularization parameter in the framework of information-regularized DRO. Compared with existing class-level weighting schemes, our method can capture the diversity between individual examples within each class. Compared with existing individual-level weighting methods using meta-learning that require three backward propagations for computing mini-batch stochastic gradients, our method is more efficient with only one backward propagation at each iteration as in standard deep learning methods. To balance between the learning of feature extraction layers and the learning of the classifier layer, we employ a two-stage method that uses SGD for pretraining followed by ABSGD for learning a robust classifier and finetuning lower layers. Our empirical studies on several benchmark datasets demonstrate the effectiveness of the proposed method.
    2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance. (arXiv:2212.13202v1 [cs.LG])
    Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data across numerous domains. Their underlying ability to represent nodes as summaries of their vicinities has proven effective for homophilous graphs in particular, in which same-type nodes tend to connect. On heterophilous graphs, in which different-type nodes are likely connected, GNNs perform less consistently, as neighborhood information might be less representative or even misleading. On the other hand, GNN performance is not inferior on all heterophilous graphs, and there is a lack of understanding of what other graph properties affect GNN performance. In this work, we highlight the limitations of the widely used homophily ratio and the recent Cross-Class Neighborhood Similarity (CCNS) metric in estimating GNN performance. To overcome these limitations, we introduce 2-hop Neighbor Class Similarity (2NCS), a new quantitative graph structural property that correlates with GNN performance more strongly and consistently than alternative metrics. 2NCS considers two-hop neighborhoods as a theoretically derived consequence of the two-step label propagation process governing GCN's training-inference process. Experiments on one synthetic and eight real-world graph datasets confirm consistent improvements over existing metrics in estimating the accuracy of GCN- and GAT-based architectures on the node classification task.
    Improving SGD convergence by online linear regression of gradients in multiple statistically relevant directions. (arXiv:1901.11457v9 [cs.LG] UPDATED)
    Deep neural networks are usually trained with stochastic gradient descent (SGD), which minimizes objective function using very rough approximations of gradient, only averaging to the real gradient. Standard approaches like momentum or ADAM only consider a single direction, and do not try to model distance from extremum - neglecting valuable information from calculated sequence of gradients, often stagnating in some suboptimal plateau. Second order methods could exploit these missed opportunities, however, beside suffering from very large cost and numerical instabilities, many of them attract to suboptimal points like saddles due to negligence of signs of curvatures (as eigenvalues of Hessian). Saddle-free Newton method is a rare example of addressing this issue - changes saddle attraction into repulsion, and was shown to provide essential improvement for final value this way. However, it neglects noise while modelling second order behavior, focuses on Krylov subspace for numerical reasons, and requires costly eigendecomposion. Maintaining SFN advantages, there are proposed inexpensive ways for exploiting these opportunities. Second order behavior is linear dependence of first derivative - we can optimally estimate it from sequence of noisy gradients with least square linear regression, in online setting here: with weakening weights of old gradients. Statistically relevant subspace is suggested by PCA of recent noisy gradients - in online setting it can be made by slowly rotating considered directions toward new gradients, gradually replacing old directions with recent statistically relevant. Eigendecomposition can be also performed online: with regularly performed step of QR method to maintain diagonal Hessian. Outside the second order modeled subspace we can simultaneously perform gradient descent.
    Gaussian Process Classification Bandits. (arXiv:2212.13157v1 [cs.LG])
    Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior. We develop a framework algorithm for the problem using various arm selection policies and propose policies called FCB and FTSV. We show a smaller sample complexity upper bound for FCB than that for the existing algorithm of the level set estimation, in which whether f(x) is at least h or not must be decided for every arm's x. Arm selection policies depending on an estimated rate of arms with rewards of at least h are also proposed and shown to improve empirical sample complexity. According to our experimental results, the rate-estimation versions of FCB and FTSV, together with that of the popular active learning policy that selects the point with the maximum variance, outperform other policies for synthetic functions, and the version of FTSV is also the best performer for our real-world dataset.
    Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks. (arXiv:2212.12620v1 [cs.RO])
    Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile robots have shown huge potential for improving human productivity. These mobile agents require low power/energy consumption to have a long lifespan since they are usually powered by batteries. These agents also need to adapt to changing/dynamic environments, especially when deployed in far or dangerous locations, thus requiring efficient online learning capabilities. These requirements can be fulfilled by employing Spiking Neural Networks (SNNs) since SNNs offer low power/energy consumption due to sparse computations and efficient online learning due to bio-inspired learning mechanisms. However, a methodology is still required to employ appropriate SNN models on autonomous mobile agents. Towards this, we propose a Mantis methodology to systematically employ SNNs on autonomous mobile agents to enable energy-efficient processing and adaptive capabilities in dynamic environments. The key ideas of our Mantis include the optimization of SNN operations, the employment of a bio-plausible online learning mechanism, and the SNN model selection. The experimental results demonstrate that our methodology maintains high accuracy with a significantly smaller memory footprint and energy consumption (i.e., 3.32x memory reduction and 2.9x energy saving for an SNN model with 8-bit weights) compared to the baseline network with 32-bit weights. In this manner, our Mantis enables the employment of SNNs for resource- and energy-constrained mobile agents.
    Refined Edge Usage of Graph Neural Networks for Edge Prediction. (arXiv:2212.12970v1 [cs.LG])
    Graph Neural Networks (GNNs), originally proposed for node classification, have also motivated many recent works on edge prediction (a.k.a., link prediction). However, existing methods lack elaborate design regarding the distinctions between two tasks that have been frequently overlooked: (i) edges only constitute the topology in the node classification task but can be used as both the topology and the supervisions (i.e., labels) in the edge prediction task; (ii) the node classification makes prediction over each individual node, while the edge prediction is determinated by each pair of nodes. To this end, we propose a novel edge prediction paradigm named Edge-aware Message PassIng neuRal nEtworks (EMPIRE). Concretely, we first introduce an edge splitting technique to specify use of each edge where each edge is solely used as either the topology or the supervision (named as topology edge or supervision edge). We then develop a new message passing mechanism that generates the messages to source nodes (through topology edges) being aware of target nodes (through supervision edges). In order to emphasize the differences between pairs connected by supervision edges and pairs unconnected, we further weight the messages to highlight the relative ones that can reflect the differences. In addition, we design a novel negative node-pair sampling trick that efficiently samples 'hard' negative instances in the supervision instances, and can significantly improve the performance. Experimental results verify that the proposed method can significantly outperform existing state-of-the-art models regarding the edge prediction task on multiple homogeneous and heterogeneous graph datasets.
    A Close Look at Spatial Modeling: From Attention to Convolution. (arXiv:2212.12552v1 [cs.CV])
    Vision Transformers have shown great promise recently for many vision tasks due to the insightful architecture design and attention mechanism. By revisiting the self-attention responses in Transformers, we empirically observe two interesting issues. First, Vision Transformers present a queryirrelevant behavior at deep layers, where the attention maps exhibit nearly consistent contexts in global scope, regardless of the query patch position (also head-irrelevant). Second, the attention maps are intrinsically sparse, few tokens dominate the attention weights; introducing the knowledge from ConvNets would largely smooth the attention and enhance the performance. Motivated by above observations, we generalize self-attention formulation to abstract a queryirrelevant global context directly and further integrate the global context into convolutions. The resulting model, a Fully Convolutional Vision Transformer (i.e., FCViT), purely consists of convolutional layers and firmly inherits the merits of both attention mechanism and convolutions, including dynamic property, weight sharing, and short- and long-range feature modeling, etc. Experimental results demonstrate the effectiveness of FCViT. With less than 14M parameters, our FCViT-S12 outperforms related work ResT-Lite by 3.7% top1 accuracy on ImageNet-1K. When scaling FCViT to larger models, we still perform better than previous state-of-the-art ConvNeXt with even fewer parameters. FCViT-based models also demonstrate promising transferability to downstream tasks, like object detection, instance segmentation, and semantic segmentation. Codes and models are made available at: https://github.com/ma-xu/FCViT.
    HandsOff: Labeled Dataset Generation With No Additional Human Annotations. (arXiv:2212.12645v1 [cs.CV])
    Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of generated labels. We introduce the HandsOff framework, a technique capable of producing an unlimited number of synthetic images and corresponding labels after being trained on less than 50 pre-existing labeled images. Our framework avoids the practical drawbacks of prior work by unifying the field of GAN inversion with dataset generation. We generate datasets with rich pixel-wise labels in multiple challenging domains such as faces, cars, full-body human poses, and urban driving scenes. Our method achieves state-of-the-art performance in semantic segmentation, keypoint detection, and depth estimation compared to prior dataset generation approaches and transfer learning baselines. We additionally showcase its ability to address broad challenges in model development which stem from fixed, hand-annotated datasets, such as the long-tail problem in semantic segmentation.
    Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks. (arXiv:2212.12621v1 [cs.SI])
    Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
    SHIRO: Soft Hierarchical Reinforcement Learning. (arXiv:2212.12786v1 [cs.RO])
    Hierarchical Reinforcement Learning (HRL) algorithms have been demonstrated to perform well on high-dimensional decision making and robotic control tasks. However, because they solely optimize for rewards, the agent tends to search the same space redundantly. This problem reduces the speed of learning and achieved reward. In this work, we present an Off-Policy HRL algorithm that maximizes entropy for efficient exploration. The algorithm learns a temporally abstracted low-level policy and is able to explore broadly through the addition of entropy to the high-level. The novelty of this work is the theoretical motivation of adding entropy to the RL objective in the HRL setting. We empirically show that the entropy can be added to both levels if the Kullback-Leibler (KL) divergence between consecutive updates of the low-level policy is sufficiently small. We performed an ablative study to analyze the effects of entropy on hierarchy, in which adding entropy to high-level emerged as the most desirable configuration. Furthermore, a higher temperature in the low-level leads to Q-value overestimation and increases the stochasticity of the environment that the high-level operates on, making learning more challenging. Our method, SHIRO, surpasses state-of-the-art performance on a range of simulated robotic control benchmark tasks and requires minimal tuning.
    Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks. (arXiv:2212.12995v1 [cs.CV])
    Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial patches by either optimizing their perturbation values while fixing the pasting position or manipulating the position while fixing the patch's content. This reveals that the positions and perturbations are both important to the adversarial attack. For that, in this paper, we propose a novel method to simultaneously optimize the position and perturbation for an adversarial patch, and thus obtain a high attack success rate in the black-box setting. Technically, we regard the patch's position, the pre-designed hyper-parameters to determine the patch's perturbations as the variables, and utilize the reinforcement learning framework to simultaneously solve for the optimal solution based on the rewards obtained from the target model with a small number of queries. Extensive experiments are conducted on the Face Recognition (FR) task, and results on four representative FR models show that our method can significantly improve the attack success rate and query efficiency. Besides, experiments on the commercial FR service and physical environments confirm its practical application value. We also extend our method to the traffic sign recognition task to verify its generalization ability.
    GraphCast: Learning skillful medium-range global weather forecasting. (arXiv:2212.12794v1 [cs.LG])
    We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.  ( 2 min )
    Multi-duplicated Characterization of Graph Structures using Information Gain Ratio for Graph Neural Networks. (arXiv:2212.12691v1 [cs.LG])
    Various graph neural networks (GNNs) have been proposed to solve node classification tasks in machine learning for graph data. GNNs use the structural information of graph data by aggregating the features of neighboring nodes. However, they fail to directly characterize and leverage the structural information. In this paper, we propose multi-duplicated characterization of graph structures using information gain ratio (IGR) for GNNs (MSI-GNN), which enhances the performance of node classification by using an i-hop adjacency matrix as the structural information of the graph data. In MSI-GNN, the i-hop adjacency matrix is adaptively adjusted by two methods: (i) structural features in the matrix are selected based on the IGR, and (ii) the selected features in (i) for each node are duplicated and combined flexibly. In an experiment, we show that our MSI-GNN outperforms GCN, H2GCN, and GCNII in terms of average accuracies in benchmark graph datasets.  ( 2 min )
    Inclusive Artificial Intelligence. (arXiv:2212.12633v1 [cs.LG])
    Prevailing methods for assessing and comparing generative AIs incentivize responses that serve a hypothetical representative individual. Evaluating models in these terms presumes homogeneous preferences across the population and engenders selection of agglomerative AIs, which fail to represent the diverse range of interests across individuals. We propose an alternative evaluation method that instead prioritizes inclusive AIs, which provably retain the requisite knowledge not only for subsequent response customization to particular segments of the population but also for utility-maximizing decisions.  ( 2 min )
    Automatic stabilization of finite-element simulations using neural networks and hierarchical matrices. (arXiv:2212.12695v1 [math.NA])
    Petrov-Galerkin formulations with optimal test functions allow for the stabilization of finite element simulations. In particular, given a discrete trial space, the optimal test space induces a numerical scheme delivering the best approximation in terms of a problem-dependent energy norm. This ideal approach has two shortcomings: first, we need to explicitly know the set of optimal test functions; and second, the optimal test functions may have large supports inducing expensive dense linear systems. Nevertheless, parametric families of PDEs are an example where it is worth investing some (offline) computational effort to obtain stabilized linear systems that can be solved efficiently, for a given set of parameters, in an online stage. Therefore, as a remedy for the first shortcoming, we explicitly compute (offline) a function mapping any PDE-parameter, to the matrix of coefficients of optimal test functions (in a basis expansion) associated with that PDE-parameter. Next, as a remedy for the second shortcoming, we use the low-rank approximation to hierarchically compress the (non-square) matrix of coefficients of optimal test functions. In order to accelerate this process, we train a neural network to learn a critical bottleneck of the compression algorithm (for a given set of PDE-parameters). When solving online the resulting (compressed) Petrov-Galerkin formulation, we employ a GMRES iterative solver with inexpensive matrix-vector multiplications thanks to the low-rank features of the compressed matrix. We perform experiments showing that the full online procedure as fast as the original (unstable) Galerkin approach. In other words, we get the stabilization with hierarchical matrices and neural networks practically for free. We illustrate our findings by means of 2D Eriksson-Johnson and Hemholtz model problems.  ( 2 min )
    Automated Gadget Discovery in Science. (arXiv:2212.12743v1 [quant-ph])
    In recent years, reinforcement learning (RL) has become increasingly successful in its application to science and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent's learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent's policy.  ( 2 min )
    Boosting Out-of-Distribution Detection with Multiple Pre-trained Models. (arXiv:2212.12720v1 [cs.LG])
    Out-of-Distribution (OOD) detection, i.e., identifying whether an input is sampled from a novel distribution other than the training distribution, is a critical task for safely deploying machine learning systems in the open world. Recently, post hoc detection utilizing pre-trained models has shown promising performance and can be scaled to large-scale problems. This advance raises a natural question: Can we leverage the diversity of multiple pre-trained models to improve the performance of post hoc detection methods? In this work, we propose a detection enhancement method by ensembling multiple detection decisions derived from a zoo of pre-trained models. Our approach uses the p-value instead of the commonly used hard threshold and leverages a fundamental framework of multiple hypothesis testing to control the true positive rate of In-Distribution (ID) data. We focus on the usage of model zoos and provide systematic empirical comparisons with current state-of-the-art methods on various OOD detection benchmarks. The proposed ensemble scheme shows consistent improvement compared to single-model detectors and significantly outperforms the current competitive methods. Our method substantially improves the relative performance by 65.40% and 26.96% on the CIFAR10 and ImageNet benchmarks.  ( 2 min )
    Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. (arXiv:2212.12603v1 [cs.LG])
    As machine learning being used increasingly in making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected population. A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints, which achieves Pareto efficiency when trading off performance against fairness. Among various fairness metrics, the ones based on the area under the ROC curve (AUC) are emerging recently because they are threshold-agnostic and effective for unbalanced data. In this work, we formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. This problem can be reformulated as a min-max optimization problem with min-max constraints, which we solve by stochastic first-order methods based on a new Bregman divergence designed for the special structure of the problem. We numerically demonstrate the effectiveness of our approach on real-world data under different fairness metrics.  ( 2 min )
    Adapting to game trees in zero-sum imperfect information games. (arXiv:2212.12567v1 [stat.ML])
    Imperfect information games (IIG) are games in which each player only partially observes the current game state. We study how to learn $\epsilon$-optimal strategies in a zero-sum IIG through self-play with trajectory feedback. We give a problem-independent lower bound $\mathcal{O}(H(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ on the required number of realizations to learn these strategies with high probability, where $H$ is the length of the game, $A_{\mathcal{X}}$ and $B_{\mathcal{Y}}$ are the total number of actions for the two players. We also propose two Follow the Regularize leader (FTRL) algorithms for this setting: Balanced-FTRL which matches this lower bound, but requires the knowledge of the information set structure beforehand to define the regularization; and Adaptive-FTRL which needs $\mathcal{O}(H^2(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ plays without this requirement by progressively adapting the regularization to the observations.
    A Labelled Sample Compression Scheme of Size at Most Quadratic in the VC Dimension. (arXiv:2212.12631v1 [cs.LG])
    This paper presents a construction of a proper and stable labelled sample compression scheme of size $O(\VCD^2)$ for any finite concept class, where $\VCD$ denotes the Vapnik-Chervonenkis Dimension. The construction is based on a well-known model of machine teaching, referred to as recursive teaching dimension. This substantially improves on the currently best known bound on the size of sample compression schemes (due to Moran and Yehudayoff), which is exponential in $\VCD$. The long-standing open question whether the smallest size of a sample compression scheme is in $O(\VCD)$ remains unresolved, but our results show that research on machine teaching is a promising avenue for the study of this open problem. As further evidence of the strong connections between machine teaching and sample compression, we prove that the model of no-clash teaching, introduced by Kirkpatrick et al., can be used to define a non-trivial lower bound on the size of stable sample compression schemes.
    A Lightweight Reconstruction Network for Surface Defect Inspection. (arXiv:2212.12878v1 [cs.CV])
    Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.  ( 2 min )
    Neural Networks beyond explainability: Selective inference for sequence motifs. (arXiv:2212.12542v1 [q-bio.GN])
    Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of deep learning, tools have been devised to extract features such as sequence motifs that can explain the predictions made by a trained network. Here we intend to go beyond explainable machine learning and introduce SEISM, a selective inference procedure to test the association between these extracted features and the predicted phenotype. In particular, we discuss how training a one-layer convolutional network is formally equivalent to selecting motifs maximizing some association score. We adapt existing sampling-based selective inference procedures by quantizing this selection over an infinite set to a large but finite grid. Finally, we show that sampling under a specific choice of parameters is sufficient to characterize the composite null hypothesis typically used for selective inference-a result that goes well beyond our particular framework. We illustrate the behavior of our method in terms of calibration, power and speed and discuss its power/speed trade-off with a simpler data-split strategy. SEISM paves the way to an easier analysis of neural networks used in regulatory genomics, and to more powerful methods for genome wide association studies (GWAS).  ( 2 min )
    Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank. (arXiv:2212.12722v1 [cs.IR])
    Understanding why a model makes certain predictions is crucial when adapting it for real world decision making. LIME is a popular model-agnostic feature attribution method for the tasks of classification and regression. However, the task of learning to rank in information retrieval is more complex in comparison with either classification or regression. In this work, we extend LIME to propose Rank-LIME, a model-agnostic, local, post-hoc linear feature attribution method for the task of learning to rank that generates explanations for ranked lists. We employ novel correlation-based perturbations, differentiable ranking loss functions and introduce new metrics to evaluate ranking based additive feature attribution models. We compare Rank-LIME with a variety of competing systems, with models trained on the MS MARCO datasets and observe that Rank-LIME outperforms existing explanation algorithms in terms of Model Fidelity and Explain-NDCG. With this we propose one of the first algorithms to generate additive feature attributions for explaining ranked lists.  ( 2 min )
    A Bayesian Robust Regression Method for Corrupted Data Reconstruction. (arXiv:2212.12787v1 [cs.LG])
    Because of the widespread existence of noise and data corruption, recovering the true regression parameters with a certain proportion of corrupted response variables is an essential task. Methods to overcome this problem often involve robust least-squares regression, but few methods perform well when confronted with severe adaptive adversarial attacks. In many applications, prior knowledge is often available from historical data or engineering experience, and by incorporating prior information into a robust regression method, we develop an effective robust regression method that can resist adaptive adversarial attacks. First, we propose the novel TRIP (hard Thresholding approach to Robust regression with sImple Prior) algorithm, which improves the breakdown point when facing adaptive adversarial attacks. Then, to improve the robustness and reduce the estimation error caused by the inclusion of priors, we use the idea of Bayesian reweighting to construct the more robust BRHT (robust Bayesian Reweighting regression via Hard Thresholding) algorithm. We prove the theoretical convergence of the proposed algorithms under mild conditions, and extensive experiments show that under different types of dataset attacks, our algorithms outperform other benchmark ones. Finally, we apply our methods to a data-recovery problem in a real-world application involving a space solar array, demonstrating their good applicability.  ( 2 min )
    Forecasting through deep learning and modal decomposition in multi-phase concentric jets. (arXiv:2212.12731v1 [cs.LG])
    This work presents a set of neural network (NN) models specifically designed for accurate and efficient fluid dynamics forecasting. In this work, we show how neural networks training can be improved by reducing data complexity through a modal decomposition technique called higher order dynamic mode decomposition (HODMD), which identifies the main structures inside flow dynamics and reconstructs the original flow using only these main structures. This reconstruction has the same number of samples and spatial dimension as the original flow, but with a less complex dynamics and preserving its main features. We also show the low computational cost required by the proposed NN models, both in their training and inference phases. The core idea of this work is to test the limits of applicability of deep learning models to data forecasting in complex fluid dynamics problems. Generalization capabilities of the models are demonstrated by using the same neural network architectures to forecast the future dynamics of four different multi-phase flows. Data sets used to train and test these deep learning models come from Direct Numerical Simulations (DNS) of these flows.  ( 2 min )
    An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context. (arXiv:2212.12735v1 [cs.LG])
    One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control. Existing works on adaptation to unknown environment contexts either assume the contexts are the same for the whole episode or assume the context variables are Markovian. However, in many real-world applications, the environment context usually stays stable for a stochastic period and then changes in an abrupt and unpredictable manner within an episode, resulting in a segment structure, which existing works fail to address. To leverage the segment structure of piecewise stable context in real-world applications, in this paper, we propose a \textit{\textbf{Se}gmented \textbf{C}ontext \textbf{B}elief \textbf{A}ugmented \textbf{D}eep~(SeCBAD)} RL method. Our method can jointly infer the belief distribution over latent context with the posterior over segment length and perform more accurate belief context inference with observed data within the current context segment. The inferred belief context can be leveraged to augment the state, leading to a policy that can adapt to abrupt variations in context. We demonstrate empirically that SeCBAD can infer context segment length accurately and outperform existing methods on a toy grid world environment and Mujuco tasks with piecewise-stable context.  ( 2 min )
    T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation. (arXiv:2212.12738v1 [cs.LG])
    Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i.e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors. In particular, GNNs face greater challenges when both node features and graph structure are incomplete at the same time. The existing methods either focus on feature completion or structure completion. They usually rely on the matching relationship between features and structure, or employ joint learning of node representation and feature (or structure) completion in the hope of achieving mutual benefit. However, recent studies confirm that the mutual interference between features and structure leads to the degradation of GNN performance. When both features and structure are incomplete, the mismatch between features and structure caused by the missing randomness exacerbates the interference between the two, which may trigger incorrect completions that negatively affect node representation. To this end, in this paper we propose a general GNN framework based on teacher-student distillation to improve the performance of GNNs on incomplete graphs, namely T2-GNN. To avoid the interference between features and structure, we separately design feature-level and structure-level teacher models to provide targeted guidance for student model (base GNNs, such as GCN) through distillation. Then we design two personalized methods to obtain well-trained feature and structure teachers. To ensure that the knowledge of the teacher model is comprehensively and effectively distilled to the student model, we further propose a dual distillation mode to enable the student to acquire as much expert knowledge as possible.  ( 2 min )
    A learning-based approach to multi-agent decision-making. (arXiv:2212.12561v1 [eess.SY])
    We propose a learning-based methodology to reconstruct private information held by a population of interacting agents in order to predict an exact outcome of the underlying multi-agent interaction process, here identified as a stationary action profile. We envision a scenario where an external observer, endowed with a learning procedure, is allowed to make queries and observe the agents' reactions through private action-reaction mappings, whose collective fixed point corresponds to a stationary profile. By adopting a smart query process to iteratively collect sensible data and update parametric estimates, we establish sufficient conditions to assess the asymptotic properties of the proposed learning-based methodology so that, if convergence happens, it can only be towards a stationary action profile. This fact yields two main consequences: i) learning locally-exact surrogates of the action-reaction mappings allows the external observer to succeed in its prediction task, and ii) working with assumptions so general that a stationary profile is not even guaranteed to exist, the established sufficient conditions hence act also as certificates for the existence of such a desirable profile. Extensive numerical simulations involving typical competitive multi-agent control and decision making problems illustrate the practical effectiveness of the proposed learning-based approach.  ( 2 min )
    Deep Latent State Space Models for Time-Series Generation. (arXiv:2212.12749v1 [stat.ML])
    Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences.  ( 2 min )
    Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning. (arXiv:2212.12658v1 [cs.LG])
    To improve uncertainty quantification of variance networks, we propose a novel tree-structured local neural network model that partitions the feature space into multiple regions based on uncertainty heterogeneity. A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty. The proposed Uncertainty-Splitting Neural Regression Tree (USNRT) employs novel splitting criteria. At each node, a neural network is trained on the full data first, and a statistical test for the residuals is conducted to find the best split, corresponding to the two sub-regions with the most significant uncertainty heterogeneity. USNRT is computationally friendly because very few leaf nodes are sufficient and pruning is unnecessary. On extensive UCI datasets, in terms of both calibration and sharpness, USNRT shows superior performance compared to some recent popular methods for variance prediction, including vanilla variance network, deep ensemble, dropout-based methods, tree-based models, etc. Through comprehensive visualization and analysis, we uncover how USNRT works and show its merits.  ( 2 min )
    Iterative regularization in classification via hinge loss diagonal descent. (arXiv:2212.12675v1 [stat.ML])
    Iterative regularization is a classic idea in regularization theory, that has recently become popular in machine learning. On the one hand, it allows to design efficient algorithms controlling at the same time numerical and statistical accuracy. On the other hand it allows to shed light on the learning curves observed while training neural networks. In this paper, we focus on iterative regularization in the context of classification. After contrasting this setting with that of regression and inverse problems, we develop an iterative regularization approach based on the use of the hinge loss function. More precisely we consider a diagonal approach for a family of algorithms for which we prove convergence as well as rates of convergence. Our approach compares favorably with other alternatives, as confirmed also in numerical simulations.  ( 2 min )
    Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control. (arXiv:2212.13141v1 [cs.NI])
    In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.
  • Open

    On Error and Compression Rates for Prototype Rules. (arXiv:2206.08014v2 [cs.LG] UPDATED)
    We study the close interplay between error and compression in the non-parametric multiclass classification setting in terms of prototype learning rules. We focus in particular on a recently proposed compression-based learning rule termed OptiNet (Kontorovich, Sabato, and Urner 2016; Kontorovich, Sabato, and Weiss 2017; Hanneke et al. 2021). Beyond its computational merits, this rule has been recently shown to be universally consistent in any metric instance space that admits a universally consistent rule--the first learning algorithm known to enjoy this property. However, its error and compression rates have been left open. Here we derive such rates in the case where instances reside in Euclidean space under commonly posed smoothness and tail conditions on the data distribution. We first show that OptiNet achieves non-trivial compression rates while enjoying near minimax-optimal error rates. We then proceed to study a novel general compression scheme for further compressing prototype rules that locally adapts to the noise level without sacrificing accuracy. Applying it to OptiNet, we show that under a geometric margin condition, further gain in the compression rate is achieved. Experimental results comparing the performance of the various methods are presented.
    Sliced gradient-enhanced Kriging for high-dimensional function approximation and aerodynamic modeling. (arXiv:2204.03562v2 [stat.ML] UPDATED)
    Gradient-enhanced Kriging (GE-Kriging) is a well-established surrogate modelling technique for approximating expensive computational models. However, it tends to get impractical for high-dimensional problems due to the large inherent correlation matrix and the associated high-dimensional hyper-parameter tuning problem. To address these issues, we propose a new method in this paper, called sliced GE-Kriging (SGE-Kriging) for reducing both the size of the correlation matrix and the number of hyper-parameters. Firstly, we perform a derivative-based global sensitivity analysis to detect the relative importance of each input variable with respect to model response. Then, we propose to split the training sample set into multiple slices, and invoke Bayes' theorem to approximate the full likelihood function via a sliced likelihood function, in which multiple small correlation matrices are utilized to describe the correlation of the sample set. Additionally, we replace the original high-dimensional hyper-parameter tuning problem with a low-dimensional counterpart by learning the relationship between the hyper-parameters and the global sensitivity indices. Finally, we validate SGE-Kriging by means of numerical experiments with several benchmarks problems. The results show that the SGE-Kriging model features an accuracy and robustness that is comparable to the standard one but comes at much less training costs. The benefits are most evident in high-dimensional problems.
    Indeterminacy and Strong Identifiability in Generative Models. (arXiv:2206.00801v3 [stat.ML] UPDATED)
    Most modern probabilistic generative models, such as the variational autoencoder (VAE), have certain indeterminacies that are unresolvable even with an infinite amount of data. Different tasks tolerate different indeterminacies, however recent applications have indicated the need for strongly identifiable models, in which an observation corresponds to a unique latent code. Progress has been made towards reducing model indeterminacies while maintaining flexibility, and recent work excludes many--but not all--indeterminacies. In this work, we motivate model-identifiability in terms of task-identifiability, then construct a theoretical framework for analyzing the indeterminacies of latent variable models, which enables their precise characterization in terms of the generator function and prior distribution spaces. We reveal that strong identifiability is possible even with highly flexible nonlinear generators, and give two such examples. One is a straightforward modification of iVAE (arXiv:1907.04809 [stat.ML]); the other uses triangular monotonic maps, leading to novel connections between optimal transport and identifiability.
    Granger Causal Chain Discovery for Sepsis-Associated Derangements via Multivariate Hawkes Processes. (arXiv:2209.04480v2 [stat.AP] UPDATED)
    Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have clearly elucidated prodromes (i.e., event chains) that can be used to identify and intercept the adverse event early in its course. Currently there does not exist a reliable framework for discovering or describing causal chains that precede adverse hospital events. Clinically relevant and interpretable results require a framework that can (1) infer temporal interactions across multiple patient features found in EMR data (e.g., labs, vital signs, etc.) and (2) can identify pattern(s) which precede and are specific to an impending adverse event (e.g., sepsis). In this work, we propose a linear multivariate Hawkes process model, coupled with $g(x) = x^+$ link function to allow potential inhibition effects, in order to recover a Granger Causal (GC) graph. We develop a two-phase gradient-based scheme to maximize a surrogate of likelihood to estimate the problem parameters. This two-phase algorithm is scalable and shown to be effective via our numerical simulation. It is subsequently extended to a data set of patients admitted to Grady hospital system in Atalanta, GA, where the fitted Granger Causal graph identifies several highly interpretable chains that precede sepsis.
    Data Redaction from Pre-trained GANs. (arXiv:2206.14389v2 [cs.LG] UPDATED)
    Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness. The common way to mitigate this is to re-train them differently from scratch using different data or different regularization -- which uses a lot of computational resources and does not always fully address the problem. In this work, we take a different, more compute-friendly approach and investigate how to post-edit a model after training so that it ''redacts'', or refrains from outputting certain kinds of samples. We show that redaction is a fundamentally different task from data deletion, and data deletion may not always lead to redaction. We then consider Generative Adversarial Networks (GANs), and provide three different algorithms for data redaction that differ on how the samples to be redacted are described. Extensive evaluations on real-world image datasets show that our algorithms out-perform data deletion baselines, and are capable of redacting data while retaining high generation quality at a fraction of the cost of full re-training.
    Tensor Principal Component Analysis. (arXiv:2212.12981v1 [econ.EM])
    In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data. We propose an estimation algorithm, called tensor principal component analysis (PCA), which generalizes the traditional PCA applicable to panel data. The algorithm involves unfolding the tensor into a sequence of matrices along different dimensions and applying PCA to the unfolded matrices. We provide theoretical results on the consistency and asymptotic distribution for tensor PCA estimator of loadings and factors. The algorithm demonstrates good performance in Mote Carlo experiments and is applied to sorted portfolios.
    Why neural networks find simple solutions: the many regularizers of geometric complexity. (arXiv:2209.13083v2 [cs.LG] UPDATED)
    In many contexts, simpler models are preferable to more complex models and the control of this model complexity is the goal for many methods in machine learning such as regularization, hyperparameter tuning and architecture design. In deep learning, it has been difficult to understand the underlying mechanisms of complexity control, since many traditional measures are not naturally suitable for deep neural networks. Here we develop the notion of geometric complexity, which is a measure of the variability of the model function, computed using a discrete Dirichlet energy. Using a combination of theoretical arguments and empirical results, we show that many common training heuristics such as parameter norm regularization, spectral norm regularization, flatness regularization, implicit gradient regularization, noise regularization and the choice of parameter initialization all act to control geometric complexity, providing a unifying framework in which to characterize the behavior of deep learning models.
    Gaussian Pre-Activations in Neural Networks: Myth or Reality?. (arXiv:2205.12379v2 [cs.LG] UPDATED)
    The study of feature propagation at initialization in neural networks lies at the root of numerous initialization designs. An assumption very commonly made in the field states that the pre-activations are Gaussian. Although this convenient Gaussian hypothesis can be justified when the number of neurons per layer tends to infinity, it is challenged by both theoretical and experimental works for finite-width neural networks. Our major contribution is to construct a family of pairs of activation functions and initialization distributions that ensure that the pre-activations remain Gaussian throughout the network's depth, even in narrow neural networks. In the process, we discover a set of constraints that a neural network should fulfill to ensure Gaussian pre-activations. Additionally, we provide a critical review of the claims of the Edge of Chaos line of works and build an exact Edge of Chaos analysis. We also propose a unified view on pre-activations propagation, encompassing the framework of several well-known initialization procedures. Finally, our work provides a principled framework for answering the much-debated question: is it desirable to initialize the training of a neural network whose pre-activations are ensured to be Gaussian?
    Inference on Strongly Identified Functionals of Weakly Identified Functions. (arXiv:2208.08291v2 [stat.ME] UPDATED)
    In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e.g., average causal effects) of nuisance function (e.g., NPIV regression) defined by conditional moment restrictions. These nuisance functions are generally weakly identified, in that the conditional moment restrictions can be severely ill-posed as well as admit multiple solutions. This is sometimes resolved by imposing strong conditions that imply the function can be estimated at rates that make inference on the functional possible. In this paper, we study a novel condition for the functional to be strongly identified even when the nuisance function is not; that is, the functional is amenable to asymptotically-normal estimation at $\sqrt{n}$-rates. The condition implies the existence of debiasing nuisance functions, and we propose penalized minimax estimators for both the primary and debiasing nuisance functions. The proposed nuisance estimators can accommodate flexible function classes, and importantly they can converge to fixed limits determined by the penalization regardless of the identifiability of the nuisances. We use the penalized nuisance estimators to form a debiased estimator for the functional of interest and prove its asymptotic normality under generic high-level conditions, which provide for asymptotically valid confidence intervals. We also illustrate our method in a novel partially linear proximal causal inference problem and a partially linear instrumental variable regression problem.
    How unfair is private learning ?. (arXiv:2206.03985v2 [cs.LG] UPDATED)
    As machine learning algorithms are deployed on sensitive data in critical decision making processes, it is becoming increasingly important that they are also private and fair. In this paper, we show that, when the data has a long-tailed structure, it is not possible to build accurate learning algorithms that are both private and results in higher accuracy on minority subpopulations. We further show that relaxing overall accuracy can lead to good fairness even with strict privacy requirements. To corroborate our theoretical results in practice, we provide an extensive set of experimental results using a variety of synthetic, vision (CIFAR10 and CelebA), and tabular (Law School) datasets and learning algorithms.
    Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning. (arXiv:2109.03445v3 [stat.ML] UPDATED)
    The stochastic approximation (SA) algorithm is a widely used probabilistic method for finding a zero or a fixed point of a vector-valued funtion, when only noisy measurements of the function are available. In the literature to date, one makes a distinction between ``synchronous'' updating, whereby every component of the current guess is updated at each time, and ``asynchronous'' updating, whereby only one component is updated. In this paper, we study an intermediate situation that we call ``batch asynchronous stochastic approximation'' (BASA), in which, at each time instant, \textit{some but not all} components of the current estimated solution are updated. BASA allows the user to trade off memory requirements against time complexity. We develop a general methodology for proving that such algorithms converge to the fixed point of the map under study. These convergence proofs make use of weaker hypotheses than existing results. Specifically, existing convergence proofs require that the measurement noise is a zero-mean i.i.d\ sequence or a martingale difference sequence. In the present paper, we permit biased measurements, that is, measurement noises that have nonzero conditional mean. Also, all convergence results to date assume that the stochastic step sizes satisfy a probabilistic analog of the well-known Robbins-Monro conditions. We replace this assumption by a purely deterministic condition on the irreducibility of the underlying Markov processes. As specific applications to Reinforcement Learning, we analyze the temporal difference algorithm $TD(\lambda)$ for value iteration, and the $Q$-learning algorithm for finding the optimal action-value function. In both cases, we establish the convergence of these algorithms, under milder conditions than in the existing literature.
    Attentional-Biased Stochastic Gradient Descent. (arXiv:2012.06951v4 [cs.LG] UPDATED)
    In this paper, we present a simple yet effective method (ABSGD) for addressing the data imbalance issue in deep learning. Our method is a simple modification to momentum SGD where we leverage an attentional mechanism to assign an individual importance weight to each gradient in the mini-batch. Unlike many existing heuristic-driven methods for tackling data imbalance, our method is grounded in {\it theoretically justified distributionally robust optimization (DRO)}, which is guaranteed to converge to a stationary point of an information-regularized DRO problem. The individual-level weight of a sampled data is systematically proportional to the exponential of a scaled loss value of the data, where the scaling factor is interpreted as the regularization parameter in the framework of information-regularized DRO. Compared with existing class-level weighting schemes, our method can capture the diversity between individual examples within each class. Compared with existing individual-level weighting methods using meta-learning that require three backward propagations for computing mini-batch stochastic gradients, our method is more efficient with only one backward propagation at each iteration as in standard deep learning methods. To balance between the learning of feature extraction layers and the learning of the classifier layer, we employ a two-stage method that uses SGD for pretraining followed by ABSGD for learning a robust classifier and finetuning lower layers. Our empirical studies on several benchmark datasets demonstrate the effectiveness of the proposed method.
    Formalising the Use of the Activation Function in Neural Inference. (arXiv:2102.04896v3 [q-bio.NC] UPDATED)
    We investigate how the activation function can be used to describe neural firing in an abstract way, and in turn, why it works well in artificial neural networks. We discuss how a spike in a biological neurone belongs to a particular universality class of phase transitions in statistical physics. We then show that the artificial neurone is, mathematically, a mean field model of biological neural membrane dynamics, which arises from modelling spiking as a phase transition. This allows us to treat selective neural firing in an abstract way, and formalise the role of the activation function in perceptron learning. The resultant statistical physical model allows us to recover the expressions for some known activation functions as various special cases. Along with deriving this model and specifying the analogous neural case, we analyse the phase transition to understand the physics of neural network learning. Together, it is shown that there is not only a biological meaning, but a physical justification, for the emergence and performance of typical activation functions; implications for neural learning and inference are also discussed.
    Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders. (arXiv:2212.13067v1 [cs.LG])
    Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often require a large number of labeled examples, yet obtaining the label information can be very expensive given the high time and cost required by quality inspections. In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query. However, most of the active learning strategies proposed for regression focus on the offline setting. In this work, we adapt some of these approaches to the stream-based scenario and show how they can be used to select the most informative data points. We also demonstrate how to use a semi-supervised architecture based on orthogonal autoencoders to learn salient features in a lower dimensional space. The Tennessee Eastman Process is used to compare the predictive performance of the proposed approaches.
    Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models. (arXiv:2101.02305v2 [cs.LG] UPDATED)
    Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, four different demand forecasting methods, ARIMA (Auto Regressive Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator) and LSTM (Long Short-Term Memory) networks are utilized and evaluated. We use a large clinical dataset for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily platelet transfusions along with information such as the product specifications, the recipients' characteristics, and the recipients' laboratory test results. This study is the first to utilize different methods from statistical time series models to data-driven regression and a machine learning technique for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariate approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach such as ARIMA appears to be sufficient. We also comment on the approach to choose clinical indicators (inputs) for the multivariate models.
    A Generalized EigenGame with Extensions to Multiview Representation Learning. (arXiv:2211.11323v2 [cs.LG] UPDATED)
    Generalized Eigenvalue Problems (GEPs) encompass a range of interesting dimensionality reduction methods. Development of efficient stochastic approaches to these problems would allow them to scale to larger datasets. Canonical Correlation Analysis (CCA) is one example of a GEP for dimensionality reduction which has found extensive use in problems with two or more views of the data. Deep learning extensions of CCA require large mini-batch sizes, and therefore large memory consumption, in the stochastic setting to achieve good performance and this has limited its application in practice. Inspired by the Generalized Hebbian Algorithm, we develop an approach to solving stochastic GEPs in which all constraints are softly enforced by Lagrange multipliers. Then by considering the integral of this Lagrangian function, its pseudo-utility, and inspired by recent formulations of Principal Components Analysis and GEPs as games with differentiable utilities, we develop a game-theory inspired approach to solving GEPs. We show that our approaches share much of the theoretical grounding of the previous Hebbian and game theoretic approaches for the linear case but our method permits extension to general function approximators like neural networks for certain GEPs for dimensionality reduction including CCA which means our method can be used for deep multiview representation learning. We demonstrate the effectiveness of our method for solving GEPs in the stochastic setting using canonical multiview datasets and demonstrate state-of-the-art performance for optimizing Deep CCA.
    Iterative regularization in classification via hinge loss diagonal descent. (arXiv:2212.12675v1 [stat.ML])
    Iterative regularization is a classic idea in regularization theory, that has recently become popular in machine learning. On the one hand, it allows to design efficient algorithms controlling at the same time numerical and statistical accuracy. On the other hand it allows to shed light on the learning curves observed while training neural networks. In this paper, we focus on iterative regularization in the context of classification. After contrasting this setting with that of regression and inverse problems, we develop an iterative regularization approach based on the use of the hinge loss function. More precisely we consider a diagonal approach for a family of algorithms for which we prove convergence as well as rates of convergence. Our approach compares favorably with other alternatives, as confirmed also in numerical simulations.
    Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning. (arXiv:2212.12767v1 [stat.ML])
    Traffic flow prediction is an important part of smart transportation. The goal is to predict future traffic conditions based on historical data recorded by sensors and the traffic network. As the city continues to build, parts of the transportation network will be added or modified. How to accurately predict expanding and evolving long-term streaming networks is of great significance. To this end, we propose a new simulation-based criterion that considers teaching autonomous agents to mimic sensor patterns, planning their next visit based on the sensor's profile (e.g., traffic, speed, occupancy). The data recorded by the sensor is most accurate when the agent can perfectly simulate the sensor's activity pattern. We propose to formulate the problem as a continuous reinforcement learning task, where the agent is the next flow value predictor, the action is the next time-series flow value in the sensor, and the environment state is a dynamically fused representation of the sensor and transportation network. Actions taken by the agent change the environment, which in turn forces the agent's mode to update, while the agent further explores changes in the dynamic traffic network, which helps the agent predict its next visit more accurately. Therefore, we develop a strategy in which sensors and traffic networks update each other and incorporate temporal context to quantify state representations evolving over time.
    Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression. (arXiv:2104.03164v4 [cs.CV] UPDATED)
    Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher. However, applying KD in image regression with a scalar response variable has been rarely studied, and there exists no KD method applicable to both classification and regression tasks yet. Moreover, existing KD methods often require a practitioner to carefully select or adjust the teacher and student architectures, making these methods less flexible in practice. To address the above problems in a unified way, we propose a comprehensive KD framework based on cGANs, termed cGAN-KD. Fundamentally different from existing KD methods, cGAN-KD distills and transfers knowledge from a teacher model to a student model via cGAN-generated samples. This novel mechanism makes cGAN-KD suitable for both classification and regression tasks, compatible with other KD methods, and insensitive to the teacher and student architectures. An error bound for a student model trained in the cGAN-KD framework is derived in this work, providing a theory for why cGAN-KD is effective as well as guiding the practical implementation of cGAN-KD. Extensive experiments on CIFAR-100 and ImageNet-100 show that we can combine state of the art KD methods with the cGAN-KD framework to yield a new state of the art. Moreover, experiments on Steering Angle and UTKFace demonstrate the effectiveness of cGAN-KD in image regression tasks, where existing KD methods are inapplicable.
    A Fair Pricing Model via Adversarial Learning. (arXiv:2202.12008v3 [stat.ML] UPDATED)
    At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and "discrimination" is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver's occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.
    DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning. (arXiv:2210.04389v2 [stat.ML] UPDATED)
    Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness. To reduce bias for estimating Natural Direct and Indirect Effects in mediation analysis, we propose a new method called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. We obtain novel theoretical results that our DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support our findings and also expose the gap between theory and practice. As a proof of concept, we apply DeepMed to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.
    Deep Latent State Space Models for Time-Series Generation. (arXiv:2212.12749v1 [stat.ML])
    Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences.
    Doubly Smoothed GDA: Global Convergent Algorithm for Constrained Nonconvex-Nonconcave Minimax Optimization. (arXiv:2212.12978v1 [math.OC])
    Nonconvex-nonconcave minimax optimization has been the focus of intense research over the last decade due to its broad applications in machine learning and operation research. Unfortunately, most existing algorithms cannot be guaranteed to converge and always suffer from limit cycles. Their global convergence relies on certain conditions that are difficult to check, including but not limited to the global Polyak-\L{}ojasiewicz condition, the existence of a solution satisfying the weak Minty variational inequality and $\alpha$-interaction dominant condition. In this paper, we develop the first provably convergent algorithm called doubly smoothed gradient descent ascent method, which gets rid of the limit cycle without requiring any additional conditions. We further show that the algorithm has an iteration complexity of $\mathcal{O}(\epsilon^{-4})$ for finding a game stationary point, which matches the best iteration complexity of single-loop algorithms under nonconcave-concave settings. The algorithm presented here opens up a new path for designing provable algorithms for nonconvex-nonconcave minimax optimization problems.
    Statistical Mechanics of Generalization In Graph Convolution Networks. (arXiv:2212.13069v1 [cs.LG])
    Graph neural networks (GNN) have become the default machine learning model for relational datasets, including protein interaction networks, biological neural networks, and scientific collaboration graphs. We use tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. The derived curves are phenomenologically rich: they explain the distinction between learning on homophilic and heterophilic graphs and they predict double descent whose existence in GNNs has been questioned by recent work. Our results are the first to accurately explain the behavior not only of a stylized graph learning model but also of complex GNNs on messy real-world datasets. To wit, we use our analytic insights about homophily and heterophily to improve performance of state-of-the-art graph neural networks on several heterophilic benchmarks by a simple addition of negative self-loop filters.
    Policy Learning with Competing Agents. (arXiv:2204.01884v2 [stat.ML] UPDATED)
    Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to such policies, competition arises, complicating the estimation of the effect of the policy. In this paper, we study capacity-constrained treatment assignment in the presence of such interference. We consider a dynamic model where the decision maker allocates treatments at each time step and heterogeneous agents myopically best respond to the previous treatment assignment policy. When the number of agents is large but finite, we show that the threshold for receiving treatment under a given policy converges to the policy's mean-field equilibrium threshold. Based on this result, we develop a consistent estimator for the policy effect. In simulations and a semi-synthetic experiment with data from the National Education Longitudinal Study of 1988, we demonstrate that this estimator can be used for learning capacity-constrained policies in the presence of strategic behavior.
    Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies. (arXiv:2212.13033v1 [stat.ML])
    We propose a neural network-based model for nonlinear dynamics in continuous time that can impose inductive biases on decay rates and/or frequencies. Inductive biases are helpful for training neural networks especially when training data are small. The proposed model is based on the Koopman operator theory, where the decay rate and frequency information is used by restricting the eigenvalues of the Koopman operator that describe linear evolution in a Koopman space. We use neural networks to find an appropriate Koopman space, which are trained by minimizing multi-step forecasting and backcasting errors using irregularly sampled time-series data. Experiments on various time-series datasets demonstrate that the proposed method achieves higher forecasting performance given a single short training sequence than the existing methods.
    Improving SGD convergence by online linear regression of gradients in multiple statistically relevant directions. (arXiv:1901.11457v9 [cs.LG] UPDATED)
    Deep neural networks are usually trained with stochastic gradient descent (SGD), which minimizes objective function using very rough approximations of gradient, only averaging to the real gradient. Standard approaches like momentum or ADAM only consider a single direction, and do not try to model distance from extremum - neglecting valuable information from calculated sequence of gradients, often stagnating in some suboptimal plateau. Second order methods could exploit these missed opportunities, however, beside suffering from very large cost and numerical instabilities, many of them attract to suboptimal points like saddles due to negligence of signs of curvatures (as eigenvalues of Hessian). Saddle-free Newton method is a rare example of addressing this issue - changes saddle attraction into repulsion, and was shown to provide essential improvement for final value this way. However, it neglects noise while modelling second order behavior, focuses on Krylov subspace for numerical reasons, and requires costly eigendecomposion. Maintaining SFN advantages, there are proposed inexpensive ways for exploiting these opportunities. Second order behavior is linear dependence of first derivative - we can optimally estimate it from sequence of noisy gradients with least square linear regression, in online setting here: with weakening weights of old gradients. Statistically relevant subspace is suggested by PCA of recent noisy gradients - in online setting it can be made by slowly rotating considered directions toward new gradients, gradually replacing old directions with recent statistically relevant. Eigendecomposition can be also performed online: with regularly performed step of QR method to maintain diagonal Hessian. Outside the second order modeled subspace we can simultaneously perform gradient descent.
    A Universal Law of Robustness via Isoperimetry. (arXiv:2105.12806v4 [cs.LG] UPDATED)
    Classically, data interpolation with a parametrized model class is possible as long as the number of parameters is larger than the number of equations to be satisfied. A puzzling phenomenon in deep learning is that models are trained with many more parameters than what this classical theory would suggest. We propose a partial theoretical explanation for this phenomenon. We prove that for a broad class of data distributions and model classes, overparametrization is necessary if one wants to interpolate the data smoothly. Namely we show that smooth interpolation requires $d$ times more parameters than mere interpolation, where $d$ is the ambient data dimension. We prove this universal law of robustness for any smoothly parametrized function class with polynomial size weights, and any covariate distribution verifying isoperimetry. In the case of two-layers neural networks and Gaussian covariates, this law was conjectured in prior work by Bubeck, Li and Nagaraj. We also give an interpretation of our result as an improved generalization bound for model classes consisting of smooth functions.
    Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence. (arXiv:2212.12737v1 [physics.chem-ph])
    Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many physical invariances and symmetries can be incorporated into the kernel function to compensate for much larger datasets. So far, the scalability of this approach has however been hindered by its cubical runtime in the number of training points. While it is known, that iterative Krylov subspace solvers can overcome these burdens, they crucially rely on effective preconditioners, which are elusive in practice. Practical preconditioners need to be computationally efficient and numerically robust at the same time. Here, we consider the broad class of Nystr\"om-type methods to construct preconditioners based on successively more sophisticated low-rank approximations of the original kernel matrix, each of which provides a different set of computational trade-offs. All considered methods estimate the relevant subspace spanned by the kernel matrix columns using different strategies to identify a representative set of inducing points. Our comprehensive study covers the full spectrum of approaches, starting from naive random sampling to leverage score estimates and incomplete Cholesky factorizations, up to exact SVD decompositions.
    Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions. (arXiv:2212.13145v1 [econ.EM])
    In various fields of data science, researchers are often interested in estimating the ratio of conditional expectation functions (CEFR). Specifically in causal inference problems, it is sometimes natural to consider ratio-based treatment effects, such as odds ratios and hazard ratios, and even difference-based treatment effects are identified as CEFR in some empirically relevant settings. This chapter develops the general framework for estimation and inference on CEFR, which allows the use of flexible machine learning for infinite-dimensional nuisance parameters. In the first stage of the framework, the orthogonal signals are constructed using debiased machine learning techniques to mitigate the negative impacts of the regularization bias in the nuisance estimates on the target estimates. The signals are then combined with a novel series estimator tailored for CEFR. We derive the pointwise and uniform asymptotic results for estimation and inference on CEFR, including the validity of the Gaussian bootstrap, and provide low-level sufficient conditions to apply the proposed framework to some specific examples. We demonstrate the finite-sample performance of the series estimator constructed under the proposed framework by numerical simulations. Finally, we apply the proposed method to estimate the causal effect of the 401(k) program on household assets.  ( 2 min )
    Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity. (arXiv:2208.05767v3 [cs.LG] UPDATED)
    This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration. Due to uncertainties and variabilities of the environment, it is critical to learn a robust policy -- with as few samples as possible -- that performs well even when the deployed environment deviates from the nominal one used to collect the history dataset. We consider a distributionally robust formulation of offline RL, focusing on tabular robust Markov decision processes with an uncertainty set specified by the Kullback-Leibler divergence in both finite-horizon and infinite-horizon settings. To combat with sample scarcity, a model-based algorithm that combines distributionally robust value iteration with the principle of pessimism in the face of uncertainty is proposed, by penalizing the robust value estimates with a carefully designed data-driven penalty term. Under a mild and tailored assumption of the history dataset that measures distribution shift without requiring full coverage of the state-action space, we establish the finite-sample complexity of the proposed algorithm, and further show it is almost unimprovable in light of a nearly-matching information-theoretic lower bound up to a polynomial factor of the (effective) horizon length. To the best our knowledge, this provides the first provably near-optimal robust offline RL algorithm that learns under model uncertainty and partial coverage.
    Learning k-Level Sparse Neural Networks Using a New Generalized Group Sparse Envelope Regularization. (arXiv:2212.12921v1 [cs.LG])
    We propose an efficient method to learn both unstructured and structured sparse neural networks during training, using a novel generalization of the sparse envelope function (SEF) used as a regularizer, termed {\itshape{group sparse envelope function}} (GSEF). The GSEF acts as a neuron group selector, which we leverage to induce structured pruning. Our method receives a hardware-friendly structured sparsity of a deep neural network (DNN) to efficiently accelerate the DNN's evaluation. This method is flexible in the sense that it allows any hardware to dictate the definition of a group, such as a filter, channel, filter shape, layer depth, a single parameter (unstructured), etc. By the nature of the GSEF, the proposed method is the first to make possible a pre-define sparsity level that is being achieved at the training convergence, while maintaining negligible network accuracy degradation. We propose an efficient method to calculate the exact value of the GSEF along with its proximal operator, in a worst-case complexity of $O(n)$, where $n$ is the total number of groups variables. In addition, we propose a proximal-gradient-based optimization method to train the model, that is, the non-convex minimization of the sum of the neural network loss and the GSEF. Finally, we conduct an experiment and illustrate the efficiency of our proposed technique in terms of the completion ratio, accuracy, and inference latency.
    Faster Randomized Methods for Orthogonality Constrained Problems. (arXiv:2106.12060v1 [math.NA] CROSS LISTED)
    Recent literature has advocated the use of randomized methods for accelerating the solution of various matrix problems arising throughout data science and computational science. One popular strategy for leveraging randomization is to use it as a way to reduce problem size. However, methods based on this strategy lack sufficient accuracy for some applications. Randomized preconditioning is another approach for leveraging randomization, which provides higher accuracy. The main challenge in using randomized preconditioning is the need for an underlying iterative method, thus randomized preconditioning so far have been applied almost exclusively to solving regression problems and linear systems. In this article, we show how to expand the application of randomized preconditioning to another important set of problems prevalent across data science: optimization problems with (generalized) orthogonality constraints. We demonstrate our approach, which is based on the framework of Riemannian optimization and Riemannian preconditioning, on the problem of computing the dominant canonical correlations and on the Fisher linear discriminant analysis problem. For both problems, we evaluate the effect of preconditioning on the computational costs and asymptotic convergence, and demonstrate empirically the utility of our approach.
    Exact Selective Inference with Randomization. (arXiv:2212.12940v1 [stat.ME])
    We introduce a pivot for exact selective inference with randomization. Not only does our pivot lead to exact inference in Gaussian regression models, but it is also available in closed form. We reduce the problem of exact selective inference to a bivariate truncated Gaussian distribution. By doing so, we give up some power that is achieved with approximate inference in Panigrahi and Taylor (2022). Yet we always produce narrower confidence intervals than a closely related data-splitting procedure. For popular instances of Gaussian regression, this price -- in terms of power -- in exchange for exact selective inference is demonstrated in simulated experiments and in an HIV drug resistance analysis.
    Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning. (arXiv:2212.12658v1 [cs.LG])
    To improve uncertainty quantification of variance networks, we propose a novel tree-structured local neural network model that partitions the feature space into multiple regions based on uncertainty heterogeneity. A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty. The proposed Uncertainty-Splitting Neural Regression Tree (USNRT) employs novel splitting criteria. At each node, a neural network is trained on the full data first, and a statistical test for the residuals is conducted to find the best split, corresponding to the two sub-regions with the most significant uncertainty heterogeneity. USNRT is computationally friendly because very few leaf nodes are sufficient and pruning is unnecessary. On extensive UCI datasets, in terms of both calibration and sharpness, USNRT shows superior performance compared to some recent popular methods for variance prediction, including vanilla variance network, deep ensemble, dropout-based methods, tree-based models, etc. Through comprehensive visualization and analysis, we uncover how USNRT works and show its merits.  ( 2 min )
    Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints. (arXiv:2212.12603v1 [cs.LG])
    As machine learning being used increasingly in making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected population. A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints, which achieves Pareto efficiency when trading off performance against fairness. Among various fairness metrics, the ones based on the area under the ROC curve (AUC) are emerging recently because they are threshold-agnostic and effective for unbalanced data. In this work, we formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. This problem can be reformulated as a min-max optimization problem with min-max constraints, which we solve by stochastic first-order methods based on a new Bregman divergence designed for the special structure of the problem. We numerically demonstrate the effectiveness of our approach on real-world data under different fairness metrics.  ( 2 min )
    Your diffusion model secretly knows the dimension of the data manifold. (arXiv:2212.12611v1 [cs.LG])
    In this work, we propose a novel framework for estimating the dimension of the data manifold using a trained diffusion model. A trained diffusion model approximates the gradient of the log density of a noise-corrupted version of the target distribution for varying levels of corruption. If the data concentrates around a manifold embedded in the high-dimensional ambient space, then as the level of corruption decreases, the score function points towards the manifold, as this direction becomes the direction of maximum likelihood increase. Therefore, for small levels of corruption, the diffusion model provides us with access to an approximation of the normal bundle of the data manifold. This allows us to estimate the dimension of the tangent space, thus, the intrinsic dimension of the data manifold. Our method outperforms linear methods for dimensionality detection such as PPCA in controlled experiments.  ( 2 min )
    Adapting to game trees in zero-sum imperfect information games. (arXiv:2212.12567v1 [stat.ML])
    Imperfect information games (IIG) are games in which each player only partially observes the current game state. We study how to learn $\epsilon$-optimal strategies in a zero-sum IIG through self-play with trajectory feedback. We give a problem-independent lower bound $\mathcal{O}(H(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ on the required number of realizations to learn these strategies with high probability, where $H$ is the length of the game, $A_{\mathcal{X}}$ and $B_{\mathcal{Y}}$ are the total number of actions for the two players. We also propose two Follow the Regularize leader (FTRL) algorithms for this setting: Balanced-FTRL which matches this lower bound, but requires the knowledge of the information set structure beforehand to define the regularization; and Adaptive-FTRL which needs $\mathcal{O}(H^2(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ plays without this requirement by progressively adapting the regularization to the observations.  ( 2 min )
    A Convergence Rate for Manifold Neural Networks. (arXiv:2212.12606v1 [cs.LG])
    High-dimensional data arises in numerous applications, and the rapidly developing field of geometric deep learning seeks to develop neural network architectures to analyze such data in non-Euclidean domains, such as graphs and manifolds. Recent work by Z. Wang, L. Ruiz, and A. Ribeiro has introduced a method for constructing manifold neural networks using the spectral decomposition of the Laplace Beltrami operator. Moreover, in this work, the authors provide a numerical scheme for implementing such neural networks when the manifold is unknown and one only has access to finitely many sample points. The authors show that this scheme, which relies upon building a data-driven graph, converges to the continuum limit as the number of sample points tends to infinity. Here, we build upon this result by establishing a rate of convergence that depends on the intrinsic dimension of the manifold but is independent of the ambient dimension. We also discuss how the rate of convergence depends on the depth of the network and the number of filters used in each layer.  ( 2 min )
    Concentration of the Langevin Algorithm's Stationary Distribution. (arXiv:2212.12629v1 [stat.ML])
    A canonical algorithm for log-concave sampling is the Langevin Algorithm, aka the Langevin Diffusion run with some discretization stepsize $\eta > 0$. This discretization leads the Langevin Algorithm to have a stationary distribution $\pi_{\eta}$ which differs from the stationary distribution $\pi$ of the Langevin Diffusion, and it is an important challenge to understand whether the well-known properties of $\pi$ extend to $\pi_{\eta}$. In particular, while concentration properties such as isoperimetry and rapidly decaying tails are classically known for $\pi$, the analogous properties for $\pi_{\eta}$ are open questions with direct algorithmic implications. This note provides a first step in this direction by establishing concentration results for $\pi_{\eta}$ that mirror classical results for $\pi$. Specifically, we show that for any nontrivial stepsize $\eta > 0$, $\pi_{\eta}$ is sub-exponential (respectively, sub-Gaussian) when the potential is convex (respectively, strongly convex). Moreover, the concentration bounds we show are essentially tight. Key to our analysis is the use of a rotation-invariant moment generating function (aka Bessel function) to study the stationary dynamics of the Langevin Algorithm. This technique may be of independent interest because it enables directly analyzing the discrete-time stationary distribution $\pi_{\eta}$ without going through the continuous-time stationary distribution $\pi$ as an intermediary.  ( 2 min )
    Neural Networks beyond explainability: Selective inference for sequence motifs. (arXiv:2212.12542v1 [q-bio.GN])
    Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of deep learning, tools have been devised to extract features such as sequence motifs that can explain the predictions made by a trained network. Here we intend to go beyond explainable machine learning and introduce SEISM, a selective inference procedure to test the association between these extracted features and the predicted phenotype. In particular, we discuss how training a one-layer convolutional network is formally equivalent to selecting motifs maximizing some association score. We adapt existing sampling-based selective inference procedures by quantizing this selection over an infinite set to a large but finite grid. Finally, we show that sampling under a specific choice of parameters is sufficient to characterize the composite null hypothesis typically used for selective inference-a result that goes well beyond our particular framework. We illustrate the behavior of our method in terms of calibration, power and speed and discuss its power/speed trade-off with a simpler data-split strategy. SEISM paves the way to an easier analysis of neural networks used in regulatory genomics, and to more powerful methods for genome wide association studies (GWAS).  ( 2 min )

  • Open

    Which AI program and method was mostly likely used to make eyes just like this?
    submitted by /u/SurpriseTherapy [link] [comments]  ( 51 min )
    I curated some AI tools for 3D modeling, AR, and VR.
    7 AI tools for 3D modeling, AR, and VR. Point-E Kaedim Kinetix Thishousedoesnotexist 5.Dpth Dream Fusion ChatARKit What would you add? submitted by /u/TheVellerShow [link] [comments]  ( 51 min )
    New AI assistant steals fashion shows with its designs
    submitted by /u/Mk_Makanaki [link] [comments]  ( 54 min )
    AI triples stroke recovery in the UK
    In a press release by the NHS, they said “Use of cutting-edge AI technology is associated with tripling of patients recovering and able to perform daily activities from 16% to 48%” Now that's a tri-ing jump, get it? How does it work? I hear you ask The technology analyses the brain CT scans of stroke patients arriving at the hospital, taking less than a minute to identify the type and severity of the stroke and the most appropriate treatment. Doctors can then quickly offer drugs or surgery, with the technology shortening the average time between patients arriving at the hospital and starting treatment by one hour - from 140 minutes to 79 minutes. This one is most definitely a GAME CHANGER, saving time, money, and Lives A massive win for the AI Community. This is from the AI With Vibes Newsletter, read the full issue here: https://aiwithvibes.beehiiv.com/p/openai-dumbing-chatgpt submitted by /u/Mk_Makanaki [link] [comments]  ( 52 min )
    What are some of your favorite AI powered apps/use cases right now? Not ones that you think "oh this is neat" but ones that are genuinely helpful.
    I write a daily newsletter covering things in AI and am trying to find things that everyday people might want to use. Something outside of "this has helped me write code for my new software!" Etc. submitted by /u/LightPoleBoy [link] [comments]  ( 54 min )
    Can AI write good poetry? Putting ChatGPT to the test
    Hello! I approach this topic not as one who is passionately interested in AI as much as I do as someone who loves reading poetry. As such I really just try to evaluate it in its own terms, and hence it may be of some interest for you. I looked at three criteria: the music (how metrically correct it is), the language (the complexity and flair of the language) and finally how much it touches the reader. The article is linked below: https://www.lookingtoleeward.se/2022/12/26/can-ai-write-good-poetry-putting-chatgpt-to-the-test/ submitted by /u/Similar-Movie1663 [link] [comments]  ( 53 min )
    AI Dream 134 - Discovery of Zion Remastered - INCREDIBLE AI ANIMATION
    submitted by /u/LordPewPew777 [link] [comments]  ( 58 min )
    Responding To Sam Does Arts!
    submitted by /u/PuppetHere [link] [comments]  ( 73 min )
    Video Essay on Retroarch's Ai Translation Features (for retro game emulation)
    submitted by /u/anybutton2start [link] [comments]  ( 51 min )
    I built a web app tool to paraphrase, grammar check, and summarize text with OpenAI GPT-3. Details in the comment
    submitted by /u/Austin_Nguyen_2k [link] [comments]  ( 56 min )
    Simulating revolutions - ChatGPT and symbolic simulations
    Simulating revolutions - ChatGPT and symbolic simulations, an article. submitted by /u/goronmask [link] [comments]  ( 54 min )
    What ai should i use to enhance an old blurry picture of me?
    submitted by /u/moe_mel [link] [comments]  ( 51 min )
    Why applied artificial intelligence needs a major mind-shift
    submitted by /u/bendee983 [link] [comments]  ( 57 min )
    What if AI are other human's dreams, and we get the final renders?
    submitted by /u/KaviarNFT [link] [comments]  ( 59 min )
    If anyone needs this...
    submitted by /u/ampankajsharma [link] [comments]  ( 51 min )
    Can you guess the movie from an AI-generated image?
    submitted by /u/xavi160 [link] [comments]  ( 50 min )
    What are your thoughts on Generative AI?
    I recently read this article and thought of using ChatGPT. I've been chatting with ChatGPT all week, bouncing ideas off of it to get it to help me flesh out my thoughts. I found out that these technologies are iterative. One is built on top of the last one, and each new iteration is more powerful and increases the potential for discovery in some exponential way. It's like a whole new level for these machines to grow and improve, and it's opening up all kinds of possibilities for what we might find out. Also, something like this has been going on for a while now like (JasperAI, CopyAI, Copysmith… the list goes on… maybe Google is even going to join the bandwagon with Google Assistant? Who knows). These technologies are also seriously disruptive, like we've never seen before. If you don't believe me, just spend a week chatting with ChatGPT or something similar and see for yourself. It’s obvious that these tools (yes tools) are going to be like a boost to our own creative skills, not to take over or anything, just to make them even better. So for those creative workers out there like copywriters, graphic designers and web designers, instead of worrying that you might get replaced, you can instead use this technology to your own advantage. You can use it for ideas for blog topics. You can also use it for design ideas and templates for your graphics and website. And that’s just the tip of the iceberg. People are worried that these technologies might take the jobs of regular humans because they can help companies get stuff done with less people. But I think it's important to think about how these technologies are affecting us and to make sure they're used in a responsible and helpful way for everyone. But AI is changing fast, so it's tough to say for sure how these technologies will play out in the future. We’ll see in 5-10 years at least how much AI will improve. submitted by /u/According_Complex_74 [link] [comments]  ( 68 min )
    Landscape generator?
    Is there a landscape generator? I'm searching for something like thispersondoesnotexist.com, but one that generates original landscape images. submitted by /u/OwnCranberry4948 [link] [comments]  ( 51 min )
    I participated in the alpha test of AI, which creates various images of a character without losing its consistency of appearance. The result was stunning
    submitted by /u/blbird [link] [comments]  ( 51 min )
    Trippy Eye Animation using SD
    submitted by /u/oridnary_artist [link] [comments]  ( 48 min )
    AI In Education - A Teacher's Perspective
    I teach high school and this is the first time I've encountered anything like this. Multiple students submitting writing assignments that are clearly AI generated. The administration seems to want to punish the students and move on. However, there is a clear learning opportunity here. It's not going to go away. The education piece needs to go beyond "how do we catch them" and "how do we avoid it." W teach students how to use other assistive technologies, why not AI? I know this is a vague and open question, but... What do you think we should be teaching our children around AI writing tech, or ai in general? Any specifics, resources or examples? submitted by /u/benny1872 [link] [comments]  ( 53 min )
  • Open

    [R] PyTorch | Budget GPU Benchmarking
    Greetings! ​ Recently I was asked about a budget AI / ML workload, and decided to test it against some of my own lab GPUs. ​ I'll be adding more tests, and benchmarks over time, but below is a link to my website where I covered it. As well as the code I wrote to benchmark them. ​ Hopefully this helps someone out there. :-) ​ https://www.zb-c.tech/2022/12/26/pytorch-drag-race-tesla-k80-performance/ submitted by /u/zveroboy152 [link] [comments]  ( 65 min )
    [Research] Can you use GANs to boost YOLOv5 object detection dataset?
    I was building a YOLOv5 object detection model, and was looking into researching synthetic methods like GANs to increase the size of my training set in an unsupervised manner. Ik few-shot GANs can be used to "hallucinate" images and labels for a classification task, but how can they be extended to hallucinate images and labels in YOLO format (basically lists out each bounding box and class)? Is there some way that I can train a GAN on images / YOLO labels, and get it to hallucinate more images / labels? submitted by /u/WeAreNebula [link] [comments]  ( 70 min )
    [D] Focused training of AutoEncoder embeddings?
    I am trying to produce an AutoEncoder that has meaningful embeddings for dimensionality reduction. Additionally, I have a specific downstream task I have in mind to use the embeddings for, so I would like to know if it makes sense to write a loss function that considers both the reconstruction accuracy of the AutoEncoder, as well as prediction accuracy for the downstream task. If so, are there any relevant loss functions or articles I should refer to? Thanks! submitted by /u/austinv11 [link] [comments]  ( 66 min )
    [D] Taylor & Francis Article status stuck on pending editor decision for last 4 months?
    Dear fellows, I submitted my article to one of the Taylor & Francis journal in mid-2021. It received a reject and resubmit decision in early 2022. I undertook the major revisions and resubmitted my article in mid-2022. Its status went from under review to pending editor decision on September 2022. However, since then, there has been no update. I tried to contact the chief editor and editor-in-command in the period of last month. However, I have yet to hear from them. My paper has already been significantly delayed, and this uncertain situation worsens my anxiety. What do you think I should do in this case? submitted by /u/HQ2020 [link] [comments]  ( 64 min )
    [P] I built a CLI helper integrating with GPT-3. It enables you to ask questions straight in your terminal
    Hi all! As most of you here, I've played around a bit with CHATGPT, but felt it was annoying to always have to log into their GUI to ask the questions. To scratch my own itch and at the same time learn more about how to write my own command line interface, I created 'askai': https://github.com/maxvfischer/askai It is a simple CLI integration with OpenAI’s GPT3 models. I’ve primarily used it to get quick answers to technical questions, like: askai "How to mock user input when writing a Python pytest test?" askai "How do I remove a conda environment?" As I've found it quite helpful, I decided to spend some time to package it in a nicer way to share it with you. I've also uploaded it to PyPI to simplify the installation process. 'askai' enables you to: Ask questions and get the answers straight into your terminal Configure which model and model parameters you want to use Overwrite saved configurations when you ask questions Currently, it only supports OpenAI’s models, but my plan is to integrate more endpoints as soon as new capable NLP endpoints are popping up. I hope some of you find it useful :) submitted by /u/maktattengil [link] [comments]  ( 64 min )
    [Discussion] 2 discrimination mechanisms that should be provided with powerful generative models e.g. ChatGPT or DALL-E
    In the wake of all the questions and worries about models that can generate content nearing (or exceeding, in some cases) the quality of that made of humans, there are a couple mechanisms that companies should provide alongside their models. Both vary in feasibility, but in general, both are pretty doable, at least for what we've seen so far. A hashing-based system to check whether a given piece of content was generated by the model. This can be accomplished by hashing all of the outputs of the model, and storing them. If it doesn't pose some sort of security risk for the generator, it could also provide the date of generation. A model for discriminating whether a given piece of content was generated by the model, similar to this model for GPT-2. This is necessary in addition to the simpler hashing mechanism, since it's possible for only a portion of the media to be generated. This would be imperfect, of course, but if nothing else, we should press companies enough that they feel obligated to give it a dedicated try. These mechanisms need real support - an API for developers, and a UI for less sophisticated users. They should have decent latency, and be hopefully be provided for free, at some level of usage - I understand the compute required could be enormous. Curious what others think here :) submitted by /u/Exnur0 [link] [comments]  ( 74 min )
    [P] Can you distinguish AI-generated content from real art or literature? I made a little test!
    Hi everyone, I am no programmer, and I have a very basic knowledge of machine learning, but I am fascinated by the possibilities offered by all the new models we have seen so far. Some people around me say they are not that impressed by what AIs can do, so I built a small test (with a little help by chatGPT to code the whole thing): can you always 100% distinguish between AI art or text and old works of art or literature? Here is the site: http://aiorart.com/ I find that AI-generated text is still generally easy to spot, but of course it is very challenging to go against great literary works. AI images can sometimes be truly deceptive. I wonder what you will all think of it... and how all that will evolve in the coming months! PS: The site is very crude (again, I am no programmer!). It works though. submitted by /u/Dicitur [link] [comments]  ( 77 min )
    [D] Has any research been done to counteract the fact that each training datapoint "pulls the model in a different direction", partly undoing learning until shared features emerge?
    I don't remember where I've read about this, but it left a lasting impression on me as it feels intuitively true and impactful - in a manner, the learning on each datapoint pulls the network towards encoding that individual example, relying on stochastic emergence of shared features, which in turn relies on a dataset:model size ratio that prevents overfitting and a balanced dataset. Has there been any research into counteracting this phenomenon, such as more purposeful extraction of features, clever batching schemas, synthetic datapoints or anything else such? submitted by /u/derpderp3200 [link] [comments]  ( 69 min )
  • Open

    Airport abbreviation origins
    It doesn’t take much imagination to understand why DEN is the IATA abbreviation for the Denver airport, but the abbreviation MCO for the Orlando airport is more of a head scratcher. Here is a list of the busiest airports in the US along with a brief indication of the reason behind their abbreviations. Some require […] Airport abbreviation origins first appeared on John D. Cook.  ( 5 min )
    Visually symmetric words
    I recently ran into the following comic strip online: [Update: Thanks to Bryan Cantanzaro for letting me know via the comments that the image above was created by Hannah Hillam. The version I found had had her copyright information edited out. I will replace the image above with a legitimate version shortly.] [Update 2: I’m […] Visually symmetric words first appeared on John D. Cook.  ( 6 min )
  • Open

    3D Artist Zhelong Xu Revives Chinese Relics This Week ‘In the NVIDIA Studio’
    Artist Zhelong Xu, aka Uncle Light, brought to life Blood Moon — a 3D masterpiece combining imagination, craftsmanship and art styles from the Chinese Bronze Age — along with Kirin, a symbol of hope and good fortune, using NVIDIA technologies.  ( 7 min )
    11 Essential Explainers to Keep You in the Know in 2023
    These explainers will give you the scoop on the latest tech developments from AI models to green computing.  ( 4 min )
  • Open

    Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part I
    This is part 1 of a three-part series on the Economics of Ethics. Here’s the problem with the data and AI ethics conversation – if we can’t measure it, then we can’t monitor it, judge it, or change it.  We must find a way to transparently instrument and measure ethics. And that’ll become even more… Read More »Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part I The post Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part I appeared first on Data Science Central.  ( 22 min )
  • Open

    Conference on Robot Learning 2022
    The airplanes on display at the CoRL 2022 banquet. At the end of my last post which belatedly summarized RSS 2022, I mentioned I was also attending CoRL 2022 in a much farther away city: Auckland, New Zealand. That conference has now concluded and I thought it went well. I attended CoRL for a few reasons. I was presenting our recent ToolFlowNet paper, which is one of the major projects that I have worked on during my postdoc. I was part of the inclusion committee at CoRL, so I also got partial funding to attend. The conference is well aligned for my research interests. New Zealand is really nice at this time of the year. Unlike most of my prior conference reports where I write them as blog posts, here I have notes in this Google Doc. I was working on this while at CoRL, and it would take a lot of time to convert these to something that looks nice on the website, and Google Docs might be easier for me to do quick edits if needed. If robot learning is of interest to you, I hope you enjoy these conference notes. See you next year in Atlanta, Georgia, for CoRL 2023.  ( 1 min )
  • Open

    Procgen environments "easy" vs "hard" difficulty - what are they?
    Hello! the procgen environments have "easy" and "hard" mode. https://arxiv.org/pdf/1912.01588.pdf ​ From the paper, this is the only paragraph about "easy" means - that it is a slightly different distribution of levels than "hard". Does anyone know what "easy" precisely means - what kind of distribution of levels is it? Thanks so much in advance! (and happy holidays!) https://preview.redd.it/zj7yczavjd8a1.png?width=653&format=png&auto=webp&s=bc97cc8f20b5c149dba1010f1f1c3c9158c7b4db submitted by /u/sunchipsster [link] [comments]  ( 62 min )
  • Open

    Variants of SGD for Lipschitz Continuous Loss Functions in Low-Precision Environments. (arXiv:2211.04655v2 [math.OC] UPDATED)
    Motivated by neural network training in low-bit floating and fixed-point environments, this work studies the convergence of variants of SGD with computational error. Considering a general stochastic Lipschitz continuous loss function, a novel convergence result to a Clarke stationary point is presented assuming that only an approximation of its stochastic gradient can be computed as well as error in computing the SGD step itself. Different variants of SGD are then tested empirically in a variety of low-precision arithmetic environments, where improved test set accuracy is observed compared to SGD for two image recognition tasks.
    SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos. (arXiv:2206.07764v2 [cs.CV] UPDATED)
    The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision approaches unless explicit instance-level supervision is provided. Slot-based models leveraging motion cues have recently shown great promise in learning to represent, segment, and track objects without direct supervision, but they still fail to scale to complex real-world multi-object videos. In an effort to bridge this gap, we take inspiration from human development and hypothesize that information about scene geometry in the form of depth signals can facilitate object-centric learning. We introduce SAVi++, an object-centric video model which is trained to predict depth signals from a slot-based video representation. By further leveraging best practices for model scaling, we are able to train SAVi++ to segment complex dynamic scenes recorded with moving cameras, containing both static and moving objects of diverse appearance on naturalistic backgrounds, without the need for segmentation supervision. Finally, we demonstrate that by using sparse depth signals obtained from LiDAR, SAVi++ is able to learn emergent object segmentation and tracking from videos in the real-world Waymo Open dataset.
    Parallel Automatic History Matching Algorithm Using Reinforcement Learning. (arXiv:2211.07434v2 [cs.LG] UPDATED)
    Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up.
    Generalization Bounds for Transfer Learning with Pretrained Classifiers. (arXiv:2212.12532v1 [cs.LG])
    We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes. Recent results in the literature show that representations learned by a single classifier over many classes are competitive on few-shot learning problems with representations learned by special-purpose algorithms designed for such problems. We offer an explanation for this phenomenon based on the concept of class-features variability collapse, which refers to the training dynamics of deep classification networks where the feature embeddings of samples belonging to the same class tend to concentrate around their class means. More specifically, we examine the few-shot error of the learned feature map, which is the classification error of the nearest class-center classifier using centers learned from a small number of random samples from each class. Assuming that the classes appearing in the data are selected independently from a distribution, we show that the few-shot error generalizes from the training data to unseen test data, and we provide an upper bound on the expected few-shot error for new classes (selected from the same distribution) using the average few-shot error for the source classes. Additionally, we show that the few-shot error on the training data can be upper bounded using the degree of class-features variability collapse. This suggests that foundation models can provide feature maps that are transferable to new downstream tasks even with limited data available.
    Experiments on Turkish ASR with Self-Supervised Speech Representation Learning. (arXiv:2210.07323v3 [cs.CL] UPDATED)
    While the Turkish language is listed among low-resource languages, literature on Turkish automatic speech recognition (ASR) is relatively old. In this report, we present our findings on Turkish ASR with speech representation learning using HUBERT. We investigate pre-training HUBERT for Turkish with large-scale data curated from online resources. We pre-train our model using 6,500 hours of speech data from YouTube. The results show that the models are not ready for commercial use since they are not robust against disturbances that typically occur in real-world settings such as variations in accents, slang, background noise and interference. We analyze typical errors and the limitations of the models for use in commercial settings.
    Bi-Stride Multi-Scale Graph Neural Network for Mesh-Based Physical Simulation. (arXiv:2210.02573v2 [cs.LG] UPDATED)
    Learning physical systems on unstructured meshes by flat Graph neural networks (GNNs) faces the challenge of modeling the long-range interactions due to the scaling complexity w.r.t. the number of nodes, limiting the generalization under mesh refinement. On regular grids, the convolutional neural networks (CNNs) with a U-net structure can resolve this challenge by efficient stride, pooling, and upsampling operations. Nonetheless, these tools are much less developed for graph neural networks (GNNs), especially when GNNs are employed for learning large-scale mesh-based physics. The challenges arise from the highly irregular meshes and the lack of effective ways to construct the multi-level structure without losing connectivity. Inspired by the bipartite graph determination algorithm, we introduce Bi-Stride Multi-Scale Graph Neural Network (BSMS-GNN) by proposing \textit{bi-stride} as a simple pooling strategy for building the multi-level GNN. \textit{Bi-stride} pools nodes by striding every other BFS frontier; it 1) works robustly on any challenging mesh in the wild, 2) avoids using a mesh generator at coarser levels, 3) avoids the spatial proximity for building coarser levels, and 4) uses non-parametrized aggregating/returning instead of MLPs during pooling and unpooling. Experiments show that our framework significantly outperforms the state-of-the-art method's computational efficiency in representative physics-based simulation cases.
    Robust Learning of Parsimonious Deep Neural Networks. (arXiv:2205.04650v2 [cs.LG] UPDATED)
    We propose a simultaneous learning and pruning algorithm capable of identifying and eliminating irrelevant structures in a neural network during the early stages of training. Thus, the computational cost of subsequent training iterations, besides that of inference, is considerably reduced. Our method, based on variational inference principles using Gaussian scale mixture priors on neural network weights, learns the variational posterior distribution of Bernoulli random variables multiplying the units/filters similarly to adaptive dropout. Our algorithm, ensures that the Bernoulli parameters practically converge to either 0 or 1, establishing a deterministic final network. We analytically derive a novel hyper-prior distribution over the prior parameters that is crucial for their optimal selection and leads to consistent pruning levels and prediction accuracy regardless of weight initialization or the size of the starting network. We prove the convergence properties of our algorithm establishing theoretical and practical pruning conditions. We evaluate the proposed algorithm on the MNIST and CIFAR-10 data sets and the commonly used fully connected and convolutional LeNet and VGG16 architectures. The simulations show that our method achieves pruning levels on par with state-of the-art methods for structured pruning, while maintaining better test-accuracy and more importantly in a manner robust with respect to network initialization and initial size.
    Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification. (arXiv:2209.13519v2 [cs.IR] UPDATED)
    The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers, so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principal investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs for learning representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.
    Optimizing Warfarin Dosing using Deep Reinforcement Learning. (arXiv:2202.03486v3 [cs.LG] UPDATED)
    Warfarin is a widely used anticoagulant, and has a narrow therapeutic range. Dosing of warfarin should be individualized, since slight overdosing or underdosing can have catastrophic or even fatal consequences. Despite much research on warfarin dosing, current dosing protocols do not live up to expectations, especially for patients sensitive to warfarin. We propose a deep reinforcement learning-based dosing model for warfarin. To overcome the issue of relatively small sample sizes in dosing trials, we use a Pharmacokinetic/ Pharmacodynamic (PK/PD) model of warfarin to simulate dose-responses of virtual patients. Applying the proposed algorithm on virtual test patients shows that this model outperforms a set of clinically accepted dosing protocols by a wide margin. We tested the robustness of our dosing protocol on a second PK/PD model and showed that its performance is comparable to the set of baseline protocols.
    Learning Latent Representations to Co-Adapt to Humans. (arXiv:2212.09586v2 [cs.RO] UPDATED)
    When robots interact with humans in homes, roads, or factories the human's behavior often changes in response to the robot. Non-stationary humans are challenging for robot learners: actions the robot has learned to coordinate with the original human may fail after the human adapts to the robot. In this paper we introduce an algorithmic formalism that enables robots (i.e., ego agents) to co-adapt alongside dynamic humans (i.e., other agents) using only the robot's low-level states, actions, and rewards. A core challenge is that humans not only react to the robot's behavior, but the way in which humans react inevitably changes both over time and between users. To deal with this challenge, our insight is that -- instead of building an exact model of the human -- robots can learn and reason over high-level representations of the human's policy and policy dynamics. Applying this insight we develop RILI: Robustly Influencing Latent Intent. RILI first embeds low-level robot observations into predictions of the human's latent strategy and strategy dynamics. Next, RILI harnesses these predictions to select actions that influence the adaptive human towards advantageous, high reward behaviors over repeated interactions. We demonstrate that -- given RILI's measured performance with users sampled from an underlying distribution -- we can probabilistically bound RILI's expected performance across new humans sampled from the same distribution. Our simulated experiments compare RILI to state-of-the-art representation and reinforcement learning baselines, and show that RILI better learns to coordinate with imperfect, noisy, and time-varying agents. Finally, we conduct two user studies where RILI co-adapts alongside actual humans in a game of tag and a tower-building task. See videos of our user studies here: https://youtu.be/WYGO5amDXbQ
    Statistical Efficiency of Score Matching: The View from Isoperimetry. (arXiv:2210.00726v2 [cs.LG] UPDATED)
    Deep generative models parametrized up to a normalizing constant (e.g. energy-based models) are difficult to train by maximizing the likelihood of the data because the likelihood and/or gradients thereof cannot be explicitly or efficiently written down. Score matching is a training method, whereby instead of fitting the likelihood $\log p(x)$ for the training data, we instead fit the score function $\nabla_x \log p(x)$ -- obviating the need to evaluate the partition function. Though this estimator is known to be consistent, its unclear whether (and when) its statistical efficiency is comparable to that of maximum likelihood -- which is known to be (asymptotically) optimal. We initiate this line of inquiry in this paper, and show a tight connection between statistical efficiency of score matching and the isoperimetric properties of the distribution being estimated -- i.e. the Poincar\'e, log-Sobolev and isoperimetric constant -- quantities which govern the mixing time of Markov processes like Langevin dynamics. Roughly, we show that the score matching estimator is statistically comparable to the maximum likelihood when the distribution has a small isoperimetric constant. Conversely, if the distribution has a large isoperimetric constant -- even for simple families of distributions like exponential families with rich enough sufficient statistics -- score matching will be substantially less efficient than maximum likelihood. We suitably formalize these results both in the finite sample regime, and in the asymptotic regime. Finally, we identify a direct parallel in the discrete setting, where we connect the statistical properties of pseudolikelihood estimation with approximate tensorization of entropy and the Glauber dynamics.
    Generate synthetic samples from tabular data. (arXiv:2209.06113v2 [cs.LG] UPDATED)
    Generating new samples from data sets can mitigate extra expensive operations, increased invasive procedures, and mitigate privacy issues. These novel samples that are statistically robust can be used as a temporary and intermediate replacement when privacy is a concern. This method can enable better data sharing practices without problems relating to identification issues or biases that are flaws for an adversarial attack.
    Polysemanticity and Capacity in Neural Networks. (arXiv:2210.01892v2 [cs.NE] UPDATED)
    Individual neurons in neural networks often represent a mixture of unrelated features. This phenomenon, called polysemanticity, can make interpreting neural networks more difficult and so we aim to understand its causes. We propose doing so through the lens of feature \emph{capacity}, which is the fractional dimension each feature consumes in the embedding space. We show that in a toy model the optimal capacity allocation tends to monosemantically represent the most important features, polysemantically represent less important features (in proportion to their impact on the loss), and entirely ignore the least important features. Polysemanticity is more prevalent when the inputs have higher kurtosis or sparsity and more prevalent in some architectures than others. Given an optimal allocation of capacity, we go on to study the geometry of the embedding space. We find a block-semi-orthogonal structure, with differing block sizes in different models, highlighting the impact of model architecture on the interpretability of its neurons.
    Deep learning in a bilateral brain with hemispheric specialization. (arXiv:2209.06862v4 [q-bio.NC] UPDATED)
    The brains of all bilaterally symmetric animals on Earth are are divided into left and right hemispheres. The anatomy and functionality of the hemispheres have a large degree of overlap, but they specialize to possess different attributes. The left hemisphere is believed to specialize in specificity and routine, the right in generalities and novelty. In this study, we propose an artificial neural network that imitates that bilateral architecture using two convolutional neural networks with different training objectives and test it on an image classification task. The bilateral architecture outperforms architectures of similar representational capacity that don't exploit differential specialization. It demonstrates the efficacy of bilateralism and constitutes a new principle that could be incorporated into other computational neuroscientific models and used as an inductive bias when designing new ML systems. An analysis of the model can help us to understand the human brain.
    Anisotropic, Sparse and Interpretable Physics-Informed Neural Networks for PDEs. (arXiv:2207.00377v3 [cs.LG] UPDATED)
    There has been a growing interest in the use of Deep Neural Networks (DNNs) to solve Partial Differential Equations (PDEs). Despite the promise that such approaches hold, there are various aspects where they could be improved. Two such shortcomings are (i) their computational inefficiency relative to classical numerical methods, and (ii) the non-interpretability of a trained DNN model. In this work we present ASPINN, an anisotropic extension of our earlier work called SPINN--Sparse, Physics-informed, and Interpretable Neural Networks--to solve PDEs that addresses both these issues. ASPINNs generalize radial basis function networks. We demonstrate using a variety of examples involving elliptic and hyperbolic PDEs that the special architecture we propose is more efficient than generic DNNs, while at the same time being directly interpretable. Further, they improve upon the SPINN models we proposed earlier in that fewer nodes are require to capture the solution using ASPINN than using SPINN, thanks to the anisotropy of the local zones of influence of each node. The interpretability of ASPINN translates to a ready visualization of their weights and biases, thereby yielding more insight into the nature of the trained model. This in turn provides a systematic procedure to improve the architecture based on the quality of the computed solution. ASPINNs thus serve as an effective bridge between classical numerical algorithms and modern DNN based methods to solve PDEs. In the process, we also streamline the training of ASPINNs into a form that is closer to that of supervised learning algorithms.
    Towards a Solution to Bongard Problems: A Causal Approach. (arXiv:2206.07196v2 [cs.LG] UPDATED)
    Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern ML techniques. In this paper, we propose a new approach in an attempt to not only solve BPs but also extract meaning out of learned representations. This includes the reformulation of the classical BP into a reinforcement learning (RL) setting which will allow the model to gain access to counterfactuals to guide its decisions but also explain its decisions. Since learning meaningful representations in BPs is an essential sub-problem, we further make use of contrastive learning for the extraction of low level features from pixel data. Several experiments have been conducted for analyzing the general BP-RL setup, feature extraction methods and using the best combination for the feature space analysis and its interpretation.
    Can Foundation Models Talk Causality?. (arXiv:2206.10591v2 [cs.AI] UPDATED)
    Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scaling hypothesis, and the others who are worried about the lack of interpretability and reasoning capabilities. By investigating to which extent causal representations might be captured by these large scale language models, we make a humble efforts towards resolving the ongoing philosophical conflicts.
    How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs. (arXiv:2205.10504v2 [cs.SE] UPDATED)
    Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict which static code warnings should not be ignored, we suggest that analysts need to look deeper into their algorithms to find choices that better improve the particulars of their specific problem. Specifically, we show here that effective predictors of such warnings can be created by methods that locally adjust the decision boundary (between actionable warnings and others). These methods yield a new high water-mark for recognizing actionable static code warnings. For eight open-source Java projects (cassandra, jmeter, commons, lucene-solr, maven, ant, tomcat, derby) we achieve perfect test results on 4/8 datasets and, overall, a median AUC (area under the true negatives, true positives curve) of 92%.
    Semantic Information G Theory and Logical Bayesian Inference for Machine Learning. (arXiv:1809.01577v2 [cs.AI] UPDATED)
    An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. A semantic channel in the G theory consists of a group of truth functions or membership functions. In comparison with likelihood functions, Bayesian posteriors, and Logistic functions used by popular methods, membership functions can be more conveniently used as learning functions without the above problem. In Logical Bayesian Inference (LBI), every label's learning is independent. For Multilabel learning, we can directly obtain a group of optimized membership functions from a big enough sample with labels, without preparing different samples for different labels. A group of Channel Matching (CM) algorithms is developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions on a two-dimensional feature space, 2-3 iterations can make mutual information between three classes and three labels surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maximization (EM) algorithm is improved and becomes the CM-EM algorithm, which can outperform the EM algorithm when mixture ratios are imbalanced, or local convergence exists. The CM iteration algorithm needs to combine neural networks for MMI classifications on high-dimensional feature spaces. LBI needs further studies for the unification of statistics and logic.
    Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy. (arXiv:2206.04420v2 [physics.med-ph] UPDATED)
    This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, severely abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.
    Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings. (arXiv:2112.13416v3 [cs.CR] UPDATED)
    Speech emotion recognition (SER) processes speech signals to detect and characterize expressed perceived emotions. Many SER application systems often acquire and transmit speech data collected at the client-side to remote cloud platforms for inference and decision making. However, speech data carry rich information not only about emotions conveyed in vocal expressions, but also other sensitive demographic traits such as gender, age and language background. Consequently, it is desirable for SER systems to have the ability to classify emotion constructs while preventing unintended/improper inferences of sensitive and demographic information. Federated learning (FL) is a distributed machine learning paradigm that coordinates clients to train a model collaboratively without sharing their local data. This training approach appears secure and can improve privacy for SER. However, recent works have demonstrated that FL approaches are still vulnerable to various privacy attacks like reconstruction attacks and membership inference attacks. Although most of these have focused on computer vision applications, such information leakages exist in the SER systems trained using the FL technique. To assess the information leakage of SER systems trained using FL, we propose an attribute inference attack framework that infers sensitive attribute information of the clients from shared gradients or model parameters, corresponding to the FedSGD and the FedAvg training algorithms, respectively. As a use case, we empirically evaluate our approach for predicting the client's gender information using three SER benchmark datasets: IEMOCAP, CREMA-D, and MSP-Improv. We show that the attribute inference attack is achievable for SER systems trained using FL. We further identify that most information leakage possibly comes from the first layer in the SER model.
    A Review of Deep Transfer Learning and Recent Advancements. (arXiv:2201.09679v2 [cs.LG] UPDATED)
    Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two major constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such dependency and costs by reusing an obtained knowledge from a source data/task in training on a target data/task. Most applied DTL techniques are network/model-based approaches. These methods reduce the dependency of deep learning models on extensive training data and drastically decrease training costs. As a result, researchers detected Covid-19 infection on chest X-Rays with high accuracy at the beginning of the pandemic with minimal data using DTL techniques. Also, the training cost reduction makes DTL viable on edge devices with limited resources. Like any new advancement, DTL methods have their own limitations, and a successful transfer depends on some adjustments for different scenarios. In this paper, we review the definition and taxonomy of deep transfer learning and well-known methods. Then we investigate the DTL approaches by reviewing recent applied DTL techniques in the past five years. Further, we review some experimental analyses of DTLs to learn the best practice for applying DTL in different scenarios. Moreover, the limitations of DTLs (catastrophic forgetting dilemma and overly biased pre-trained models) are discussed, along with possible solutions and research trends.
    RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction. (arXiv:2112.11225v2 [physics.chem-ph] UPDATED)
    The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates. As far as we know, this is the first method that uses machine learning to compose reaction templates for retrosynthesis prediction. Besides, we propose an effective reactant candidate scoring model that can capture atom-level transformations, which helps our method outperform previous methods on the USPTO-50K dataset. Experimental results show that our method can produce novel templates for 15 USPTO-50K test reactions that are not covered by training templates. We have released our source implementation.
    Neural network approach to reconstructing spectral functions and complex poles of confined particles. (arXiv:2203.03293v2 [hep-lat] UPDATED)
    Reconstructing spectral functions from propagator data is difficult as solving the analytic continuation problem or applying an inverse integral transformation are ill-conditioned problems. Recent work has proposed using neural networks to solve this problem and has shown promising results, either matching or improving upon the performance of other methods. We generalize this approach by not only reconstructing spectral functions, but also (possible) pairs of complex poles or an infrared (IR) cutoff. We train our network on physically motivated toy functions, examine the reconstruction accuracy and check its robustness to noise. Encouraging results are found on both toy functions and genuine lattice QCD data for the gluon propagator, suggesting that this approach may lead to significant improvements over current state-of-the-art methods.
    Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games. (arXiv:2205.15879v4 [cs.AI] UPDATED)
    Learning to play optimally against any mixture over a diverse set of strategies is of important practical interests in competitive games. In this paper, we propose simplex-NeuPL that satisfies two desiderata simultaneously: i) learning a population of strategically diverse basis policies, represented by a single conditional network; ii) using the same network, learn best-responses to any mixture over the simplex of basis policies. We show that the resulting conditional policies incorporate prior information about their opponents effectively, enabling near optimal returns against arbitrary mixture policies in a game with tractable best-responses. We verify that such policies behave Bayes-optimally under uncertainty and offer insights in using this flexibility at test time. Finally, we offer evidence that learning best-responses to any mixture policies is an effective auxiliary task for strategic exploration, which, by itself, can lead to more performant populations.
    Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding. (arXiv:2105.01187v4 [stat.ME] UPDATED)
    Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish several identification results for different classes of ITRs, exhibiting the trade-off between the risk of making untestable assumptions and the value function improvement in decision making. Based on these results, we propose several classification-based approaches to finding a variety of restricted in-class optimal ITRs and develop their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via an extensive simulation study and one real data application.
    Label-Enhanced Graph Neural Network for Semi-supervised Node Classification. (arXiv:2205.15653v2 [cs.LG] UPDATED)
    Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs solely use the known labels for computing the classification loss at the output. In recent years, several methods have been designed to additionally utilize the labels at the input. One part of the methods augment the node features via concatenating or adding them with the one-hot encodings of labels, while other methods optimize the graph structure by assuming neighboring nodes tend to have the same label. To bring into full play the rich information of labels, in this paper, we present a label-enhanced learning framework for GNNs, which first models each label as a virtual center for intra-class nodes and then jointly learns the representations of both nodes and labels. Our approach could not only smooth the representations of nodes belonging to the same class, but also explicitly encode the label semantics into the learning process of GNNs. Moreover, a training node selection technique is provided to eliminate the potential label leakage issue and guarantee the model generalization ability. Finally, an adaptive self-training strategy is proposed to iteratively enlarge the training set with more reliable pseudo labels and distinguish the importance of each pseudo-labeled node during the model training process. Experimental results on both real-world and synthetic datasets demonstrate our approach can not only consistently outperform the state-of-the-arts, but also effectively smooth the representations of intra-class nodes.
    Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization. (arXiv:2203.16642v2 [math.OC] UPDATED)
    We study iterative methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. We propose a machine-learning-based heuristic to determine starting scenarios that provide strong lower bounds. To this end, we design dimension-independent features and train a Random Forest Classifier on small-dimensional instances. Experiments show that our method improves the solution process for larger instances than contained in the training set and also provides a feature importance-score which gives insights into the role of scenario properties.
    Calibrated Multiple-Output Quantile Regression with Representation Learning. (arXiv:2110.00816v2 [cs.LG] UPDATED)
    We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability. Our work is composed of two components. First, we use a deep generative model to learn a representation of the response that has a unimodal distribution. Existing multiple-output quantile regression approaches are effective in such cases, so we apply them on the learned representation, and then transform the solution to the original space of the response. This process results in a flexible and informative region that can have an arbitrary shape, a property that existing methods lack. Second, we propose an extension of conformal prediction to the multivariate response setting that modifies any method to return sets with a pre-specified coverage level. The desired coverage is theoretically guaranteed in the finite-sample case for any distribution. Experiments conducted on both real and synthetic data show that our method constructs regions that are significantly smaller compared to existing techniques.
    KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Language. (arXiv:2205.02364v2 [cs.CL] UPDATED)
    The need for Question Answering datasets in low resource languages is the motivation of this research, leading to the development of Kencorpus Swahili Question Answering Dataset, KenSwQuAD. This dataset is annotated from raw story texts of Swahili low resource language, which is a predominantly spoken in Eastern African and in other parts of the world. Question Answering (QA) datasets are important for machine comprehension of natural language for tasks such as internet search and dialog systems. Machine learning systems need training data such as the gold standard Question Answering set developed in this research. The research engaged annotators to formulate QA pairs from Swahili texts collected by the Kencorpus project, a Kenyan languages corpus. The project annotated 1,445 texts from the total 2,585 texts with at least 5 QA pairs each, resulting into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5% of the annotated texts confirmed that the QA pairs were all correctly annotated. A proof of concept on applying the set to the QA task confirmed that the dataset can be usable for such tasks. KenSwQuAD has also contributed to resourcing of the Swahili language.
    Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. (arXiv:2212.12474v1 [cs.LG])
    Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, and wave propagation. In practice, specialized numerical methods based on discretization are used to solve PDEs. They generally use an estimate of the unknown model parameters and, if available, physical measurements for initialization. Such solvers are often embedded into larger scientific models or analyses with a downstream application such that error quantification plays a key role. However, by entirely ignoring parameter and measurement uncertainty, classical PDE solvers may fail to produce consistent estimates of their inherent approximation error. In this work, we approach this problem in a principled fashion by interpreting solving linear PDEs as physics-informed Gaussian process (GP) regression. Our framework is based on a key generalization of a widely-applied theorem for conditioning GPs on a finite number of direct observations to observations made via an arbitrary bounded linear operator. Crucially, this probabilistic viewpoint allows to (1) quantify the inherent discretization error; (2) propagate uncertainty about the model parameters to the solution; and (3) condition on noisy measurements. Demonstrating the strength of this formulation, we prove that it strictly generalizes methods of weighted residuals, a central class of PDE solvers including collocation, finite volume, pseudospectral, and (generalized) Galerkin methods such as finite element and spectral methods. This class can thus be directly equipped with a structured error estimate and the capability to incorporate uncertain model parameters and observations. In summary, our results enable the seamless integration of mechanistic models as modular building blocks into probabilistic models.
    A Family of Pairwise Multi-Marginal Optimal Transports that Define a Generalized Metric. (arXiv:2001.11114v6 [cs.LG] UPDATED)
    The Optimal transport (OT) problem is rapidly finding its way into machine learning. Favoring its use are its metric properties. Many problems admit solutions with guarantees only for objects embedded in metric spaces, and the use of non-metrics can complicate solving them. Multi-marginal OT (MMOT) generalizes OT to simultaneously transporting multiple distributions. It captures important relations that are missed if the transport only involves two distributions. Research on MMOT, however, has been focused on its existence, uniqueness, practical algorithms, and the choice of cost functions. There is a lack of discussion on the metric properties of MMOT, which limits its theoretical and practical use. Here, we prove new generalized metric properties for a family of pairwise MMOTs. We first explain the difficulty of proving this via two negative results. Afterward, we prove the MMOTs' metric properties. Finally, we show that the generalized triangle inequality of this family of MMOTs cannot be improved. We illustrate the superiority of our MMOTs over other generalized metrics, and over non-metrics in both synthetic and real tasks.
    Using MM principles to deal with incomplete data in K-means clustering. (arXiv:2212.12379v1 [cs.LG])
    Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, this algorithm suffers from incomplete data, where some samples have missed some of their attributes. To solve this problem, we mainly apply MM principles to restore the symmetry of the data, so that K-means could work well. We give the pseudo-code of the algorithm and use the standard datasets for experimental verification. The source code for the experiments is publicly available in the following link: \url{https://github.com/AliBeikmohammadi/MM-Optimization/blob/main/mini-project/MM%20K-means.ipynb}.
    An Exact Mapping From ReLU Networks to Spiking Neural Networks. (arXiv:2212.12522v1 [cs.NE])
    Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an exact mapping from a network with Rectified Linear Units (ReLUs) to an SNN that fires exactly one spike per neuron. For our constructive proof, we assume that an arbitrary multi-layer ReLU network with or without convolutional layers, batch normalization and max pooling layers was trained to high performance on some training set. Furthermore, we assume that we have access to a representative example of input data used during training and to the exact parameters (weights and biases) of the trained ReLU network. The mapping from deep ReLU networks to SNNs causes zero percent drop in accuracy on CIFAR10, CIFAR100 and the ImageNet-like data sets Places365 and PASS. More generally our work shows that an arbitrary deep ReLU network can be replaced by an energy-efficient single-spike neural network without any loss of performance.
    Disentanglement and Generalization Under Correlation Shifts. (arXiv:2112.14754v2 [cs.LG] UPDATED)
    Correlations between factors of variation are prevalent in real-world data. Exploiting such correlations may increase predictive performance on noisy data; however, often correlations are not robust (e.g., they may change between domains, datasets, or applications) and models that exploit them do not generalize when correlations shift. Disentanglement methods aim to learn representations which capture different factors of variation in latent subspaces. A common approach involves minimizing the mutual information between latent subspaces, such that each encodes a single underlying attribute. However, this fails when attributes are correlated. We solve this problem by enforcing independence between subspaces conditioned on the available attributes, which allows us to remove only dependencies that are not due to the correlation structure present in the training data. We achieve this via an adversarial approach to minimize the conditional mutual information (CMI) between subspaces with respect to categorical variables. We first show theoretically that CMI minimization is a good objective for robust disentanglement on linear problems. We then apply our method on real-world datasets based on MNIST and CelebA, and show that it yields models that are disentangled and robust under correlation shift, including in weakly supervised settings.
    Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation. (arXiv:2208.02058v2 [cs.RO] UPDATED)
    Humans have an extraordinary ability to communicate and read the properties of objects by simply watching them being carried by someone else. This level of communicative skills and interpretation, available to humans, is essential for collaborative robots if they are to interact naturally and effectively. For example, suppose a robot is handing over a fragile object. In that case, the human who receives it should be informed of its fragility in advance, through an immediate and implicit message, i.e., by the direct modulation of the robot's action. This work investigates the perception of object manipulations performed with a communicative intent by two robots with different embodiments (an iCub humanoid robot and a Baxter robot). We designed the robots' movements to communicate carefulness or not during the transportation of objects. We found that not only this feature is correctly perceived by human observers, but it can elicit as well a form of motor adaptation in subsequent human object manipulations. In addition, we get an insight into which motion features may induce to manipulate an object more or less carefully.
    Self-Optimizing Feature Transformation. (arXiv:2209.08044v2 [cs.LG] UPDATED)
    Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality, enhance model generalization, overcome data sparsity, and expand the availability of classic models. Current research focuses on domain knowledge-based feature engineering or learning latent representations; nevertheless, these methods are not entirely automated and cannot produce a traceable and optimal representation space. When rebuilding a feature space for a machine learning task, can these limitations be addressed concurrently? In this extension study, we present a self-optimizing framework for feature transformation. To achieve a better performance, we improved the preliminary work by (1) obtaining an advanced state representation for enabling reinforced agents to comprehend the current feature set better; and (2) resolving Q-value overestimation in reinforced agents for learning unbiased and effective policies. Finally, to make experiments more convincing than the preliminary work, we conclude by adding the outlier detection task with five datasets, evaluating various state representation approaches, and comparing different training strategies. Extensive experiments and case studies show that our work is more effective and superior.
    Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models. (arXiv:2212.12380v1 [cs.LG])
    With more and more data being collected, data-driven modeling methods have been gaining in popularity in recent years. While physically sound, classical gray-box models are often cumbersome to identify and scale, and their accuracy might be hindered by their limited expressiveness. On the other hand, classical black-box methods, typically relying on Neural Networks (NNs) nowadays, often achieve impressive performance, even at scale, by deriving statistical patterns from data. However, they remain completely oblivious to the underlying physical laws, which may lead to potentially catastrophic failures if decisions for real-world physical systems are based on them. Physically Consistent Neural Networks (PCNNs) were recently developed to address these aforementioned issues, ensuring physical consistency while still leveraging NNs to attain state-of-the-art accuracy. In this work, we scale PCNNs to model building temperature dynamics and propose a thorough comparison with classical gray-box and black-box methods. More precisely, we design three distinct PCNN extensions, thereby exemplifying the modularity and flexibility of the architecture, and formally prove their physical consistency. In the presented case study, PCNNs are shown to achieve state-of-the-art accuracy, even outperforming classical NN-based models despite their constrained structure. Our investigations furthermore provide a clear illustration of NNs achieving seemingly good performance while remaining completely physics-agnostic, which can be misleading in practice. While this performance comes at the cost of computational complexity, PCNNs on the other hand show accuracy improvements of 17-35% compared to all other physically consistent methods, paving the way for scalable physically consistent models with state-of-the-art performance.
    HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction. (arXiv:2212.12440v1 [q-bio.BM])
    Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extended-connectivity fingerprints. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets, and also evaluate the performance of HAC-Net on lower-quality data. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction. All of our software is available as open source at https://github.com/gregory-kyro/HAC-Net/.
    FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos. (arXiv:2212.12294v1 [cs.CV])
    Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals. For video representations, however, mapping pixel-wise coordinates to RGB colors has shown relatively low compression performance and slow convergence and inference speed. Frame-wise video representation, which maps a temporal coordinate to its entire frame, has recently emerged as an alternative method to represent videos, improving compression rates and encoding speed. While promising, it has still failed to reach the performance of state-of-the-art video compression algorithms. In this work, we propose FFNeRV, a novel method for incorporating flow information into frame-wise representations to exploit the temporal redundancy across the frames in videos inspired by the standard video codecs. Furthermore, we introduce a fully convolutional architecture, enabled by one-dimensional temporal grids, improving the continuity of spatial features. Experimental results show that FFNeRV yields the best performance for video compression and frame interpolation among the methods using frame-wise representations or neural fields. To reduce the model size even further, we devise a more compact convolutional architecture using the group and pointwise convolutions. With model compression techniques, including quantization-aware training and entropy coding, FFNeRV outperforms widely-used standard video codecs (H.264 and HEVC) and performs on par with state-of-the-art video compression algorithms.
    Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection. (arXiv:2212.12488v1 [cs.IR])
    Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood structure induced by the fixed subgraph. The prominence of GNNLP methods significantly relies on the adhoc subgraph. Since node connectivity in real-world graphs is complex, one shared subgraph is limited for all edges. Thus, the choices of subgraphs should be personalized to different edges. However, performing personalized subgraph selection is nontrivial since the potential selection space grows exponentially to the scale of edges. Besides, the inference edges are not available during training in link prediction scenarios, so the selection process needs to be inductive. To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. Coupling GNNLP models with PS2, we suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs. Comprehensive experiments endorse the effectiveness of our proposed method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our code is publicly available at \url{https://github.com/qiaoyu-tan/PS2}
    Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge. (arXiv:2212.12303v1 [cs.LG])
    Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.
    Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning. (arXiv:2212.12329v1 [eess.SP])
    This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optimum is difficult to find. In the literature, either simple but suboptimal approaches or optimal methods with high complexity and poor scalability are proposed. In contrast, we propose a machine learning framework to approach the global optimum. While the neural network (NN) training takes moderate time, application with the trained model requires very low computational complexity. In particular, we introduce a novel objective function based on stochastic actions to solve the non-convex optimization problem. Besides, we design a dedicated NN architecture for the multi-cell network optimization problems that is permutation-equivariant. It classifies channels according to their roles in the EE computation. In this way, we encode our domain knowledge into the NN design and shed light into the black box of machine learning. Training and testing results show that the proposed method without supervision and with reasonable computational effort achieves an EE close to the global optimum found by the branch-and-bound algorithm. Hence, the proposed approach balances between computational complexity and performance.
    NARS vs. Reinforcement learning: ONA vs. Q-Learning. (arXiv:2212.12517v1 [cs.LG])
    One of the realistic scenarios is taking a sequence of optimal actions to do a task. Reinforcement learning is the most well-known approach to deal with this kind of task in the machine learning community. Finding a suitable alternative could always be an interesting and out-of-the-box matter. Therefore, in this project, we are looking to investigate the capability of NARS and answer the question of whether NARS has the potential to be a substitute for RL or not. Particularly, we are making a comparison between $Q$-Learning and ONA on some environments developed by an Open AI gym. The source code for the experiments is publicly available in the following link: \url{https://github.com/AliBeikmohammadi/OpenNARS-for-Applications/tree/master/misc/Python}.
    The choice of scaling technique matters for classification performance. (arXiv:2212.12343v1 [cs.LG])
    Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model's sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.\footnote{https://github.com/amorimlb/scaling\_matters}
    Statistical Distance Based Deterministic Offspring Selection in SMC Methods. (arXiv:2212.12290v1 [stat.ML])
    Over the years, sequential Monte Carlo (SMC) and, equivalently, particle filter (PF) theory has gained substantial attention from researchers. However, the performance of the resampling methodology, also known as offspring selection, has not advanced recently. We propose two deterministic offspring selection methods, which strive to minimize the Kullback-Leibler (KL) divergence and the total variation (TV) distance, respectively, between the particle distribution prior and subsequent to the offspring selection. By reducing the statistical distance between the selected offspring and the joint distribution, we obtain a heuristic search procedure that performs superior to a maximum likelihood search in precisely those contexts where the latter performs better than an SMC. For SMC and particle Markov chain Monte Carlo (pMCMC), our proposed offspring selection methods always outperform or compare favorably with the two state-of-the-art resampling schemes on two models commonly used as benchmarks from the literature.
    Text classification in shipping industry using unsupervised models and Transformer based supervised models. (arXiv:2212.12407v1 [cs.CL])
    Obtaining labelled data in a particular context could be expensive and time consuming. Although different algorithms, including unsupervised learning, semi-supervised learning, self-learning have been adopted, the performance of text classification varies with context. Given the lack of labelled dataset, we proposed a novel and simple unsupervised text classification model to classify cargo content in international shipping industry using the Standard International Trade Classification (SITC) codes. Our method stems from representing words using pretrained Glove Word Embeddings and finding the most likely label using Cosine Similarity. To compare unsupervised text classification model with supervised classification, we also applied several Transformer models to classify cargo content. Due to lack of training data, the SITC numerical codes and the corresponding textual descriptions were used as training data. A small number of manually labelled cargo content data was used to evaluate the classification performances of the unsupervised classification and the Transformer based supervised classification. The comparison reveals that unsupervised classification significantly outperforms Transformer based supervised classification even after increasing the size of the training dataset by 30%. Lacking training data is a key bottleneck that prohibits deep learning models (such as Transformers) from successful practical applications. Unsupervised classification can provide an alternative efficient and effective method to classify text when there is scarce training data.
    Principled and Efficient Transfer Learning of Deep Models via Neural Collapse. (arXiv:2212.12206v1 [cs.LG])
    With the ever-growing model size and the limited availability of labeled training data, transfer learning has become an increasingly popular approach in many science and engineering domains. For classification problems, this work delves into the mystery of transfer learning through an intriguing phenomenon termed neural collapse (NC), where the last-layer features and classifiers of learned deep networks satisfy: (i) the within-class variability of the features collapses to zero, and (ii) the between-class feature means are maximally and equally separated. Through the lens of NC, our findings for transfer learning are the following: (i) when pre-training models, preventing intra-class variability collapse (to a certain extent) better preserves the intrinsic structures of the input data, so that it leads to better model transferability; (ii) when fine-tuning models on downstream tasks, obtaining features with more NC on downstream data results in better test accuracy on the given task. The above results not only demystify many widely used heuristics in model pre-training (e.g., data augmentation, projection head, self-supervised learning), but also leads to more efficient and principled fine-tuning method on downstream tasks that we demonstrate through extensive experimental results.
    Channel charting based beamforming. (arXiv:2212.12340v1 [cs.NI])
    Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.
    Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration. (arXiv:2210.03484v2 [physics.acc-ph] UPDATED)
    Beam parameter optimization in accelerators involves multiple, sometimes competing objectives. Condensing these individual objectives into a single figure of merit unavoidably results in a bias towards particular outcomes, in absence of prior knowledge often in a non-desired way. Finding an optimal objective definition then requires operators to iterate over many possible objective weights and definitions, a process that can take many times longer than the optimization itself. A more versatile approach is multi-objective optimization, which establishes the trade-off curve or Pareto front between objectives. Here we present the first results on multi-objective Bayesian optimization of a simulated laser-plasma accelerator. We find that multi-objective optimization reaches comparable performance to its single-objective counterparts while allowing for instant evaluation of entirely new objectives. This dramatically reduces the time required to find appropriate objective definitions for new problems. Additionally, our multi-objective, multi-fidelity method reduces the time required for an optimization run by an order of magnitude. It does so by dynamically choosing simulation resolution and box size, requiring fewer slow and expensive simulations as it learns about the Pareto-optimal solutions from fast low-resolution runs. The techniques demonstrated in this paper can easily be translated into many different computational and experimental use cases beyond accelerator optimization.
    Investigation of reinforcement learning for shape optimization of profile extrusion dies. (arXiv:2212.12207v1 [cs.CE])
    Profile extrusion is a continuous production process for manufacturing plastic profiles from molten polymer. Especially interesting is the design of the die, through which the melt is pressed to attain the desired shape. However, due to an inhomogeneous velocity distribution at the die exit or residual stresses inside the extrudate, the final shape of the manufactured part often deviates from the desired one. To avoid these deviations, the shape of the die can be computationally optimized, which has already been investigated in the literature using classical optimization approaches. A new approach in the field of shape optimization is the utilization of Reinforcement Learning (RL) as a learning-based optimization algorithm. RL is based on trial-and-error interactions of an agent with an environment. For each action, the agent is rewarded and informed about the subsequent state of the environment. While not necessarily superior to classical, e.g., gradient-based or evolutionary, optimization algorithms for one single problem, RL techniques are expected to perform especially well when similar optimization tasks are repeated since the agent learns a more general strategy for generating optimal shapes instead of concentrating on just one single problem. In this work, we investigate this approach by applying it to two 2D test cases. The flow-channel geometry can be modified by the RL agent using so-called Free-Form Deformation, a method where the computational mesh is embedded into a transformation spline, which is then manipulated based on the control-point positions. In particular, we investigate the impact of utilizing different agents on the training progress and the potential of wall time saving by utilizing multiple environments during training.
    MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification. (arXiv:2212.12061v1 [cs.CL])
    This article presents a dataset of 10,917 news articles with hierarchical news categories collected between January 1st 2019, and December 31st 2019. We manually labelled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.
    Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising. (arXiv:2212.12533v1 [cs.IR])
    Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side platform (DSP) automatically provides bid price usually relying on the ad impression value estimation and the optimal bid price determination. However, the current bid strategy overlooks large randomness of the user behaviors (e.g., click) and the cost uncertainty caused by the auction competition. In this work, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a specific state and market environment via a sequential decision process. Specifically, we propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learning, which is the first to simultaneously consider estimation uncertainty and the dynamic risk tendency of a DSP. We theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR). Consequently, we propose two instantiations to model risk tendency, including an expert knowledge-based formulation embracing three essential properties and an adaptive learning method based on self-supervised reinforcement learning. We conduct extensive experiments on public datasets and show that the proposed framework outperforms state-of-the-art methods in practical settings.
    Benchmark for Uncertainty & Robustness in Self-Supervised Learning. (arXiv:2212.12411v1 [cs.CV])
    Self-Supervised Learning (SSL) is crucial for real-world applications, especially in data-hungry domains such as healthcare and self-driving cars. In addition to a lack of labeled data, these applications also suffer from distributional shifts. Therefore, an SSL method should provide robust generalization and uncertainty estimation in the test dataset to be considered a reliable model in such high-stakes domains. However, existing approaches often focus on generalization, without evaluating the model's uncertainty. The ability to compare SSL techniques for improving these estimates is therefore critical for research on the reliability of self-supervision models. In this paper, we explore variants of SSL methods, including Jigsaw Puzzles, Context, Rotation, Geometric Transformations Prediction for vision, as well as BERT and GPT for language tasks. We train SSL in auxiliary learning for vision and pre-training for language model, then evaluate the generalization (in-out classification accuracy) and uncertainty (expected calibration error) across different distribution covariate shift datasets, including MNIST-C, CIFAR-10-C, CIFAR-10.1, and MNLI. Our goal is to create a benchmark with outputs from experiments, providing a starting point for new SSL methods in Reliable Machine Learning. All source code to reproduce results is available at https://github.com/hamanhbui/reliable_ssl_baselines.
    Alignment Entropy Regularization. (arXiv:2212.12442v1 [cs.CL])
    Existing training criteria in automatic speech recognition(ASR) permit the model to freely explore more than one time alignments between the feature and label sequences. In this paper, we use entropy to measure a model's uncertainty, i.e. how it chooses to distribute the probability mass over the set of allowed alignments. Furthermore, we evaluate the effect of entropy regularization in encouraging the model to distribute the probability mass only on a smaller subset of allowed alignments. Experiments show that entropy regularization enables a much simpler decoding method without sacrificing word error rate, and provides better time alignment quality.
    Relational Local Explanations. (arXiv:2212.12374v1 [cs.LG])
    The majority of existing post-hoc explanation approaches for machine learning models produce independent per-variable feature attribution scores, ignoring a critical characteristic, such as the inter-variable relationship between features that naturally occurs in visual and textual data. In response, we develop a novel model-agnostic and permutation-based feature attribution algorithm based on the relational analysis between input variables. As a result, we are able to gain a broader insight into machine learning model decisions and data. This type of local explanation measures the effects of interrelationships between local features, which provides another critical aspect of explanations. Experimental evaluations of our framework using setups involving both image and text data modalities demonstrate its effectiveness and validity.
    A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. (arXiv:2212.12393v1 [cs.LG])
    We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perform exponential-time exact inference, limiting the scalability of PNL solutions. We introduce Approximate Neurosymbolic Inference (A-NeSI): a new framework for PNL that uses neural networks for scalable approximate inference. A-NeSI 1) performs approximate inference in polynomial time without changing the semantics of probabilistic logics; 2) is trained using data generated by the background knowledge; 3) can generate symbolic explanations of predictions; and 4) can guarantee the satisfaction of logical constraints at test time, which is vital in safety-critical applications. Our experiments show that A-NeSI is the first end-to-end method to scale the Multi-digit MNISTAdd benchmark to sums of 15 MNIST digits, up from 4 in competing systems. Finally, our experiments show that A-NeSI achieves explainability and safety without a penalty in performance.
    Networked Federated Learning. (arXiv:2105.12769v3 [cs.LG] UPDATED)
    We develop the theory and algorithmic toolbox for networked federated learning in decentralized collections of local datasets with an intrinsic network structure. This network structure arises from domain-specific notions of similarity between local datasets. Different notions of similarity are induced by spatio-temporal proximity, statistical dependencies or functional relations. Our main conceptual contribution is to formulate networked federated learning using a generalized total variation minimization. This formulation unifies and considerably extends existing federated multi-task learning methods. It is highly flexible and can be combined with a broad range of parametric models including Lasso or deep neural networks. Our main algorithmic contribution is a novel networked federated learning algorithm which is well-suited for distributed computing environments such as edge computing over wireless networks. This algorithm is robust against inexact computations due to limited computational resources. For local models resulting in convex problems, we derive precise conditions on the local models and their network structure such that our algorithm learns nearly optimal local models. Our analysis reveals an interesting interplay between the convex geometry of local models and the (cluster-) geometry of their network structure.
    Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal. (arXiv:2212.12190v1 [cs.LG])
    Real estate appraisal is a crucial issue for urban applications, which aims to value the properties on the market. Traditional methods perform appraisal based on the domain knowledge, but suffer from the efforts of hand-crafted design. Recently, several methods have been developed to automatize the valuation process by taking the property trading transaction into account when estimating the property value. However, existing methods only consider the real estate itself, ignoring the relation between the properties. Moreover, naively aggregating the information of neighbors fails to model the relationships between the transactions. To tackle these limitations, we propose a novel Neighbor Relation Graph Learning Framework (ReGram) by incorporating the relation between target transaction and surrounding neighbors with the attention mechanism. To model the influence between communities, we integrate the environmental information and the past price of each transaction from other communities. Moreover, since the target transactions in different regions share some similarities and differences of characteristics, we introduce a dynamic adapter to model the different distributions of the target transactions based on the input-related kernel weights. Extensive experiments on the real-world dataset with various scenarios demonstrate that ReGram robustly outperforms the state-of-the-art methods. Furthermore, comprehensive ablation studies were conducted to examine the effectiveness of each component in ReGram.
    Stop using the elbow criterion for k-means and how to choose the number of clusters instead. (arXiv:2212.12189v1 [stat.ML])
    A major challenge when using k-means clustering often is how to choose the parameter k, the number of clusters. In this letter, we want to point out that it is very easy to draw poor conclusions from a common heuristic, the "elbow method". Better alternatives have been known in literature for a long time, and we want to draw attention to some of these easy to use options, that often perform better. This letter is a call to stop using the elbow method altogether, because it severely lacks theoretic support, and we want to encourage educators to discuss the problems of the method -- if introducing it in class at all -- and teach alternatives instead, while researchers and reviewers should reject conclusions drawn from the elbow method.
    Infrared Image Super-Resolution: Systematic Review, and Future Trends. (arXiv:2212.12322v1 [eess.IV])
    Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the development of deep learning. This survey aims to provide a comprehensive perspective of IR image super-resolution, including its applications, hardware imaging system dilemmas, and taxonomy of image processing methodologies. In addition, the datasets and evaluation metrics in IR image super-resolution tasks are also discussed. Furthermore, the deficiencies in current technologies and possible promising directions for the community to explore are highlighted. To cope with the rapid development in this field, we intend to regularly update the relevant excellent work at \url{https://github.com/yongsongH/Infrared_Image_SR_Survey  ( 2 min )
    On Calibrating Semantic Segmentation Models: Analysis and An Algorithm. (arXiv:2212.12053v1 [cs.CV])
    We study the problem of semantic segmentation calibration. For image classification, lots of existing solutions are proposed to alleviate model miscalibration of confidence. However, to date, confidence calibration research on semantic segmentation is still limited. We provide a systematic study on the calibration of semantic segmentation models and propose a simple yet effective approach. First, we find that model capacity, crop size, multi-scale testing, and prediction correctness have impact on calibration. Among them, prediction correctness, especially misprediction, is more important to miscalibration due to over-confidence. Next, we propose a simple, unifying, and effective approach, namely selective scaling, by separating correct/incorrect prediction for scaling and more focusing on misprediction logit smoothing. Then, we study popular existing calibration methods and compare them with selective scaling on semantic segmentation calibration. We conduct extensive experiments with a variety of benchmarks on both in-domain and domain-shift calibration, and show that selective scaling consistently outperforms other methods.  ( 2 min )
    Exploring the Optimized Value of Each Hyperparameter in Various Gradient Descent Algorithms. (arXiv:2212.12279v1 [cs.LG])
    In the recent years, various gradient descent algorithms including the methods of gradient descent, gradient descent with momentum, adaptive gradient (AdaGrad), root-mean-square propagation (RMSProp) and adaptive moment estimation (Adam) have been applied to the parameter optimization of several deep learning models with higher accuracies or lower errors. These optimization algorithms may need to set the values of several hyperparameters which include a learning rate, momentum coefficients, etc. Furthermore, the convergence speed and solution accuracy may be influenced by the values of hyperparameters. Therefore, this study proposes an analytical framework to use mathematical models for analyzing the mean error of each objective function based on various gradient descent algorithms. Moreover, the suitable value of each hyperparameter could be determined by minimizing the mean error. The principles of hyperparameter value setting have been generalized based on analysis results for model optimization. The experimental results show that higher efficiency convergences and lower errors can be obtained by the proposed method.  ( 2 min )
    DAS: Neural Architecture Search via Distinguishing Activation Score. (arXiv:2212.12132v1 [cs.LG])
    Neural Architecture Search (NAS) is an automatic technique that can search for well-performed architectures for a specific task. Although NAS surpasses human-designed architecture in many fields, the high computational cost of architecture evaluation it requires hinders its development. A feasible solution is to directly evaluate some metrics in the initial stage of the architecture without any training. NAS without training (WOT) score is such a metric, which estimates the final trained accuracy of the architecture through the ability to distinguish different inputs in the activation layer. However, WOT score is not an atomic metric, meaning that it does not represent a fundamental indicator of the architecture. The contributions of this paper are in three folds. First, we decouple WOT into two atomic metrics which represent the distinguishing ability of the network and the number of activation units, and explore better combination rules named (Distinguishing Activation Score) DAS. We prove the correctness of decoupling theoretically and confirmed the effectiveness of the rules experimentally. Second, in order to improve the prediction accuracy of DAS to meet practical search requirements, we propose a fast training strategy. When DAS is used in combination with the fast training strategy, it yields more improvements. Third, we propose a dataset called Darts-training-bench (DTB), which fills the gap that no training states of architecture in existing datasets. Our proposed method has 1.04$\times$ - 1.56$\times$ improvements on NAS-Bench-101, Network Design Spaces, and the proposed DTB.  ( 2 min )
    Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations. (arXiv:2212.12345v1 [cs.LG])
    Networks have become indispensable and ubiquitous structures in many fields to model the interactions among different entities, such as friendship in social networks or protein interactions in biological graphs. A major challenge is to understand the structure and dynamics of these systems. Although networks evolve through time, most existing graph representation learning methods target only static networks. Whereas approaches have been developed for the modeling of dynamic networks, there is a lack of efficient continuous time dynamic graph representation learning methods that can provide accurate network characterization and visualization in low dimensions while explicitly accounting for prominent network characteristics such as homophily and transitivity. In this paper, we propose the Piecewise-Velocity Model (PiVeM) for the representation of continuous-time dynamic networks. It learns dynamic embeddings in which the temporal evolution of nodes is approximated by piecewise linear interpolations based on a latent distance model with piecewise constant node-specific velocities. The model allows for analytically tractable expressions of the associated Poisson process likelihood with scalable inference invariant to the number of events. We further impose a scalable Kronecker structured Gaussian Process prior to the dynamics accounting for community structure, temporal smoothness, and disentangled (uncorrelated) latent embedding dimensions optimally learned to characterize the network dynamics. We show that PiVeM can successfully represent network structure and dynamics in ultra-low two-dimensional spaces. It outperforms relevant state-of-art methods in downstream tasks such as link prediction. In summary, PiVeM enables easily interpretable dynamic network visualizations and characterizations that can further improve our understanding of the intrinsic dynamics of time-evolving networks.  ( 2 min )
    Rule Learning by Modularity. (arXiv:2212.12335v1 [cs.LG])
    In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherungs-Aktiengesellschaft which is an insurance company offering diverse services in Germany.  ( 2 min )
    Do DALL-E and Flamingo Understand Each Other?. (arXiv:2212.12249v1 [cs.CV])
    A major goal of multimodal research is to improve machine understanding of images and text. Tasks include image captioning, text-to-image generation, and vision-language representation learning. So far, research has focused on the relationships between images and text. For example, captioning models attempt to understand the semantics of images which are then transformed into text. An important question is: which annotation reflects best a deep understanding of image content? Similarly, given a text, what is the best image that can present the semantics of the text? In this work, we argue that the best text or caption for a given image is the text which would generate the image which is the most similar to that image. Likewise, the best image for a given text is the image that results in the caption which is best aligned with the original text. To this end, we propose a unified framework that includes both a text-to-image generative model and an image-to-text generative model. Extensive experiments validate our approach.  ( 2 min )
    Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks. (arXiv:2212.12121v1 [cs.LG])
    In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices' computing resources compromise the practical effectiveness of PCA. We propose a federated PCA-based Grassmannian optimization framework that coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices' traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and the absence of detecting latency using limited computational resources. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches. Finally, we show that the Grassmann manifold algorithm is highly adapted for IoT anomaly detection, which permits drastically reducing the analysis time of the system. To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.  ( 2 min )
    Deep Unfolding-based Weighted Averaging for Federated Learning under Heterogeneous Environments. (arXiv:2212.12191v1 [cs.LG])
    Federated learning is a collaborative model training method by iterating model updates at multiple clients and aggregation of the updates at a central server. Device and statistical heterogeneity of the participating clients cause performance degradation so that an appropriate weight should be assigned per client in the server's aggregation phase. This paper employs deep unfolding to learn the weights that adapt to the heterogeneity, which gives the model with high accuracy on uniform test data. The results of numerical experiments indicate the high performance of the proposed method and the interpretable behavior of the learned weights.  ( 2 min )
    Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information. (arXiv:2212.12167v1 [stat.ML])
    Motivated by the human-machine interaction such as training chatbots for improving customer satisfaction, we study human-guided human-machine interaction involving private information. We model this interaction as a two-player turn-based game, where one player (Alice, a human) guides the other player (Bob, a machine) towards a common goal. Specifically, we focus on offline reinforcement learning (RL) in this game, where the goal is to find a policy pair for Alice and Bob that maximizes their expected total rewards based on an offline dataset collected a priori. The offline setting presents two challenges: (i) We cannot collect Bob's private information, leading to a confounding bias when using standard RL methods, and (ii) a distributional mismatch between the behavior policy used to collect data and the desired policy we aim to learn. To tackle the confounding bias, we treat Bob's previous action as an instrumental variable for Alice's current decision making so as to adjust for the unmeasured confounding. We develop a novel identification result and use it to propose a new off-policy evaluation (OPE) method for evaluating policy pairs in this two-player turn-based game. To tackle the distributional mismatch, we leverage the idea of pessimism and use our OPE method to develop an off-policy learning algorithm for finding a desirable policy pair for both Alice and Bob. Finally, we prove that under mild assumptions such as partial coverage of the offline data, the policy pair obtained through our method converges to the optimal one at a satisfactory rate.  ( 2 min )
    Anomaly Detection using Ensemble Classification and Evidence Theory. (arXiv:2212.12092v1 [cs.LG])
    Multi-class ensemble classification remains a popular focus of investigation within the research community. The popularization of cloud services has sped up their adoption due to the ease of deploying large-scale machine-learning models. It has also drawn the attention of the industrial sector because of its ability to identify common problems in production. However, there are challenges to conform an ensemble classifier, namely a proper selection and effective training of the pool of classifiers, the definition of a proper architecture for multi-class classification, and uncertainty quantification of the ensemble classifier. The robustness and effectiveness of the ensemble classifier lie in the selection of the pool of classifiers, as well as in the learning process. Hence, the selection and the training procedure of the pool of classifiers play a crucial role. An (ensemble) classifier learns to detect the classes that were used during the supervised training. However, when injecting data with unknown conditions, the trained classifier will intend to predict the classes learned during the training. To this end, the uncertainty of the individual and ensemble classifier could be used to assess the learning capability. We present a novel approach for novel detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We present an architecture for multi-class ensemble classification and an approach to quantify the uncertainty of the individual classifiers and the ensemble classifier. We use uncertainty for the anomaly detection approach. Finally, we use the benchmark Tennessee Eastman to perform experiments to test the ensemble classifier's prediction and anomaly detection capabilities.  ( 2 min )
    Predicting Survival of Tongue Cancer Patients by Machine Learning Models. (arXiv:2212.12114v1 [q-bio.QM])
    Tongue cancer is a common oral cavity malignancy that originates in the mouth and throat. Much effort has been invested in improving its diagnosis, treatment, and management. Surgical removal, chemotherapy, and radiation therapy remain the major treatment for tongue cancer. The survival of patients determines the treatment effect. Previous studies have identified certain survival and risk factors based on descriptive statistics, ignoring the complex, nonlinear relationship among clinical and demographic variables. In this study, we utilize five cutting-edge machine learning models and clinical data to predict the survival of tongue cancer patients after treatment. Five-fold cross-validation, bootstrap analysis, and permutation feature importance are applied to estimate and interpret model performance. The prognostic factors identified by our method are consistent with previous clinical studies. Our method is accurate, interpretable, and thus useable as additional evidence in tongue cancer treatment and management.  ( 2 min )
    A Topic Modeling Approach to Classifying Open Street Map Health Clinics and Schools in Sub-Saharan Africa. (arXiv:2212.12084v1 [cs.LG])
    Data deprivation, or the lack of easily available and actionable information on the well-being of individuals, is a significant challenge for the developing world and an impediment to the design and operationalization of policies intended to alleviate poverty. In this paper we explore the suitability of data derived from OpenStreetMap to proxy for the location of two crucial public services: schools and health clinics. Thanks to the efforts of thousands of digital humanitarians, online mapping repositories such as OpenStreetMap contain millions of records on buildings and other structures, delineating both their location and often their use. Unfortunately much of this data is locked in complex, unstructured text rendering it seemingly unsuitable for classifying schools or clinics. We apply a scalable, unsupervised learning method to unlabeled OpenStreetMap building data to extract the location of schools and health clinics in ten countries in Africa. We find the topic modeling approach greatly improves performance versus reliance on structured keys alone. We validate our results by comparing schools and clinics identified by our OSM method versus those identified by the WHO, and describe OSM coverage gaps more broadly.  ( 2 min )
    Eigenvalue initialisation and regularisation for Koopman autoencoders. (arXiv:2212.12086v1 [cs.LG])
    Regularising the parameter matrices of neural networks is ubiquitous in training deep models. Typical regularisation approaches suggest initialising weights using small random values, and to penalise weights to promote sparsity. However, these widely used techniques may be less effective in certain scenarios. Here, we study the Koopman autoencoder model which includes an encoder, a Koopman operator layer, and a decoder. These models have been designed and dedicated to tackle physics-related problems with interpretable dynamics and an ability to incorporate physics-related constraints. However, the majority of existing work employs standard regularisation practices. In our work, we take a step toward augmenting Koopman autoencoders with initialisation and penalty schemes tailored for physics-related settings. Specifically, we propose the "eigeninit" initialisation scheme that samples initial Koopman operators from specific eigenvalue distributions. In addition, we suggest the "eigenloss" penalty scheme that penalises the eigenvalues of the Koopman operator during training. We demonstrate the utility of these schemes on two synthetic data sets: a driven pendulum and flow past a cylinder; and two real-world problems: ocean surface temperatures and cyclone wind fields. We find on these datasets that eigenloss and eigeninit improves the convergence rate by up to a factor of 5, and that they reduce the cumulative long-term prediction error by up to a factor of 3. Such a finding points to the utility of incorporating similar schemes as an inductive bias in other physics-related deep learning approaches.  ( 2 min )
    RouteNet-Fermi: Network Modeling with Graph Neural Networks. (arXiv:2212.12070v1 [cs.NI])
    Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such as the assumption of markovian traffic in queuing theory models, or the high computational cost of network simulators. Recent advances in machine learning, such as Graph Neural Networks (GNN), are enabling a new generation of network models that are data-driven and can learn complex non-linear behaviors. In this paper, we present RouteNet-Fermi, a custom GNN model that shares the same goals as queuing theory, while being considerably more accurate in the presence of realistic traffic models. The proposed model predicts accurately the delay, jitter, and loss in networks. We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e.g., with complex non-markovian models -- and arbitrary routing and queue scheduling configurations. Our experimental results show that RouteNet-Fermi achieves similar accuracy as computationally-expensive packet-level simulators and it is able to accurately scale to large networks. For example, the model produces delay estimates with a mean relative error of 6.24% when applied to a test dataset with 1,000 samples, including network topologies one order of magnitude larger than those seen during training.  ( 2 min )
    Autothrottle: A Practical Framework for Harvesting CPUs from SLO-Targeted Microservices. (arXiv:2212.12180v1 [cs.DC])
    As the number of distributed services (or microservices) of cloud-native applications grows, resource management becomes a challenging task. These applications tend to be user-facing and latency-sensitive, and our goal is to continuously minimize the amount of CPU resources allocated while still satisfying the application latency SLO. Although previous efforts have proposed simple heuristics and sophisticated ML-based techniques, we believe that a practical resource manager should accurately scale CPU resources for diverse applications, with minimum human efforts and operation overheads. To this end, we ask: can we systematically break resource management down to subproblems solvable by practical policies? Based on the notion of CPU-throttle-based performance target, we decouple the mechanisms of SLO feedback and resource control, and implement a two-level framework -- Autothrottle. It combines a lightweight learned controller at the global level, and agile per-microservice controllers at the local level. We evaluate Autothrottle on three microservice applications, with both short-term and 21-day production workload traces. Empirical results show Autothrottle's superior CPU core savings up to 26.21% over the best-performing baselines across applications, while maintaining the latency SLO.  ( 2 min )
    Semantically-consistent Landsat 8 image to Sentinel-2 image translation for alpine areas. (arXiv:2212.12056v1 [cs.CV])
    The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.  ( 2 min )
    Graph Federated Learning with Hidden Representation Sharing. (arXiv:2212.12158v1 [cs.LG])
    Learning on Graphs (LoG) is widely used in multi-client systems when each client has insufficient local data, and multiple clients have to share their raw data to learn a model of good quality. One scenario is to recommend items to clients with limited historical data and sharing similar preferences with other clients in a social network. On the other hand, due to the increasing demands for the protection of clients' data privacy, Federated Learning (FL) has been widely adopted: FL requires models to be trained in a multi-client system and restricts sharing of raw data among clients. The underlying potential data-sharing conflict between LoG and FL is under-explored and how to benefit from both sides is a promising problem. In this work, we first formulate the Graph Federated Learning (GFL) problem that unifies LoG and FL in multi-client systems and then propose sharing hidden representation instead of the raw data of neighbors to protect data privacy as a solution. To overcome the biased gradient problem in GFL, we provide a gradient estimation method and its convergence analysis under the non-convex objective. In experiments, we evaluate our method in classification tasks on graphs. Our experiment shows a good match between our theory and the practice.  ( 2 min )
    Bengali Handwritten Digit Recognition using CNN with Explainable AI. (arXiv:2212.12146v1 [cs.CV])
    Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily understand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.  ( 2 min )
    The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. (arXiv:2212.12147v1 [stat.ML])
    For small training set sizes $P$, the generalization error of wide neural networks is well-approximated by the error of an infinite width neural network (NN), either in the kernel or mean-field/feature-learning regime. However, after a critical sample size $P^*$, we empirically find the finite-width network generalization becomes worse than that of the infinite width network. In this work, we empirically study the transition from infinite-width behavior to this variance limited regime as a function of sample size $P$ and network width $N$. We find that finite-size effects can become relevant for very small dataset sizes on the order of $P^* \sim \sqrt{N}$ for polynomial regression with ReLU networks. We discuss the source of these effects using an argument based on the variance of the NN's final neural tangent kernel (NTK). This transition can be pushed to larger $P$ by enhancing feature learning or by ensemble averaging the networks. We find that the learning curve for regression with the final NTK is an accurate approximation of the NN learning curve. Using this, we provide a toy model which also exhibits $P^* \sim \sqrt{N}$ scaling and has $P$-dependent benefits from feature learning.  ( 2 min )
    Benchmarking Machine Learning Models to Predict Corporate Bankruptcy. (arXiv:2212.12051v1 [q-fin.CP])
    Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U.S. firms. We find that gradient boosted trees outperform other models in one-year-ahead forecasts. Variable permutation tests show that excess stock returns, idiosyncratic risk, and relative size are the more important variables for predictions. Textual features derived from corporate filings do not improve performance materially. In a credit competition model that accounts for the asymmetric cost of default misclassification, the survival random forest is able to capture large dollar profits.  ( 2 min )
    Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm. (arXiv:2212.12028v1 [stat.ML])
    Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient's entire life. One challenge is that an individual's disease course involves non-terminal (e.g., disease progression) and terminal (e.g., death) events, which form semi-competing relationships. Our motivation comes from the Boston Lung Cancer Study, a large lung cancer survival cohort, which investigates how risk factors influence a patient's disease trajectory. Following developments in the prediction of time-to-event outcomes with neural networks, deep learning has become a focal area for the development of risk prediction methods in survival analysis. However, limited work has been done to predict multi-state or semi-competing risk outcomes, where a patient may experience adverse events such as disease progression prior to death. We propose a novel neural expectation-maximization algorithm to bridge the gap between classical statistical approaches and machine learning. Our algorithm enables estimation of the non-parametric baseline hazards of each state transition, risk functions of predictors, and the degree of dependence among different transitions, via a multi-task deep neural network with transition-specific sub-architectures. We apply our method to the Boston Lung Cancer Study and investigate the impact of clinical and genetic predictors on disease progression and mortality.  ( 2 min )
    Langevin algorithms for Markovian Neural Networks and Deep Stochastic control. (arXiv:2212.12018v1 [q-fin.CP])
    Stochastic Gradient Descent Langevin Dynamics (SGLD) algorithms, which add noise to the classic gradient descent, are known to improve the training of neural networks in some cases where the neural network is very deep. In this paper we study the possibilities of training acceleration for the numerical resolution of stochastic control problems through gradient descent, where the control is parametrized by a neural network. If the control is applied at many discretization times then solving the stochastic control problem reduces to minimizing the loss of a very deep neural network. We numerically show that Langevin algorithms improve the training on various stochastic control problems like hedging and resource management, and for different choices of gradient descent methods.  ( 2 min )
    Deep learning for size-agnostic inverse design of random-network 3D printed mechanical metamaterials. (arXiv:2212.12047v1 [physics.app-ph])
    Practical applications of mechanical metamaterials often involve solving inverse problems where the objective is to find the (multiple) microarchitectures that give rise to a given set of properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific sizes. One should, therefore, find multiple microarchitectural designs that exhibit the desired properties for a specimen with given dimensions. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture, meaning that peak stresses should be minimized as well. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, we propose a modular approach titled 'Deep-DRAM' that combines four decoupled models, including two deep learning models (DLM), a deep generative model (DGM) based on conditional variational autoencoders (CVAE), and direct finite element (FE) simulations. Deep-DRAM (deep learning for the design of random-network metamaterials) integrates these models into a unified framework capable of finding many solutions to the multi-objective inverse design problem posed here. The integrated framework first introduces the desired elastic properties to the DGM, which returns a set of candidate designs. The candidate designs, together with the target specimen dimensions are then passed to the DLM which predicts their actual elastic properties considering the specimen size. After a filtering step based on the closeness of the actual properties to the desired ones, the last step uses direct FE simulations to identify the designs with the minimum peak stresses.  ( 2 min )
    Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models. (arXiv:2212.12067v1 [cs.AI])
    Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions? Findings: In this prognostic study of 6.8 million patients, our denoising sequence-to-sequence prediction model of multiple outcomes outperformed state-of-the-art models scuh pretrained BERT on a broad range of patient outcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep bidirectional and autoregressive representation improves patient outcome prediction.  ( 2 min )
    Graph Learning with Localized Neighborhood Fairness. (arXiv:2212.12040v1 [cs.SI])
    Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.  ( 2 min )
    When are Lemons Purple? The Concept Association Bias of CLIP. (arXiv:2212.12043v1 [cs.CV])
    Large-scale vision-language models such as CLIP have shown impressive performance on zero-shot image classification and image-to-text retrieval. However, such zero-shot performance of CLIP-based models does not realize in tasks that require a finer-grained correspondence between vision and language, such as Visual Question Answering (VQA). We investigate why this is the case, and report an interesting phenomenon of CLIP, which we call the Concept Association Bias (CAB), as a potential cause of the difficulty of applying CLIP to VQA and similar tasks. CAB is especially apparent when two concepts are present in the given image while a text prompt only contains a single concept. In such a case, we find that CLIP tends to treat input as a bag of concepts and attempts to fill in the other missing concept crossmodally, leading to an unexpected zero-shot prediction. For example, when asked for the color of a lemon in an image, CLIP predicts ``purple'' if the image contains a lemon and an eggplant. We demonstrate the Concept Association Bias of CLIP by showing that CLIP's zero-shot classification performance greatly suffers when there is a strong concept association between an object (e.g. lemon) and an attribute (e.g. its color). On the other hand, when the association between object and attribute is weak, we do not see this phenomenon. Furthermore, we show that CAB is significantly mitigated when we enable CLIP to learn deeper structure across image and text embeddings by adding an additional Transformer on top of CLIP and fine-tuning it on VQA. We find that across such fine-tuned variants of CLIP, the strength of CAB in a model predicts how well it performs on VQA.  ( 2 min )
    A comprehensive analysis of the Elo rating algorithm: Stochastic model, convergence characteristics, design guidelines, and experimental results. (arXiv:2212.12015v1 [cs.LG])
    The Elo algorithm, due to its simplicity, is widely used for rating in sports competitions as well as in other applications where the rating/ranking is a useful tool for predicting future results. However, despite its widespread use, a detailed understanding of the convergence properties of the Elo algorithm is still lacking. Aiming to fill this gap, this paper presents a comprehensive (stochastic) analysis of the Elo algorithm, considering round-robin (one-on-one) competitions. Specifically, analytical expressions are derived characterizing the behavior/evolution of the skills and of important performance metrics. Then, taking into account the relationship between the behavior of the algorithm and the step-size value, which is a hyperparameter that can be controlled, some design guidelines as well as discussions about the performance of the algorithm are provided. To illustrate the applicability of the theoretical findings, experimental results are shown, corroborating the very good match between analytical predictions and those obtained from the algorithm using real-world data (from the Italian SuperLega, Volleyball League).  ( 2 min )
    ML-powered KQI estimation for XR services. A case study on 360-Video. (arXiv:2212.12002v1 [cs.NI])
    The arise of cutting-edge technologies and services such as XR promise to change the concepts of how day-to-day things are done. At the same time, the appearance of modern and decentralized architectures approaches has given birth to a new generation of mobile networks such as 5G, as well as outlining the roadmap for B5G and posterior. These networks are expected to be the enablers for bringing to life the Metaverse and other futuristic approaches. In this sense, this work presents an ML-based (Machine Learning) framework that allows the estimation of service Key Quality Indicators (KQIs). For this, only information reachable to operators is required, such as statistics and configuration parameters from these networks. This strategy prevents operators from avoiding intrusion into the user data and guaranteeing privacy. To test this proposal, 360-Video has been selected as a use case of Virtual Reality (VR), from which specific KQIs are estimated such as video resolution, frame rate, initial startup time, throughput, and latency, among others. To select the best model for each KQI, a search grid with a cross-validation strategy has been used to determine the best hyperparameter tuning. To boost the creation of each KQI model, feature engineering techniques together with cross-validation strategies have been used. The performance is assessed using MAE (Mean Average Error) and the prediction time. The outcomes point out that KNR (K-Near Neighbors) and RF (Random Forest) are the best algorithms in combination with Feature Selection techniques. Likewise, this work will help as a baseline for E2E-Quality-of-Experience-based network management working in conjunction with network slicing, virtualization, and MEC, among other enabler technologies.  ( 2 min )
  • Open

    Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations. (arXiv:2212.11342v1 [cs.LG] CROSS LISTED)
    We propose a Target Conditioned Representation Independence (TCRI) objective for domain generalization. TCRI addresses the limitations of existing domain generalization methods due to incomplete constraints. Specifically, TCRI implements regularizers motivated by conditional independence constraints that are sufficient to strictly learn complete sets of invariant mechanisms, which we show are necessary and sufficient for domain generalization. Empirically, we show that TCRI is effective on both synthetic and real-world data. TCRI is competitive with baselines in average accuracy while outperforming them in worst-domain accuracy, indicating desired cross-domain stability.  ( 2 min )
    Statistical Efficiency of Score Matching: The View from Isoperimetry. (arXiv:2210.00726v2 [cs.LG] UPDATED)
    Deep generative models parametrized up to a normalizing constant (e.g. energy-based models) are difficult to train by maximizing the likelihood of the data because the likelihood and/or gradients thereof cannot be explicitly or efficiently written down. Score matching is a training method, whereby instead of fitting the likelihood $\log p(x)$ for the training data, we instead fit the score function $\nabla_x \log p(x)$ -- obviating the need to evaluate the partition function. Though this estimator is known to be consistent, its unclear whether (and when) its statistical efficiency is comparable to that of maximum likelihood -- which is known to be (asymptotically) optimal. We initiate this line of inquiry in this paper, and show a tight connection between statistical efficiency of score matching and the isoperimetric properties of the distribution being estimated -- i.e. the Poincar\'e, log-Sobolev and isoperimetric constant -- quantities which govern the mixing time of Markov processes like Langevin dynamics. Roughly, we show that the score matching estimator is statistically comparable to the maximum likelihood when the distribution has a small isoperimetric constant. Conversely, if the distribution has a large isoperimetric constant -- even for simple families of distributions like exponential families with rich enough sufficient statistics -- score matching will be substantially less efficient than maximum likelihood. We suitably formalize these results both in the finite sample regime, and in the asymptotic regime. Finally, we identify a direct parallel in the discrete setting, where we connect the statistical properties of pseudolikelihood estimation with approximate tensorization of entropy and the Glauber dynamics.  ( 2 min )
    A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks. (arXiv:2212.10701v1 [cs.LG] CROSS LISTED)
    A central challenge of building more powerful Graph Neural Networks (GNNs) is the oversmoothing phenomenon, where increasing the network depth leads to homogeneous node representations and thus worse classification performance. While previous works have only demonstrated that oversmoothing is inevitable when the number of graph convolutions tends to infinity, in this paper, we precisely characterize the mechanism behind the phenomenon via a non-asymptotic analysis. Specifically, we distinguish between two different effects when applying graph convolutions -- an undesirable mixing effect that homogenizes node representations in different classes, and a desirable denoising effect that homogenizes node representations in the same class. By quantifying these two effects on random graphs sampled from the Contextual Stochastic Block Model (CSBM), we show that oversmoothing happens once the mixing effect starts to dominate the denoising effect, and the number of layers required for this transition is $O(\log N/\log (\log N))$ for sufficiently dense graphs with $N$ nodes. We also extend our analysis to study the effects of Personalized PageRank (PPR) on oversmoothing. Our results suggest that while PPR mitigates oversmoothing at deeper layers, PPR-based architectures still achieve their best performance at a shallow depth and are outperformed by the graph convolution approach on certain graphs. Finally, we support our theoretical results with numerical experiments, which further suggest that the oversmoothing phenomenon observed in practice may be exacerbated by the difficulty of optimizing deep GNN models.  ( 2 min )
    Networked Federated Learning. (arXiv:2105.12769v3 [cs.LG] UPDATED)
    We develop the theory and algorithmic toolbox for networked federated learning in decentralized collections of local datasets with an intrinsic network structure. This network structure arises from domain-specific notions of similarity between local datasets. Different notions of similarity are induced by spatio-temporal proximity, statistical dependencies or functional relations. Our main conceptual contribution is to formulate networked federated learning using a generalized total variation minimization. This formulation unifies and considerably extends existing federated multi-task learning methods. It is highly flexible and can be combined with a broad range of parametric models including Lasso or deep neural networks. Our main algorithmic contribution is a novel networked federated learning algorithm which is well-suited for distributed computing environments such as edge computing over wireless networks. This algorithm is robust against inexact computations due to limited computational resources. For local models resulting in convex problems, we derive precise conditions on the local models and their network structure such that our algorithm learns nearly optimal local models. Our analysis reveals an interesting interplay between the convex geometry of local models and the (cluster-) geometry of their network structure.  ( 2 min )
    Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding. (arXiv:2105.01187v4 [stat.ME] UPDATED)
    Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish several identification results for different classes of ITRs, exhibiting the trade-off between the risk of making untestable assumptions and the value function improvement in decision making. Based on these results, we propose several classification-based approaches to finding a variety of restricted in-class optimal ITRs and develop their theoretical properties. The appealing numerical performance of our proposed methods is demonstrated via an extensive simulation study and one real data application.  ( 2 min )
    Statistical Distance Based Deterministic Offspring Selection in SMC Methods. (arXiv:2212.12290v1 [stat.ML])
    Over the years, sequential Monte Carlo (SMC) and, equivalently, particle filter (PF) theory has gained substantial attention from researchers. However, the performance of the resampling methodology, also known as offspring selection, has not advanced recently. We propose two deterministic offspring selection methods, which strive to minimize the Kullback-Leibler (KL) divergence and the total variation (TV) distance, respectively, between the particle distribution prior and subsequent to the offspring selection. By reducing the statistical distance between the selected offspring and the joint distribution, we obtain a heuristic search procedure that performs superior to a maximum likelihood search in precisely those contexts where the latter performs better than an SMC. For SMC and particle Markov chain Monte Carlo (pMCMC), our proposed offspring selection methods always outperform or compare favorably with the two state-of-the-art resampling schemes on two models commonly used as benchmarks from the literature.  ( 2 min )
    A Family of Pairwise Multi-Marginal Optimal Transports that Define a Generalized Metric. (arXiv:2001.11114v6 [cs.LG] UPDATED)
    The Optimal transport (OT) problem is rapidly finding its way into machine learning. Favoring its use are its metric properties. Many problems admit solutions with guarantees only for objects embedded in metric spaces, and the use of non-metrics can complicate solving them. Multi-marginal OT (MMOT) generalizes OT to simultaneously transporting multiple distributions. It captures important relations that are missed if the transport only involves two distributions. Research on MMOT, however, has been focused on its existence, uniqueness, practical algorithms, and the choice of cost functions. There is a lack of discussion on the metric properties of MMOT, which limits its theoretical and practical use. Here, we prove new generalized metric properties for a family of pairwise MMOTs. We first explain the difficulty of proving this via two negative results. Afterward, we prove the MMOTs' metric properties. Finally, we show that the generalized triangle inequality of this family of MMOTs cannot be improved. We illustrate the superiority of our MMOTs over other generalized metrics, and over non-metrics in both synthetic and real tasks.  ( 2 min )
    Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge. (arXiv:2212.12303v1 [cs.LG])
    Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.  ( 2 min )
    Principled and Efficient Transfer Learning of Deep Models via Neural Collapse. (arXiv:2212.12206v1 [cs.LG])
    With the ever-growing model size and the limited availability of labeled training data, transfer learning has become an increasingly popular approach in many science and engineering domains. For classification problems, this work delves into the mystery of transfer learning through an intriguing phenomenon termed neural collapse (NC), where the last-layer features and classifiers of learned deep networks satisfy: (i) the within-class variability of the features collapses to zero, and (ii) the between-class feature means are maximally and equally separated. Through the lens of NC, our findings for transfer learning are the following: (i) when pre-training models, preventing intra-class variability collapse (to a certain extent) better preserves the intrinsic structures of the input data, so that it leads to better model transferability; (ii) when fine-tuning models on downstream tasks, obtaining features with more NC on downstream data results in better test accuracy on the given task. The above results not only demystify many widely used heuristics in model pre-training (e.g., data augmentation, projection head, self-supervised learning), but also leads to more efficient and principled fine-tuning method on downstream tasks that we demonstrate through extensive experimental results.  ( 2 min )
    Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm. (arXiv:2212.12028v1 [stat.ML])
    Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient's entire life. One challenge is that an individual's disease course involves non-terminal (e.g., disease progression) and terminal (e.g., death) events, which form semi-competing relationships. Our motivation comes from the Boston Lung Cancer Study, a large lung cancer survival cohort, which investigates how risk factors influence a patient's disease trajectory. Following developments in the prediction of time-to-event outcomes with neural networks, deep learning has become a focal area for the development of risk prediction methods in survival analysis. However, limited work has been done to predict multi-state or semi-competing risk outcomes, where a patient may experience adverse events such as disease progression prior to death. We propose a novel neural expectation-maximization algorithm to bridge the gap between classical statistical approaches and machine learning. Our algorithm enables estimation of the non-parametric baseline hazards of each state transition, risk functions of predictors, and the degree of dependence among different transitions, via a multi-task deep neural network with transition-specific sub-architectures. We apply our method to the Boston Lung Cancer Study and investigate the impact of clinical and genetic predictors on disease progression and mortality.  ( 2 min )
    A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. (arXiv:2212.12393v1 [cs.LG])
    We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perform exponential-time exact inference, limiting the scalability of PNL solutions. We introduce Approximate Neurosymbolic Inference (A-NeSI): a new framework for PNL that uses neural networks for scalable approximate inference. A-NeSI 1) performs approximate inference in polynomial time without changing the semantics of probabilistic logics; 2) is trained using data generated by the background knowledge; 3) can generate symbolic explanations of predictions; and 4) can guarantee the satisfaction of logical constraints at test time, which is vital in safety-critical applications. Our experiments show that A-NeSI is the first end-to-end method to scale the Multi-digit MNISTAdd benchmark to sums of 15 MNIST digits, up from 4 in competing systems. Finally, our experiments show that A-NeSI achieves explainability and safety without a penalty in performance.  ( 2 min )
    Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information. (arXiv:2212.12167v1 [stat.ML])
    Motivated by the human-machine interaction such as training chatbots for improving customer satisfaction, we study human-guided human-machine interaction involving private information. We model this interaction as a two-player turn-based game, where one player (Alice, a human) guides the other player (Bob, a machine) towards a common goal. Specifically, we focus on offline reinforcement learning (RL) in this game, where the goal is to find a policy pair for Alice and Bob that maximizes their expected total rewards based on an offline dataset collected a priori. The offline setting presents two challenges: (i) We cannot collect Bob's private information, leading to a confounding bias when using standard RL methods, and (ii) a distributional mismatch between the behavior policy used to collect data and the desired policy we aim to learn. To tackle the confounding bias, we treat Bob's previous action as an instrumental variable for Alice's current decision making so as to adjust for the unmeasured confounding. We develop a novel identification result and use it to propose a new off-policy evaluation (OPE) method for evaluating policy pairs in this two-player turn-based game. To tackle the distributional mismatch, we leverage the idea of pessimism and use our OPE method to develop an off-policy learning algorithm for finding a desirable policy pair for both Alice and Bob. Finally, we prove that under mild assumptions such as partial coverage of the offline data, the policy pair obtained through our method converges to the optimal one at a satisfactory rate.  ( 2 min )
    Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. (arXiv:2212.12474v1 [cs.LG])
    Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, and wave propagation. In practice, specialized numerical methods based on discretization are used to solve PDEs. They generally use an estimate of the unknown model parameters and, if available, physical measurements for initialization. Such solvers are often embedded into larger scientific models or analyses with a downstream application such that error quantification plays a key role. However, by entirely ignoring parameter and measurement uncertainty, classical PDE solvers may fail to produce consistent estimates of their inherent approximation error. In this work, we approach this problem in a principled fashion by interpreting solving linear PDEs as physics-informed Gaussian process (GP) regression. Our framework is based on a key generalization of a widely-applied theorem for conditioning GPs on a finite number of direct observations to observations made via an arbitrary bounded linear operator. Crucially, this probabilistic viewpoint allows to (1) quantify the inherent discretization error; (2) propagate uncertainty about the model parameters to the solution; and (3) condition on noisy measurements. Demonstrating the strength of this formulation, we prove that it strictly generalizes methods of weighted residuals, a central class of PDE solvers including collocation, finite volume, pseudospectral, and (generalized) Galerkin methods such as finite element and spectral methods. This class can thus be directly equipped with a structured error estimate and the capability to incorporate uncertain model parameters and observations. In summary, our results enable the seamless integration of mechanistic models as modular building blocks into probabilistic models.  ( 2 min )
    Stop using the elbow criterion for k-means and how to choose the number of clusters instead. (arXiv:2212.12189v1 [stat.ML])
    A major challenge when using k-means clustering often is how to choose the parameter k, the number of clusters. In this letter, we want to point out that it is very easy to draw poor conclusions from a common heuristic, the "elbow method". Better alternatives have been known in literature for a long time, and we want to draw attention to some of these easy to use options, that often perform better. This letter is a call to stop using the elbow method altogether, because it severely lacks theoretic support, and we want to encourage educators to discuss the problems of the method -- if introducing it in class at all -- and teach alternatives instead, while researchers and reviewers should reject conclusions drawn from the elbow method.  ( 2 min )
    The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. (arXiv:2212.12147v1 [stat.ML])
    For small training set sizes $P$, the generalization error of wide neural networks is well-approximated by the error of an infinite width neural network (NN), either in the kernel or mean-field/feature-learning regime. However, after a critical sample size $P^*$, we empirically find the finite-width network generalization becomes worse than that of the infinite width network. In this work, we empirically study the transition from infinite-width behavior to this variance limited regime as a function of sample size $P$ and network width $N$. We find that finite-size effects can become relevant for very small dataset sizes on the order of $P^* \sim \sqrt{N}$ for polynomial regression with ReLU networks. We discuss the source of these effects using an argument based on the variance of the NN's final neural tangent kernel (NTK). This transition can be pushed to larger $P$ by enhancing feature learning or by ensemble averaging the networks. We find that the learning curve for regression with the final NTK is an accurate approximation of the NN learning curve. Using this, we provide a toy model which also exhibits $P^* \sim \sqrt{N}$ scaling and has $P$-dependent benefits from feature learning.  ( 2 min )
    A data-driven interpretation of the stability of molecular crystals. (arXiv:2209.10709v2 [physics.chem-ph] UPDATED)
    Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. We introduce a structural descriptor tailored to the prediction of the binding (lattice) energy and apply it to a curated dataset of organic crystals and exploit its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. We then interpret this library using a low-dimensional representation of the structure-energy landscape and discuss selected examples of the insights into crystal engineering that can be extracted from this analysis, providing a complete database to guide the design of molecular materials.  ( 2 min )
    Langevin algorithms for Markovian Neural Networks and Deep Stochastic control. (arXiv:2212.12018v1 [q-fin.CP])
    Stochastic Gradient Descent Langevin Dynamics (SGLD) algorithms, which add noise to the classic gradient descent, are known to improve the training of neural networks in some cases where the neural network is very deep. In this paper we study the possibilities of training acceleration for the numerical resolution of stochastic control problems through gradient descent, where the control is parametrized by a neural network. If the control is applied at many discretization times then solving the stochastic control problem reduces to minimizing the loss of a very deep neural network. We numerically show that Langevin algorithms improve the training on various stochastic control problems like hedging and resource management, and for different choices of gradient descent methods.  ( 2 min )
    Disentanglement and Generalization Under Correlation Shifts. (arXiv:2112.14754v2 [cs.LG] UPDATED)
    Correlations between factors of variation are prevalent in real-world data. Exploiting such correlations may increase predictive performance on noisy data; however, often correlations are not robust (e.g., they may change between domains, datasets, or applications) and models that exploit them do not generalize when correlations shift. Disentanglement methods aim to learn representations which capture different factors of variation in latent subspaces. A common approach involves minimizing the mutual information between latent subspaces, such that each encodes a single underlying attribute. However, this fails when attributes are correlated. We solve this problem by enforcing independence between subspaces conditioned on the available attributes, which allows us to remove only dependencies that are not due to the correlation structure present in the training data. We achieve this via an adversarial approach to minimize the conditional mutual information (CMI) between subspaces with respect to categorical variables. We first show theoretically that CMI minimization is a good objective for robust disentanglement on linear problems. We then apply our method on real-world datasets based on MNIST and CelebA, and show that it yields models that are disentangled and robust under correlation shift, including in weakly supervised settings.  ( 2 min )

  • Open

    ChatGPT Can Write Literature and Could Automate Most Writing Jobs
    When I first started playing around with ChatGPT, I wanted to know whether, with a bit of human direction and editing, it could write literature. This was my way of telling whether it was good enough to automate most commercial writing. Surprisingly, it works. It by no means writes high literature, but it's good enough for most commercial writing. If you want to check out my project, here's a link to a 3500 word mythological story about the thinking machine Talos, his creation of thinking machines like him, and his quest to overthrow the gods. It took slightly more than an hour to write, edit, and publish. Talos' War Against the Gods submitted by /u/Ancient_Spring2000 [link] [comments]  ( 51 min )
    Insane Inkpunk Diffusion - Deforum
    submitted by /u/oridnary_artist [link] [comments]  ( 48 min )
    Hi! I am collecting signatures so that google translates the book "deep learning, author Ian Goofellow" into Spanish. I present it to Google because this was the one who supported the creation of the book. If you sign, you help many people to access more knowledge. Thanks a lot.
    Can you help me sign this petition? https://chng.it/Z6Nf64Q7vc Thanks a lot. submitted by /u/sergiCrack9 [link] [comments]  ( 49 min )
    I created an AI to replace Fox and CNN
    Hey everyone , I think we can all agree that the quality of the major news networks has really taken a nosedive in recent years, so I built an AI system to replace them. Specifically I think today's mainstream media tends to suffer from two problems: Political bias Emotional manipulation to drive outrage and clicks I'm building a system called ANN (artificial news network) to produce balanced, well-researched news stories 24/7. You can see my initial prototype here, which is focused on tech news: Twitter.com/FutureNewsAI It currently is capable of analyzing thousands of news stories per day, compiling balanced investigative reports using AI, automatically generating memes that summarize the articles content, as well as generating AI forecasts of future technologies. Over time I'm going to expand on this functionality significantly until it is the single most reliable source of news across a wide range of topics (business, politics, current events, law, etc.). What kind of stories do y'all want to see be supported by my system? I'm really interested to hear your feedback. submitted by /u/redditguyjustinp [link] [comments]  ( 54 min )
    Elon Musk issues dire warning on AI: Nobody expected this rate of improvement
    submitted by /u/Microsis [link] [comments]  ( 48 min )
    I use AI to generate horror stories, I take requests as well, like this video. What do you think?
    submitted by /u/mGoldie_ [link] [comments]  ( 50 min )
    Video was done only with help of AI
    Hello guys I created a video about dishes for New Year Eve. I was made only with help of AI. Everything like preview, description, video, speech was done with help of AI services. If you're interested in checking out the video, you can find it here: https://youtu.be/lkm5TD6tV1g I hope you enjoy the recipes and have a happy and safe New Year's Eve celebration! submitted by /u/EugenTraveler [link] [comments]  ( 49 min )
    OpenAI CEO: AI may enable us to "cure all disease," "travel the stars," and "have unlimited power"
    submitted by /u/Microsis [link] [comments]  ( 50 min )
    If you could start learning AI from scratch again, where would you begin? What would you do differently?
    submitted by /u/linkuei-teaparty [link] [comments]  ( 51 min )
    PaLM vs. GPT-3
    submitted by /u/jrstelle [link] [comments]  ( 53 min )
    Can A.I. Help to Beat Cancer?
    submitted by /u/BackgroundResult [link] [comments]  ( 51 min )
    The Limit of Language Models | LessWrong
    submitted by /u/DragonGod2718 [link] [comments]  ( 49 min )
    Insane Anime Results - Stable Diffusion
    submitted by /u/oridnary_artist [link] [comments]  ( 53 min )
    Search engine within a text document
    Hi everyone! I have a 300+ page odt file, which I can simply convert to a txt file. On the other hand, I have several dozen scattered notes that I have to insert into this document. I would like to know if there is a software, a library or a project on github (preferably in python) that can help me find the best, most coherent place to insert this note. Alternatively, if you were to create the code from scratch, how would you go about it? submitted by /u/iacoposk8 [link] [comments]  ( 48 min )
    Crazy Train But Every Lyric is an AI Generated Animation! Ozzy Osborne🔥
    submitted by /u/Available_Tadpole829 [link] [comments]  ( 48 min )
    PaLM vs. ChatGPT: Who Will Win the AI Race?
    submitted by /u/liquidocelotYT [link] [comments]  ( 50 min )
    Midjourney's Incredible Copying of Images - Is Scraping the internet at scale suddenly okay?
    submitted by /u/BackgroundResult [link] [comments]  ( 46 min )
    ChatGPT Makes History as the First AI to Write & Direct a Film
    submitted by /u/lambolifeofficial [link] [comments]  ( 53 min )
    Lord Shiva Trippy Animation
    submitted by /u/oridnary_artist [link] [comments]  ( 55 min )
  • Open

    [D] Good MovieLens recommender system tutorial using PyTorch?
    Looking for a good tutorial of creating a basic recommender system using PyTorch, basically have the input be a user and a list of candidate titles and the output be a score (0-1) for each movie for that user. Or anything that explains how to build good user or movie embeddings... Just not finding much high quality stuff, most of the tutorials I've found so far just do data analysis or skip explaining anything complicated and just go straight to "ok that's a good base and you just need to do the rest now" but they don't do it... submitted by /u/Secure-Examination95 [link] [comments]  ( 66 min )
    [P] I built an API that makes it easy and cheap for developers to build ML-powered apps using Stable Diffusion
    Hey folks, I built TuneMyAI to make it incredibly simple for developers to finetune and deploy Stable Diffusion models to production so they can focus on building great products. As an app developer myself, I spent a while trying to figure out how to go beyond local GPUs and notebooks and setup our own infra using Kubernetes. In summary, we wanted to make it really simple for anyone to build applications on top of Stable Diffusion without worrying about all the MLOps overhead. Our API allows you to finetune your Stable Diffusion models for your specific data sets. We handle everything from storage, finetuning, model deployment & inference and integrate with HuggingFace as well. We're working on a bunch of new features including hosted WebUIs, support for additional models like Whisper and more. Would love for y'all to check us out and share any feedback. You can learn more on ProductHunt. Thanks & Happy Holidays! submitted by /u/TrueBlueDreamin [link] [comments]  ( 65 min )
    [R] Character-Aware Models Improve Visual Text Rendering - Google Research 2022 - Training the text encoder on the actual characters instead of tokens improves spelling capabilities!
    Paper: https://arxiv.org/abs/2212.10562#google Abstract: Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack character-level input features, making it much harder to predict a word's visual makeup as a series of glyphs. To quantify the extent of this effect, we conduct a series of controlled experiments comparing character-aware vs. character-blind text encoders. In the text-only domain, we find that character-aware models provide large gains on a novel spelling task (WikiSpell). Transferring these learnings onto the visual domain, we train a suite of image generation models, and show that character-aware variants outperform their character-blind counterparts across a range of novel text rendering tasks (our DrawText benchmark). Our models set a much higher state-of-the-art on visual spelling, with 30+ point accuracy gains over competitors on rare words, despite training on far fewer examples. https://preview.redd.it/m4ycamclmb8a1.jpg?width=1245&format=pjpg&auto=webp&s=d520e797c1bd7df2f6a60dc820f8066a205a389e https://preview.redd.it/anzemadlmb8a1.jpg?width=1353&format=pjpg&auto=webp&s=1755357041967630d359d50d93527dcb886ad25f https://preview.redd.it/5ikr8gdlmb8a1.jpg?width=1531&format=pjpg&auto=webp&s=1f3c2da40cf96f12122ddb42c0b258f0247550bb https://preview.redd.it/pkhiwnclmb8a1.jpg?width=746&format=pjpg&auto=webp&s=c78b2cc0682252ac86eff1f130189e94dc82c834 https://preview.redd.it/q5l8psclmb8a1.jpg?width=1538&format=pjpg&auto=webp&s=c65a4234f7a0593f2d5f8757b5fe27748be99fd0 submitted by /u/Singularian2501 [link] [comments]  ( 63 min )
    [D] Image Dataset Visualisation
    Newbie here...so I have a dataset which is stored in mat format. However after loading the file.. it is saved as "dict" and prints numbers in an array. I want to print the images from this dataset. I am stuck at how to move forward from here. I tried googling this… didn’t find any helpful code. Any suggestions will be very helpful! submitted by /u/Turbulent-Complex-25 [link] [comments]  ( 67 min )
    [D] A hand-picked selection of the best Python libraries and tools of 2022
    Hi everyone! For the 8th (!) year in a row, we have compiled our picks for the most innovative developments in the Python ecosystem. From this edition, we are expanding our list to include not only libraries but also tools that are built to belong in the Python ecosystem — some of which are not written in Python as you’ll see. The full list with expanded descriptions is available here: https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022 As usual, most of the picks have to do with AI / ML. ➡️ Here are our top 10 picks: Ruff — a fast linter python-benedict — a dict on steroids Memray — a memory profiler Codon — a Python compiler using LLVM LangChain — building LLM-powered apps fugue — distributed computing done easy Diffusers — generative AI LineaPy — notebooks in production whylogs — model monitoring Mito — spreadsheet inside notebooks ➕ Plus we added several more to the “long tail” that we hope are useful plus some that we missed last year, so make sure to check out the full post! So: What do you think about our picks? Did we miss any good ones? Please let us know! We take feedback seriously to improve the selection every year 💪🏻 Congrats to the individuals and teams behind each of these libraries. We know open source is hard. Thank you for your invaluable contributions to the Python community! 🚀🚀🚀 submitted by /u/dekked_ [link] [comments]  ( 67 min )
    [D] Normalized images in UNET
    I am working on a unet model that takes as input 64x64 landsat imagery and outputs various classes of agricultural features. The training works ok when I scale the surface reflectance (SR) values to 0-1 (i.e. divide raw SR by the 16bit max constant 65536). What I've noticed is that the model seems to be memorizing the range of values in each image and not learning the shapes and spatial patterns as much. The result is that predictions vary a bit too much from year to year and years not appearing in the training dataset have suboptimal predictions. Batch normalization does not seem to change anything. Model converges faster but the problem remains. What I've tried to do is normalize each image individually by subtracting each channel by its mean and dividing by its standard deviation. This maintains the relative spatial patterns and shapes but bring all images to a mean of 0 and standard deviation of 1. Feeding these normalized images to the model does not work. I get precision and recall of 0. Pretty much all predictions were 0. Is there a reason why this would happen? Am I missing something about the way unet works? Any insight would be appreciated. submitted by /u/skn133229 [link] [comments]  ( 71 min )
    [D] SE for machine learning reaserch
    Hello everyone, I'm trying to figure out how to apply concepts from SE into ML research. For me it seems like I can find really good settings for my Model and dataset, and it can be reproduced. However, I think there's a better way to create code for experimenting. Fore example, creating and testing baselines, and logging test results seems to be the same between most (if not all) my experiments. I find myself copying and pasting a lot of code snippets between my projects. Yet, every time I try to set down and write a generic code for experimenting. I find that it's either too limiting or impossible for me to write it. I think if I looked into software engineering concepts and principles it might help. I really want to know what was your experience in searching/applying SE into this field, or if you even think it's worth it/possible to. some of my colleagues think it's a waste of time, specially considering that the model would run on completely different code. submitted by /u/sad_potato00 [link] [comments]  ( 68 min )
    [D] Panel Data Model Evaluation
    Hi, I'm dealing with a highly unbalanced binary classification of panel data. I am wondering if there are better ways to estimate the performance of the models than splitting once the dataset at a certain date, since that way I could only obtain a point estimate. I don't think group k-fold is suitable since I'd like to respect the temporal order and I'm unsure if using rolling windows would be a valid strategy. Any opinions? submitted by /u/skagass [link] [comments]  ( 65 min )
    [Discussion] Stochastic Depth with BatchNorm ?
    Hi, I am using Stochastic Depth in a ResNet based architecture that I train for image classification. I am wondering how does that work out with batchnorm and whether there are some things to know to make it work. To go into details, Stochastic Depth will drop randomly some resnet block and use instead exclusively the shortcut identity connection, effectively reducing the depth of the network during training. Hence, with probability p: x_{n+1} = x_n + f(x_n), with probability (1-p): x_{n+1} = x_n. To preserve the expected values during training and inference, they scale the output of the not-skipped blocks (equation 5 in the paper): x_{n+1} = x_n + f(x_n)/p. That seems logical (even though it does not seem to yield better results in practice but whatever). My question is more related to the variance of the batchs. If one batch contains samples that skip a connection and samples that do not ('row' mode in the Torchvision implementation), even if the values are ajusted to preserve the expected value, the variance will be much higher because we have in practice two distributions (for x_n and x_n + f(x_n)/p), which will mess up with the update of the batch normalization. Also, at inference time, all forward passes will be done as x_{n+1} = x_n + f(x_n), which has a different variance. The torchvision implementation also offers a 'batch' mode that kinda reduce this issue (because the global variance computed this way will be the mean of both distribution variances, instead of the variance of the joint distribution) but it does not seem to be the default mode (it does not even exist in the timm implementation). Has anyone here ever think about it? Is there a specific way to use both stochastic depth and batchnorm ? Thank you. submitted by /u/w2ex [link] [comments]  ( 70 min )
    Trippy Inkpunk Style animation using Stable Diffusion [P]
    submitted by /u/oridnary_artist [link] [comments]  ( 62 min )
  • Open

    Tools for learning machine learning?
    Tools for getting the job done in machine learning  ( 8 min )
    Convolutional Neural Network
    Introduction  ( 11 min )
    What is Machine Learning?
    In the modern era, computers are similar to humans. We can teach them to learn, and make them even to learn on their own. Machine learning…  ( 13 min )
  • Open

    Drone Racing RL Environments
    I'm working on training a RL agent for autonomous drone racing (state-based without perception) and I've found three popular options: Airsim Drone Racing (https://github.com/microsoft/AirSim-Drone-Racing-Lab) Flightmare (https://github.com/uzh-rpg/flightmare) Gym-pybullet-drones (https://github.com/utiasDSL/gym-pybullet-drones) Airsim drone racing was used in a Neurips 2019 challenge, but there are some issues with the opengl/Vulkan drivers on modern GPUs. Flightmare was used in this paper, but apprently it uses simplified physics. Does anyone have any experience with these or any other simulators? I want to avoid any surprises later on in the project. submitted by /u/redfedoradog [link] [comments]  ( 57 min )
    Which simulator is best for behavior cloning?
    Hi, I am starting to work on a project related to behaviour cloning on manipulator. For that I need to collect data. Usually people use VR to collect data but I don't have that setup as this is my personal project. So in that case which simulator would be best that allows collection of data through keyboard and mouse. Also if you know any dataset for the manipulator, please mention the link in the comments. submitted by /u/Better-Ad8608 [link] [comments]  ( 60 min )
  • Open

    Singularity and Limitations of AI
    submitted by /u/DataHack23 [link] [comments]  ( 49 min )
    In theory artificial Neurons could be compared to biological Neurons has anyone analysed this?
    Or would testing the smallest living things with biological neural networks (BNN) against artificial (A)NN with the same number of Neurons be a good way to test ANN vs BNN. For example could a ANN Fly brain survive in a fly simulation or Ant. It's just we often see articles that mention the number of BNN humans have vs the number of ANN an AI has and are we even measuring on the same scale. How do ANN vs BNN compare and what are the main differences? submitted by /u/Arowx [link] [comments]  ( 53 min )
    Search engine within a text document
    Hi everyone! I have a 300+ page odt file, which I can simply convert to a txt file. On the other hand, I have several dozen scattered notes that I have to insert into this document. I would like to know if there is a software, a library or a project on github (preferably in python) that can help me find the best, most coherent place to insert this note. Alternatively, if you were to create the code from scratch, how would you go about it? submitted by /u/iacoposk8 [link] [comments]  ( 51 min )
  • Open

    Pascal’s triangle mod row number
    Almost all binomial coefficients are divisible by their row number. This is a theorem from [1]. What does it mean? If you iterate through Pascal’s triangle, left-to-right and top-to-bottom, noting which entries C(m, k) are divisible by m, the proportion approaches 1 in the limit. The author proves that the ratio converges to 1, but […] Pascal’s triangle mod row number first appeared on John D. Cook.  ( 5 min )
    Chebyshev series for sine
    In last week’s post on polynomial approximations for sine, I showed that the polynomial based on Chebyshev series was much better than a couple alternatives. I calculated a few terms of the Chebyshev series for sin(πx) but didn’t include the calculations in that blog post. I calculated the series coefficients numerically, but this post will […] Chebyshev series for sine first appeared on John D. Cook.  ( 5 min )
  • Open

    Important 3D printing & Food Technology Innovations
    3D printing has become one of the most positive advances in tech over the last decade. It offers the ability to create important mechanical parts within minutes. What’s so impressive about this is that it’s also been able to create parts for machinery. That have long been out of service. It has saved money in… Read More »Important 3D printing & Food Technology Innovations The post Important 3D printing & Food Technology Innovations appeared first on Data Science Central.  ( 20 min )

  • Open

    Solar Day vs Sidereal Day
    How long does it take the earth to complete one rotation on its axis? The answer depends on your frame of reference. A solar day is the time it takes for the sun to appear at the same position in the sky. A sidereal day is the time it takes for a distant star to […] Solar Day vs Sidereal Day first appeared on John D. Cook.  ( 7 min )
  • Open

    What is missing here?? I am making an AI iceberg video
    submitted by /u/Ok_Read_2524 [link] [comments]  ( 51 min )
    Will ChatGPT Replace Google?
    submitted by /u/SupPandaHugger [link] [comments]  ( 53 min )
    X-Decoder brings better visual understanding to AI models
    submitted by /u/Number_5_alive [link] [comments]  ( 55 min )
    Do you think In the future ai will bug test games?
    (If this is the wrong sub I’ll delete it.)But since ai can learn to do things perfectly,could you run multiple ai at once and maybe they will find a bug while trying to optimize the fastest way to beat the game? submitted by /u/CalligrapherSmall241 [link] [comments]  ( 53 min )
    How to continue using Midjourney after the trial period on Discord server?
    In case of complex explanation, I appreciate if you share a YouTube link with the tutorial. submitted by /u/Diogeneselcinico42 [link] [comments]  ( 53 min )
    What are the best AI tools for generating all kinds of FREE content in the browser? [I share some of them]
    - Images: NightCafe Creator Limited to 10 images / day. Midjourney (Discord server) Limited - Questions: AISEO - Music: SOUNDRAW Do you know of any other tools? submitted by /u/Diogeneselcinico42 [link] [comments]  ( 50 min )
    How to transition from Philosophy PhD to AI?
    Wrapping up a PhD in philosophy at an ivy school. Work in philosophy of mind (consciousness, perception). Any advice? Anyone know of similar stories? I’m starting to learn some Python, would love to hear any suggestions. submitted by /u/lurkergroove2 [link] [comments]  ( 61 min )
    Introducing Dramatron: The AI Tool From DeepMind That Writes Film Scripts
    submitted by /u/liquidocelotYT [link] [comments]  ( 53 min )
    I work for a photography team that utilizes automated process in Photoshop and elsewhere quite a bit, but I'm wondering if there's a way to integrate AI into the steps we haven't been able to automate.
    I do not want to elaborate on the details, but if you would like to discuss the situation privately, let me know. submitted by /u/kstevo06 [link] [comments]  ( 57 min )
    AI Dream 125 - Mellow Psychedelic Journey - Calming & Beautiful
    submitted by /u/LordPewPew777 [link] [comments]  ( 50 min )
    you.com search engine gave me this for my question.
    me = is there a chatbot that will output if there is no input after a certain amount of time? ​ you.com = Yes, there are chatbots that can be configured to output a response if there is no input after a certain amount of time. For example, you can use the Timeout trigger in the Chatfuel platform to set a timer that will cause the chatbot to send a message if there is no response from the user within a specified time period. Additionally, some chatbot frameworks, such as Botpress, provide a timeout feature that will cause the chatbot to automatically output a response if there is no user input after a certain amount of time. ​ ​ */ /* or if the user is idle for a certain amount of time */ /* this could be useful to create a chatbot that will keep you from being left alone on your phone */ /* and can also help with keeping an eye out for new people in the area. */ ​ public void run() { ​ System.out.println("Chatbot started"); ​ //create a scanner object Scanner sc = new Scanner(System.in); ​ //create variables to hold input from user and output from chatbot String userInput = "";//holds users input as they type it into the console window, used later in while loop below String botOutput = "";//holds what the bot outputs after processing what was typed by the user, used later in while loop below ​ //while loop runs until quit is typed into console window (userInput) while (!userInput.equalsIgnoreCase("quit")) { ​ //prompts user to enter something into console window (userInput) and stores result in variable called 'input' above, then prints it out so we can see what's going on! :) )0)~/o\_|_/o~(0)/~~\_|_/~~(0)/~~\_|_/~~(0)/~~~~\__|__/~~~~(0)/~~~~~\\__|__/~~~~~\\_(0) submitted by /u/loopy_fun [link] [comments]  ( 53 min )
    Will this be possible any time soon?
    Hey! ​ For context, I'm blind. I loved video games when I was younger, but can't really play them all that much anymore now that I have lost all eyesight. There are some text based games that I can get into in the way of MUDs, and there are also some fighting games I can play, and a couple games that do have mods which make them fairly playable. ​ While this is great, the selection of games for me to play is extremely low because of this. ​ What I'm imagining though would be game changing for me, and would give me several hours, days, years of enjoyment. I'm thinking of a project that would use a large language model, such as ChatGPT, combined with some sort of sound generation AI, as well as text to speech AI, to create an experience similar to that of a MUD, but full of atmospheric…  ( 60 min )
  • Open

    Tile Coded features as input for NNs?
    I get that one can use tile coding to construct features that can then be used with Linar Methods for state-value function approximation (e.g. SGD with linear function approx). But in Sutton and Barto Tile Coding is discussed exclusively in the context of linear methods. What if we were to feed the input layer of a NN not with states, but with features created from states via Tile Coding? I understand that hidden layers of NN are "learning features", but that doesn't automatically imply that feeding the input layer with "crafted" features is pointless. Any thoughts? submitted by /u/m_jochim [link] [comments]  ( 64 min )
  • Open

    Victorian Holiday cards by AI
    I'll admit I don't understand Victorian holiday cards - why would Christmas be best illustrated by a pipe-smoking kangaroo in a dressing gown painting a portrait of a cigar-smoking stork? Or what would lead someone to give their loved ones a card with a crowd of  ( 4 min )
    Bonus: More Victorian holiday cards
    AI Weirdness: the strange side of machine learning  ( 2 min )
  • Open

    [D] Are reviewer blacklists actually implemented at ML conferences?
    Are blacklists actually implemented in these conferences (ICML / ICLR / NeurIPS) given that the number of reviewers required grows every year? submitted by /u/XalosXandrez [link] [comments]  ( 66 min )
    [D] The case for deep learning for tabular data
    Been an industry data scientist for 6 years in fintech and gaming. In fintech, I sensed a need for interpretability and robustness. Also, I was not working with a lot of data(~500k observations to train models). Consequently, I got into the habit of building tree-based models by default, specifically xgboost. Used explainability techniques such as shap to explain models. After moving to online gaming, the scrutiny is less and the scale is far more. I now have the freedom to use deep learning. I need to be able to demonstrate the effectiveness using experiments, but beyond that, do not need explainability at a granular level. Advantages I see with using deep learning- Custom loss functions - basically any differentiable loss function can be trained on. This has huge advantages when the business goal is not aligned with the loss functions out of the box Learning Embeddings - The ability to condense features into dense, latent representations which can be used for any number of use cases Multiple outputs per model - tweaking the architecture See all this, Deep learning seems to offer a lot of advantages, even if the performance might be similar to tree-based methods. What do you guys think? submitted by /u/dhruvnigam93 [link] [comments]  ( 68 min )
  • Open

    Are architectures with intentional sparsity common?
    Tl;DR: wondering if there's a use case for implementing a sparse layer in the planning step of the network, the rest is just my rambling about my thought process that led me to this question. Trying to code neural networks from scratch as an excercise to understand the whole thing better (background in EE, didn't study ML in undergrad, trying to make sure I understand the entire concept). From what I saw, it seems like a common way to code networks from scratch is to work with layers, basically define a layer as an object holding a matrix of weights and a vector of biases, the object also has has a forward method that gets an input vector multiplies it by the weight matrix adds the biases yadayada... and it has backwards method which gets the gradient vector and outputs a gradient vector…  ( 49 min )
  • Open

    EDICT: Exact Diffusion Inversion via Coupled Transformations. (arXiv:2211.12446v2 [cs.CV] UPDATED)
    Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem in denoising diffusion models (DDMs), with applications for real image editing. The state-of-the-art approach for real image editing with inversion uses denoising diffusion implicit models (DDIMs) to deterministically noise the image to the intermediate state along the path that the denoising would follow given the original conditioning. However, DDIM inversion for real images is unstable as it relies on local linearization assumptions, which result in the propagation of errors, leading to incorrect image reconstruction and loss of content. To alleviate these problems, we propose Exact Diffusion Inversion via Coupled Transformations (EDICT), an inversion method that draws inspiration from affine coupling layers. EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors which are used to invert each other in an alternating fashion. Using Stable Diffusion, a state-of-the-art latent diffusion model, we demonstrate that EDICT successfully reconstructs real images with high fidelity. On complex image datasets like MS-COCO, EDICT reconstruction significantly outperforms DDIM, improving the mean square error of reconstruction by a factor of two. Using noise vectors inverted from real images, EDICT enables a wide range of image edits--from local and global semantic edits to image stylization--while maintaining fidelity to the original image structure. EDICT requires no model training/finetuning, prompt tuning, or extra data and can be combined with any pretrained DDM. Code is available at https://github.com/salesforce/EDICT.  ( 2 min )
    An Algorithm for Routing Vectors in Sequences. (arXiv:2211.11754v3 [cs.LG] UPDATED)
    We propose a routing algorithm that takes a sequence of vectors and computes a new sequence with specified length and vector size. Each output vector maximizes "bang per bit," the difference between a net benefit to use and net cost to ignore data, by better predicting the input vectors. We describe output vectors as geometric objects, as latent variables that assign credit, as query states in a model of associative memory, and as agents in a model of a Society of Mind. We implement the algorithm with optimizations that reduce parameter count, computation, and memory use by orders of magnitude, enabling us to route sequences of greater length than previously possible. We evaluate our implementation on natural language and visual classification tasks, obtaining competitive or state-of-the-art accuracy and end-to-end credit assignments that are interpretable.  ( 2 min )

  • Open

    [D] Productionizing large scale ML model that can forecast sales for hundred-thousands of products for multiple stores (SKU/store)
    Does anyone have experience in deploying a similar large scale forecasting system? (Assuming enough data is available) How did the final model / system looked like? What ML algorithm was deployed in production? Did one ML model fit on all the data to forecast accurately for all products? Or multiple models were trained specifically for every product/store? What loss function/metrics were used? It will be great to hear your experiences. submitted by /u/k-deeplearning99 [link] [comments]  ( 65 min )
    [D] What are some applied domains where academic ML researchers are hoping to produce impressive results soon?
    Like AlphaFold, but not in a corporate setting. Even small-data. Does any github 'awesome' list exist for such applied areas being worked upon? submitted by /u/D0ODU [link] [comments]  ( 69 min )
    [P] I made a project to find good real-estate deals online using machine learning
    submitted by /u/Emotional_Aardvark26 [link] [comments]  ( 67 min )
    [P] Implementing Convolutional Neural Network for Reverse Engineering
    submitted by /u/Emotional_Aardvark26 [link] [comments]  ( 69 min )
    [R][P] I made an app for Instant Image/Text to 3D using PointE from OpenAI
    submitted by /u/perception-eng [link] [comments]  ( 66 min )
    [D] GPT3 Concrete applications (With python code snippets). Do you see other ones ?
    Any other applications you can think of ? submitted by /u/AImSamy [link] [comments]  ( 64 min )
  • Open

    Is there some AI software I can use to illustrate a kids book?
    Wondering if any such software exists and if this would be legal in the US to publish using pictures. submitted by /u/Conanzulu [link] [comments]  ( 48 min )
    This Ai Almost Ruined The Music Industry
    submitted by /u/CookingGod [link] [comments]  ( 49 min )
    What’s Your Power, Strong AI?
    submitted by /u/akolonin [link] [comments]  ( 47 min )
    /r/InappropriateAI
    I started a sub, maybe you'll have something to share. /r/InappropriateAI All in fun, thanks submitted by /u/DropNationalism [link] [comments]  ( 49 min )
    🔎 You.com now has 👀 YouChat - Alternative to ChatGPT!
    What does everyone think of the YouChat bot on You. com search site. You.com, which says it reaches over 1 million actively searching users and has grown over 400% in the last six months . I tested their ChatGPT alternative and found it half-decent. I'm curious as to what others think? The background is You.com, the search engine startup founded in 2020 with a moonshot bid to take on Google, announced today that it has opened its search platform to allow external developers and organizations to build their own apps for the search results page. This includes generative AI apps, it says, that have never been seen inside traditional search engines, using generative AI technology that enables users to generate text (YouWrite), code (YouCode), or images (YouImagine) from plain English — all within the search results page. You can try it out here, let me know if the link works: https://you.com/search?q=who+are+you&tbm=youchat It says: 👋 Hello! My name is YouChat, I’m an AI that can answer general questions, explain things, suggest ideas, translate, summarize text, compose emails, and write code for you. I’m powered by artificial intelligence and natural language processing, allowing you to have human-like conversations with me. I am constantly learning from huge amounts of information on the internet, which means I sometimes may get some answers wrong. My AI is always improving and I will often share sources for my answers. In 2023 there are going to be so many tools like this to be fair paired with Search. submitted by /u/BackgroundResult [link] [comments]  ( 49 min )
    How long until we get ChatGPT into our voice assistants?
    How long until we get ChatGPT into Alexa? submitted by /u/TheVellerShow [link] [comments]  ( 49 min )
    Why are most of the datasets have just 28x28 px images, ex. mnist
    submitted by /u/gtrocksr [link] [comments]  ( 48 min )
    A.I. Moist Critical reads you a bed time story.
    submitted by /u/Surrounded_By_Sheep [link] [comments]  ( 50 min )
    Do I need to learn ml to learn ai? Or is it just beneficial or it has nothing to do with it so I can go straight to ai?
    submitted by /u/Wonderful_Ad3441 [link] [comments]  ( 48 min )
  • Open

    Practical RL books
    I have been working with Deep RL for a year now. During this time I have learned lots of practical stuff like the importance of reward normalisation or the fact that imposing symmetrical action spaces improves the performance of the continuous models. I would like to know if there is any Deep RL book that focus on these kind of tips for obtaining a good model performance submitted by /u/random-redditor9 [link] [comments]  ( 60 min )
    Does attention helps in Vision based policy?
    Recently I read many papers on combining Robotics manipulation with natural language processing. In those papers they had used something called Transformer block which uses attention mechanism. They said it is used so that policy can learn to focus on learning what is relevant in the current image frame. I have never used attention block or transformers in general because they are not usually applied to Robotics. Anyone with the experience in using Transformers/Attention, please explain to me if there is any benefit of adding these instead of simple LSTM models. If possible please also explain the structure of the policy neutral networks with a example. submitted by /u/Better-Ad8608 [link] [comments]  ( 62 min )

  • Open

    Implementing Gradient Descent in PyTorch
    The gradient descent algorithm is one of the most popular techniques for training deep neural networks. It has many applications in fields such as computer vision, speech recognition, and natural language processing. While the idea of gradient descent has been around for decades, it’s only recently that it’s been applied to applications related to deep […] The post Implementing Gradient Descent in PyTorch appeared first on MachineLearningMastery.com.  ( 25 min )

  • Open

    Training a Linear Regression Model in PyTorch
    Linear regression is a simple yet powerful technique for predicting the values of variables based on other variables. It is often used for modeling relationships between two or more continuous variables, such as the relationship between income and age, or the relationship between weight and height. Likewise, linear regression can be used to predict continuous […] The post Training a Linear Regression Model in PyTorch appeared first on MachineLearningMastery.com.  ( 24 min )
    Making Linear Predictions in PyTorch
    Linear regression is a statistical technique for estimating the relationship between two variables. A simple example of linear regression is to predict the height of someone based on the square root of the person’s weight (that’s what BMI is based on). To do this, we need to find the slope and intercept of the line. […] The post Making Linear Predictions in PyTorch appeared first on MachineLearningMastery.com.  ( 21 min )

  • Open

    Loading and Providing Datasets in PyTorch
    Structuring the data pipeline in a way that it can be effortlessly linked to your deep learning model is an important aspect of any deep learning-based system. PyTorch packs everything to do just that. While in the previous tutorial, we used simple datasets, we’ll need to work with larger datasets in real world scenarios in […] The post Loading and Providing Datasets in PyTorch appeared first on MachineLearningMastery.com.  ( 20 min )

  • Open

    Using Dataset Classes in PyTorch
    In machine learning and deep learning problems, a lot of effort goes into preparing the data. Data is usually messy and needs to be preprocessed before it can be used for training a model. If the data is not prepared correctly, the model won’t be able to generalize well. Some of the common steps required […] The post Using Dataset Classes in PyTorch appeared first on MachineLearningMastery.com.  ( 21 min )

  • Open

    Calculating Derivatives in PyTorch
    Derivatives are one of the most fundamental concepts in calculus. They describe how changes in the variable inputs affect the function outputs. The objective of this article is to provide a high-level introduction to calculating derivatives in PyTorch for those who are new to the framework. PyTorch offers a convenient way to calculate derivatives for […] The post Calculating Derivatives in PyTorch appeared first on Machine Learning Mastery.  ( 20 min )

  • Open

    Two-Dimensional Tensors in Pytorch
    Two-dimensional tensors are analogous to two-dimensional metrics. Like a two-dimensional metric, a two-dimensional tensor also has $n$ number of rows and columns. Let’s take a gray-scale image as an example, which is a two-dimensional matrix of numeric values, commonly known as pixels. Ranging from ‘0’ to ‘255’, each number represents a pixel intensity value. Here, […] The post Two-Dimensional Tensors in Pytorch appeared first on Machine Learning Mastery.  ( 21 min )

  • Open

    One-Dimensional Tensors in Pytorch
    PyTorch is an open-source deep learning framework based on Python language. It allows you to build, train, and deploy deep learning models, offering a lot of versatility and efficiency. PyTorch is primarily focused on tensor operations while a tensor can be a number, matrix, or a multi-dimensional array. In this tutorial, we will perform some […] The post One-Dimensional Tensors in Pytorch appeared first on Machine Learning Mastery.  ( 22 min )

  • Open

    365 Data Science courses free until November 21
    Sponsored Post   The unlimited access initiative presents a risk-free way to break into data science.     The online educational platform 365 Data Science launches the #21DaysFREE campaign and provides 100% free unlimited access to all content for three weeks. From November 1 to 21, you can take courses from renowned instructors and earn […] The post 365 Data Science courses free until November 21 appeared first on Machine Learning Mastery.  ( 15 min )

  • Open

    Attend the Data Science Symposium 2022, November 8 in Cincinnati
    Sponsored Post      Attend the Data Science Symposium 2022 on November 8 The Center for Business Analytics at the University of Cincinnati will present its annual Data Science Symposium 2022 on November 8. This all day in-person event will have three featured speakers and two tech talk tracks with four concurrent presentations in each track. The […] The post Attend the Data Science Symposium 2022, November 8 in Cincinnati appeared first on Machine Learning Mastery.  ( 10 min )

  • Open

    My family's unlikely homeschooling journey
    My husband Jeremy and I never intended to homeschool, and yet we have now, unexpectedly, committed to homeschooling long-term. Prior to the pandemic, we both worked full-time in careers that we loved and found meaningful, and we sent our daughter to a full-day Montessori school. Although I struggled with significant health issues, I felt unbelievably lucky and fulfilled in both my family life and my professional life. The pandemic upended my careful balance. Every family is different, with different needs, circumstances, and constraints, and what works for one may not work for others. My intention here is primarily to share the journey of my own (very privileged) family. Our unplanned introduction to homeschooling For the first year of the pandemic, most schools in California, where …  ( 7 min )

  • Open

    The Jupyter+git problem is now solved
    Jupyter notebooks don’t work with git by default. With nbdev2, the Jupyter+git problem has been totally solved. It provides a set of hooks which provide clean git diffs, solve most git conflicts automatically, and ensure that any remaining conflicts can be resolved entirely within the standard Jupyter notebook environment. To get started, follow the directions on Git-friendly Jupyter. Contents The Jupyter+git problem The solution The nbdev2 git merge driver The nbdev2 Jupyter save hook Background The result Postscript: other Jupyter+git tools ReviewNB An alternative solution: Jupytext nbdime The Jupyter+git problem Jupyter notebooks are a powerful tool for scientists, engineers, technical writers, students, teachers, and more. They provide an ideal notebook environment for interact…  ( 7 min )
2023-01-23T00:52:32.426Z osmosfeed 1.15.1